- Introduction to Monitoring for Applications
- Monitoring Systems and Techniques
- Methodologies and Tools in Logging
- Observability and Concepts
- Module 5
- Monitoring will tell you if an application is open source or proprietary.
- Monitoring is only performed by disinterested parties, who have no vested interest in an application.
- Monitoring is done by a single person who observes and logs all activities performed by an application.
- Monitoring is the tracking of important data.
Monitoring refers to the continuous tracking of important data to identify trends, potential issues, or ensure everything is functioning as intended.
- Using different types of monitoring offers continuous monitoring and user-specific operating system (OS) options.
- Using different types of monitoring allows developers to try out different tools in order to be familiar with the maximum number of options.
- Using different types of monitoring benefits developers by providing options if one type of monitoring tool is unavailable or too expensive.
- Using different types of monitoring allows you to gain maximum visibility into your application and connected IT systems.
Using a variety of monitoring types provides comprehensive visibility into various aspects of an application and its related IT infrastructure. Different monitoring tools and methods can capture diverse metrics and performance indicators, such as system resource usage, application performance, network traffic, security events, and user interactions
Q3. Which of the following are the four Golden Signals that provide important metrics for measuring the health of your service or systems?
- Server errors, Client errors, Incorrect content, and Identified errors
- Saturation, Errors, Traffic, and Latency
- Saturation, Marination, Sanitation, and Colorization
- Earth, Wind, Water, and Fire
The concept of the Four Golden Signals comes from Google's Site Reliability Engineering (SRE) practices.
These metrics are crucial for monitoring the health and performance of a service:
- Saturation: This measures the workload on your system, such as CPU, memory usage, or disk space, indicating how " full" your resources are.
- Errors: This tracks the rate of requests that fail, providing insight into the reliability and correctness of your service.
- Traffic: This measures the demand on your system, often in terms of the number of requests or the amount of data being processed.
- Latency: This measures the time it takes to service a request, reflecting the responsiveness of your system.
These four metrics collectively give a comprehensive view of a system's health and performance.
- External independent parties who have no connection with the application usually perform evaluation.
- Elected officials who regulate computing technology and cloud systems usually perform evaluation.
- Customers who are responsible for reviewing products and providing feedback usually perform evaluation.
- Monitors who collect data during programming, testing, and the post-development lifecycle usually perform evaluation.
Evaluation is typically carried out by external independent parties to ensure objectivity and impartiality, as they do not have a vested interest in the application being evaluated.
- Metrics
- Objectives
- Observability
- Alerting
Alerting is the component that notifies administrators when certain metrics exceed predefined thresholds, indicating potential issues that need attention.
Q3. Which type of metrics is, in many ways, just a higher-level extrapolation of application and server metrics?
- Application metrics
- Host-based
- Server pool
- External dependencies
Server pool metrics provide a broader view of the performance and health of a group of servers, aggregating individual server metrics to give an overall picture of the infrastructure's status.
Q4. How does data collected through the monitoring process help the development process of applications?
- The data proves to developers that monitoring needs to be performed regularly to ensure optimal performance of applications and services.
- Using this data, administrators and developers can identify and solve problems in the development process of applications.
- The data collected during the development process minimizes the time your service is down or running slowly.
- The data helps decision-makers to develop profitable applications and increase the popularity of their apps.
The primary benefit of monitoring data is to help developers and administrators detect and troubleshoot issues, ensuring the development process is smooth and the application performs well.
Q1. Which one of the following statements is the most appropriate definition of application monitoring?
- Application monitoring prevents all errors, issues, and problems within applications.
- Application monitoring is how application developers ensure that their applications are working as intended and are delivering the best user experience.
- Application monitoring is a wholly automated process, using monitoring tools that observe applications, fix issues, and then deliver updated applications to customers.
- Application monitoring is performed solely by a team of technical support specialists who manually watch and maintain applications in order to optimize the user experience.
This definition highlights the proactive and ongoing nature of application monitoring, emphasizing its importance in maintaining the quality and reliability of applications.
Q2. Which of the following statements most accurately summarizes the importance of the monitoring process?
- Monitoring ensures that an application is healthy, alerts developers and their teams of outages and problems, and helps developers deliver a useful and safe application.
- Monitoring is an assessment of whether a solution meets its stated goals, which were determined at the design stage or when the solution was implemented.
- Monitoring allows developers and operations teams to observe applications on your device and also on servers, databases, message queues, and other similar online programs.
- Monitoring provides visualizations, alerts, tracing, and dependencies tracking in order to maximize the profitability of an application and reputation of the developer.
This statement emphasizes the key aspects of monitoring: ensuring the health and functionality of an application, alerting developers to issues, and ultimately contributing to the delivery of a reliable and effective application.
Q3. Which one of the following statements identifies a monitoring type that allows application developers to map resources that an application depends on?
- Hall monitoring
- Dependency monitoring
- Metric monitoring
- User tracking and monitoring
Dependency monitoring involves mapping the resources that an application depends on, such as external services, databases, APIs, and other components. This type of monitoring helps developers understand the dependencies of their application and identify any potential points of failure or performance bottlenecks.
- Different monitoring types offer continuous monitoring and user-specific operating system (OS) options.
- Using different monitoring types allows developers to try out different tools in order to be familiar with the maximum number of options.
- Different types of monitoring benefits developers by providing options if one type of monitoring tool is unavailable or too expensive.
- Using multiple types of monitoring allows you to gain maximum visibility into your application and connected IT systems.
This highlights the overarching benefit of using multiple monitoring types, which is to achieve comprehensive visibility into the application and associated IT infrastructure, enabling better insights and decision-making.
Q5. Which one of the following statements explains the importance of using the Golden Signals in Monitoring?
- The Golden Signals are the only four metrics that are important to monitor.
- By using the Golden Signals, you reveal to customers how serious you are about your monitoring solutions.
- Using the Golden Signals, you can focus on your application’s most critical performance indicators and proactively monitor your application.
- The Golden Signals include all the metrics, logs, and information about applications and systems.
Golden Signals are a set of key performance indicators (KPIs) crucial for monitoring the health and performance of applications and systems. These signals provide essential insights into the overall performance, reliability, and user experience of an application or system, helping teams proactively identify and address issues before they impact users.
Q6. Which one of the four Golden Signals of Monitoring indicates service demand and helps developers fine-tune user experiences?
- Latency
- Errors
- Saturation
- Traffic
All four of the Golden Signals of Monitoring (Latency, Errors, Saturation, Traffic) can indicate service demand and help developers fine-tune user experiences, although some provide a stronger indication than others.
Traffic directly measures the volume of requests your service is receiving, giving a clear picture of user demand.
Q7. Which of the following statements identifies a key difference between monitoring and evaluation?
- Monitoring and evaluation are the same process, and the terms are used interchangeably.
- Monitoring is a routine ongoing process, and evaluations are long-term processes that may happen only a few times.
- Monitoring is a process performed by one person, and evaluation is performed by a whole team.
- Monitoring is only done once at the beginning of application development, and evaluation is only performed at the end of application development.
- Monitoring is continuous: It tracks progress and identifies issues throughout a project's lifecycle, providing real-time data for adjustments.
- Evaluation is periodic: It assesses the overall effectiveness and impact of a project at specific points, often at the end of a phase or the entire project.
- Dashboards, Historical Data, and Timelines
- Metrics, Observability, and Alerting
- Visibility, Trends, and Awareness
- Usage, Behavior, and Summaries
Three components represent the core elements of a monitoring system:
- Metrics: These are quantitative measurements that provide data about the performance, health, and behavior of a system or application.
- Observability: This refers to the ability to understand the internal state of a system based on its external outputs. It involves collecting and analyzing logs, traces, and other data to gain insights into system behavior.
- Alerting: This involves setting up notifications or alerts based on predefined thresholds or conditions to promptly notify stakeholders of any issues or anomalies detected within the system.
Q9. Which one of the following answers lists the important metrics you should track in a monitoring system?
- Important metrics you should track are host-based, application, network and connectivity, and server pool.
- Important metrics to track are stability, resources, project maturity, and level of service that you require.
- All metrics are important to track, regardless of your resources available and the purpose of your application.
- You should track the complexity and purpose of your application.
A brief explanation of each of the mentioned important metrics:
| Metric Category | Explanation |
|---|---|
| Host-based | Metrics related to individual hosts or machines, such as CPU usage, memory utilization, disk space, and network activity. Monitoring these metrics helps assess the health and performance of each host. |
| Application | Metrics specific to the application, including response time, throughput, error rates, and transaction rates. Monitoring application metrics provides insights into the application's performance and user experience. |
| Network and Connectivity | Metrics pertaining to network infrastructure and connectivity, such as bandwidth usage, latency, packet loss, and network errors. Monitoring network metrics helps ensure optimal network performance and reliability. |
| Server Pool | Metrics related to server pools or clusters, including load balancing, server health, and resource distribution across servers. Monitoring server pool metrics aids in optimizing resource allocation and maintaining high availability and scalability. |
Tracking these metrics provides comprehensive visibility into various aspects of your infrastructure and application, enabling effective monitoring, troubleshooting, and performance optimization.
Q10. How does data collected through the monitoring process help the development process of applications?
- The data helps decision-makers develop profitable applications and increase the popularity of their apps.
- The data collected during the development process minimizes the time your service is down or running slowly.
- Using this data, administrators can identify and solve problems in the development process of applications.
- The data proves to developers that monitoring needs to be performed regularly to ensure optimal performance of applications and services.
Data collected through monitoring provides valuable insights into the performance, behavior, and usage patterns of applications and infrastructure. By analyzing this data, administrators and developers can identify potential issues, optimize performance, prioritize development efforts, and enhance the overall quality of applications during the development process.
Q1. Which of the following behaviors is used by synthetic monitoring to improve a website or an application performance?
- Inferential
- Predictive
- Associative
- Prescriptive
Synthetic monitoring simulates user traffic and behavior to proactively identify performance issues in websites and applications. Prescriptive analytics goes beyond identifying problems; it suggests specific actions to address them.
Synthetic monitoring focuses on simulating real user scenarios to uncover performance bottlenecks and pinpoint areas for improvement. Prescriptive analytics aligns with the proactive nature of synthetic monitoring by recommending solutions to the performance issues identified.
- Scripting is a way to identify outages or availability issues.
- Scripting enables you to walk through important application flows.
- It is the ability to test out various “What If” scenarios.
- Scripting measures an industry or competitors’ performance.
In the context of synthetic monitoring, scripting refers to the creation of automated sequences that mimic user interactions with a website or application. These scripts are used to simulate real user behavior, allowing for continuous monitoring and testing of specific functionalities and workflows.
- Application monitoring increases your sales and drives the growth of your business.
- Application monitoring only provides visualization and alerts to detect anomalies and target security issues.
- Application monitoring allows you to decrease your application’s productivity and reduce the quality of the user experience.
- Application monitoring allows developers to fine-tune and maintain apps for increased performance and user satisfaction by providing a better understanding of app usage.
Application monitoring involves tracking and analyzing the performance and behavior of software applications. This process is essential for maintaining and improving the application's reliability, performance, and user experience.
Here's why the correct answer is the most appropriate summary:
- Fine-Tuning and Maintenance: Application monitoring provides detailed insights into how an application is performing. Developers can use this information to identify bottlenecks, optimize code, and make necessary adjustments to enhance performance.
- Increased Performance: By monitoring key metrics such as response times, error rates, and resource usage, developers can ensure that the application runs smoothly and efficiently. This proactive approach helps in preemptively addressing issues before they affect users.
- User Satisfaction: Understanding how users interact with the application helps developers to improve the user experience. By resolving issues quickly and optimizing performance, application monitoring contributes to higher user satisfaction and retention.
- Better Understanding of App Usage: Monitoring tools provide valuable data on how the application is used, which features are most popular, and where users may encounter problems. This insight helps in making informed decisions for future development and enhancements.
- Prometheus can collect millions of metrics every second.
- Prometheus works best with simple workloads, limited metrics, and simplified databases.
- Prometheus does not include official libraries for Go, Python, Ruby, and third-party libraries, mandating that developers create their own.
- Prometheus only works with Linux.
The correct answer is:
- Prometheus can collect millions of metrics every second.
Prometheus is a powerful and widely-used open-source system for monitoring and alerting, particularly well-suited for time-series data.
Prometheus is designed to handle high volumes of data efficiently, capable of collecting millions of metrics every second. This scalability makes it suitable for monitoring large and complex environments.
Q5. Which of the following best describes the primary function of application monitoring (APM) tools?
- APM tools follow collective tracing to detect the origins of errors.
- APM tools visually depict the event’s dependency and flow mapping connection.
- APM tools provide insights and overviews without focusing on specific problems.
- APM tools observe app components instead of visually depicting the events.
APM tools go beyond simple application observation. Their core function lies in providing a comprehensive view of how different parts of your application interact and flow together. This includes:
- Dependency Mapping: Visually showing how various components rely on each other.
- Event Flow Mapping: Tracing the journey of a user action through the application, highlighting interactions between components at each step.
By visualizing these connections, APM tools empower developers to pinpoint bottlenecks, identify performance issues, and understand how changes in one part of the application might impact others.
- Grafana is an error alerting tool that manages your databases and systems based on defined metrics and thresholds.
- Grafana is a proprietary charting and dashboard tool.
- Grafana is a professional cross-platform, open-source data visualization and metrics analysis tool.
- Grafana is a data visualization tool that only works on Linux and Unix machines.
The correct answer is:
- Grafana is a professional cross-platform, open-source data visualization and metrics analysis tool.
Grafana is widely recognized as a powerful and flexible tool for visualizing and analyzing data from various sources. Here’s why the correct answer best describes Grafana:
- Professional and Cross-Platform: Grafana is used by professionals across different industries to create detailed and interactive dashboards. It is cross-platform, meaning it can run on various operating systems including Linux, Windows, and macOS.
- Open-Source: Grafana is open-source, allowing users to modify, extend, and share the software freely. This contributes to a large community of users and contributors who enhance its capabilities.
- Data Visualization and Metrics Analysis: Grafana excels at creating visual representations of data, enabling users to analyze metrics and gain insights into their systems and applications. It supports a wide range of data sources, including time-series databases like Prometheus, InfluxDB, and more.
Q1. Which of the following is an important reason for using data visualization to help developers understand the data their monitoring tools have collected?
- Visualization helps developers understand the benefits and components of monitoring.
- Data visualization eliminates the need for metrics, numbers, and data by presenting information in stories and pictures.
- Data visualization is important because it can present massive amounts of data, allowing an operator to spot any trends or problems quickly.
- Visualization helps developers use illustrator programs that create graphically stunning and aesthetically pleasing art.
Data visualization is crucial because it enables developers to quickly identify trends, patterns, and anomalies in large datasets, facilitating faster and more effective decision-making.
Q2. Which one of the four types of alerts analyzes telemetry sent by your application to warn of potential failure anomalies?
- Log alerts
- Smart detection
- Activity log alerts
- Metric alerts
Smart detection alerts analyze telemetry data from your application to detect potential failure anomalies. This type of alert use advanced algorithms to identify issues that might not be immediately obvious through standard metric or log monitoring.
Q1. Which one of the following statements is the most appropriate summary of application monitoring?
- Application monitoring increases your sales and drives the growth of your business.
- Application monitoring allows you to decrease your application’s productivity and reduce the user experience.
- Application monitoring only provides visualization and alerts to detect anomalies and target security issues.
- Application monitoring provides a better understanding of app usage, allowing developers to fine-tune and maintain apps for increased performance and user satisfaction.
Application monitoring involves tracking and analyzing the performance and behavior of software applications. This process is essential for maintaining and improving the application's reliability, performance, and user experience.
Here's why the correct answer is the most appropriate summary:
- Fine-Tuning and Maintenance: Application monitoring provides detailed insights into how an application is performing. Developers can use this information to identify bottlenecks, optimize code, and make necessary adjustments to enhance performance.
- Increased Performance: By monitoring key metrics such as response times, error rates, and resource usage, developers can ensure that the application runs smoothly and efficiently. This proactive approach helps in preemptively addressing issues before they affect users.
- User Satisfaction: Understanding how users interact with the application helps developers to improve the user experience. By resolving issues quickly and optimizing performance, application monitoring contributes to higher user satisfaction and retention.
- Better Understanding of App Usage: Monitoring tools provide valuable data on how the application is used, which features are most popular, and where users may encounter problems. This insight helps in making informed decisions for future development and enhancements.
Q2. Which one of the following statements most accurately describes the application monitoring tool Prometheus?
- Prometheus provides data visualization, is customizable, and allows you to manage and explore logs and data more effectively.
- Prometheus is a text-based spreadsheet application that only collects data and requires a developer to interpret the data to make software updates and changes.
- Prometheus is a powerful analytics tool that collects and evaluates metrics for numeric time-series data.
- Prometheus is a social network for professionals to connect, share, and learn.
Prometheus is specifically designed to collect and analyze time-series data, making it a powerful tool for monitoring applications.
- Prometheus does not include official libraries for Go, Python, Ruby, or third-party libraries and mandates that developers create their own.
- Prometheus only works with Linux.
- Prometheus handles alerts and sends alert notifications using email, on-call notification systems, and chat platforms.
- Prometheus can collect millions of metrics every second.
Prometheus is known for its ability to handle a high volume of metrics efficiently, making it suitable for large-scale monitoring.
Q4. Which of the following is true for error monitoring, one of the essential aspects of application monitoring?
- It includes tracking user actions, session durations, conversion rates, and other relevant metrics to measure customer satisfaction.
- It checks the uptime and downtime of your application by periodically sending requests to verify its responsiveness.
- It identifies bugs or other issues in the code, helps you capture stack traces, and provides detailed information about the root cause of errors, enabling efficient debugging.
- It involves tracking metrics like response time, throughput, error rates, and resource utilization to ensure optimal performance.
Error monitoring focuses on detecting and diagnosing bugs and issues within the application code to facilitate effective debugging and resolution.
- Grafana is a simple, text-only visualization tool.
- Grafana is exclusively for developers who have reached the highest rank in Six Sigma.
- Grafana not only works on data stored in databases but also performs data collection to help you focus on just the data that is important to you.
- Grafana transforms your data into graphs and visualizations that help you make sense of massive amounts of monitored data.
Grafana is a powerful visualization tool that turns complex data into understandable graphs and charts, aiding in data analysis and decision-making.
- Grafana is a data visualization tool that only works on Linux and Unix machines.
- Grafana is a proprietary charting and dashboard tool.
- Grafana is a professional cross-platform, open-source data visualization and metrics analysis tool.
- Grafana is an error alerting tool that manages your databases and systems based on defined metrics and thresholds.
Grafana is an open-source tool that is widely used across various platforms for data visualization and metrics analysis.
- Popularity
- Storytelling
- Widgets
- Color palette
When selecting a visualization tool, storytelling is a key factor to consider. Effective data visualization goes beyond just presenting raw data; it should tell a compelling story that helps the audience understand the insights and trends hidden within the data.
Q8. Which one of the following answers summarizes one of the benefits of using visualization in monitoring?
- Visualization is the graphical representation of data collected from a business infrastructure and is used to help a business maintain its applications.
- Visualization is a program used for creating animations and graphics for non-profit government organizations ( NGOs).
- Visualization is used in monitoring to help developers create newer programs that users want to purchase.
- Visualization eliminates the need for metrics, numbers, and data by presenting information in stories and pictures.
Visualization in monitoring refers to the use of graphical representations to display the data collected from a business's infrastructure. This helps in understanding the performance, health, and status of applications and systems.
The benefits of using visualization in monitoring:
- Graphical Representation of Data: Visualization converts complex data into visual formats like charts, graphs, and dashboards, making it easier to comprehend and analyze.
- Helps Maintain Applications: By visualizing metrics and performance data, businesses can quickly identify issues, monitor trends, and make informed decisions to maintain and improve their applications.: By visualizing metrics and performance data, businesses can quickly identify issues, monitor trends, and make informed decisions to maintain and improve their applications.
- The best reason for using alerting in monitoring is to fix problems before they are noticed by users, hence improving the user experience.
- The most common reason to use alerting is to notify those responsible for that system, service, or application.
- The most common reason is to explain different types of alerts to developers and their teams.
- A common reason for alerting is the continuation of monitoring.
The primary purpose of alerting is to quickly inform the people responsible for maintaining the system when there is an issue. This enables a prompt response to potential problems, helping to minimize downtime and mitigate the impact on users.
Q10. Which one of the four types of alerts uncovers trends in usage and behavior to help you understand the effects of any changes made to your applications or infrastructure?
- Activity log alerts
- Log alerts
- Metric alerts
- Smart detection
Metric alerts are the type of alerts that uncover trends in usage and behavior to help you understand the effects of any changes made to your applications or infrastructure.
By monitoring metrics and setting up metric alerts, you can proactively notify yourself when important conditions are found in your monitoring data. So, if you're looking to gain insight into how your systems are performing and understand the impact of changes, metric alerts are the way to go!
- Completed operations
- Proprietary information
- Solutions for debugging operations
- Unlogged events
Logged events typically include completed operations. These logs record the details of actions that have been executed, which is essential for monitoring, auditing, and debugging purposes.
- Logs can be used for auditing purposes.
- Logs contain the program code kernel that the application runs on.
- Logs can automatically fix message flow issues and resolve application problems.
- Logs are a security issue and should never be used in applications.
Application logs are essential for various reasons, and one important reason is that they can be used for auditing purposes. Logs help track and record significant events within an application, making it possible to review and analyze these events for security, compliance, and troubleshooting.
Q3. Which of the following tools shape log data to make it more actionable by using automatic and custom parsing?
- Sumo Logic
- IBM Instana Observability (Instana)
- Datadog
- Mezmo
The tool that shapes log data to make it more actionable by using automatic and custom parsing is Mezmo.
Mezmo makes it easy to centralize log data with support for a broad range of ingestion options. Its Automatic and Custom Parsing feature allows you to shape log data, making it more valuable and actionable.
- Context propagation
- Distributed logging
- Distributed tracing
- Instrumentation
Instrumentation involves adding code to applications or services to generate traces and spans. This process is crucial in distributed tracing, where instrumentation allows the collection of detailed performance and diagnostic data, which is essential for monitoring and troubleshooting complex, distributed systems.
Q1. Which type of application log is useful for troubleshooting issues, detecting security breaches, and recording user actions?
- Access Logs
- Error Logs
- Performance Logs
- Event Logs
The type of application log that is useful for troubleshooting issues, detecting security breaches, and recording user actions is Event Logs.
These logs capture information about events that occur on a system, including application-specific events, security-related events, and user actions. Event logs are essential for monitoring and diagnosing problems in software applications and systems. They provide valuable insights into system behavior, errors, and potential security threats.
- Storing logs for a minimum of one year is not recommended
- Compliance mandates do not have log storage and retention requirements
- Storing logs in the cloud can impact retrieval speed
- AES-256 encryption is used for data at rest when storing logs in the cloud
AES-256 encryption is commonly used for securing data at rest, including logs stored in the cloud. This ensures that sensitive log data is protected against unauthorized access. Other statements are incorrect because storing logs for a certain period is often required for compliance, compliance mandates do have storage requirements, and cloud storage can offer efficient retrieval mechanisms.
- Mezmo is a robust observability platform to manage and take action on your telemetry data.
- Mezmo is a cloud storage platform to manage, store, and retrieve telemetry data.
- Mezmo is a security platform with analytics and troubleshooting features.
- Mezmo is a network analytics tool to manage and secure data networks.
Mezmo, previously known as LogDNA, is a robust observability platform for managing and taking action on your telemetry data. Mezmo Log analysis gives operations teams control over their data flow and empowers developers to quickly find value from their logs.
- Storing logs for a minimum of one year is not recommended
- Compliance mandates do not have log storage and retention requirements
- Storing logs in the cloud can impact retrieval speed
- AES-256 encryption is used for data at rest when storing logs in the cloud
AES-256 encryption is commonly used for securing data at rest, including logs stored in the cloud. This ensures that sensitive log data is protected against unauthorized access. Other statements are incorrect because storing logs for a certain period is often required for compliance, compliance mandates do have storage requirements, and cloud storage can offer efficient retrieval mechanisms.
Q1. Which one of the following statements is the most appropriate definition of application logging?
- Logging contains developer notes about how the application works, how it was designed, when the last update was made, and whom to contact if there’s an issue.
- Logging is a recording of a series of messages containing information about events within an application.
- Logging is a pre-emptive activity performed by professional arborists to prevent destructive wildfires in forests.
- Logging is how developers identify information, design applications based on that information, and then extract meaningful data.
Application logging refers to the practice of recording information about the execution of an application. This information typically includes messages about the application's state, errors, warnings, and other significant events that occur during its operation.
- Logs can automatically fix message flow issues and resolve application problems before they become an issue.
- Logs are a security issue and should never be used in applications.
- Logs contain the program code kernel that the application runs on.
- Logs can be used for auditing purposes.
Application logs serve multiple important purposes, one of which is auditing. Logs provide a record of events and actions that have taken place within an application, which can be crucial for tracking changes, identifying unauthorized access or modifications, and ensuring compliance with regulatory requirements.
- End User License Agreement (EULA) details
- Open source or proprietary information
- Completed operations
- Copyright dates
Logged events usually include information about actions and operations that have been completed within the application. This can involve details such as when the operation started and finished, its outcome, any errors encountered, and other relevant data that can help in understanding the application's behavior and diagnosing issues.
Q4. Imagine your application is an enterprise application used by many businesses. When you are determining what your application should log, you should consider which one of the following?
- Application testing
- Data and application operations
- Application training and feedback
- Service functions
For an enterprise application used by many businesses, it's crucial to log data and application operations. This includes information about transactions, user activities, system performance, errors, and other events that provide insights into the application's behavior and help in troubleshooting issues. Effective logging of these operations aids in maintaining the application's reliability, performance, and security, which are vital for business-critical applications.
Q5. Which of the following is a fully automated application performance management (APM) solution designed for the challenges of managing microservice and cloud-native applications?
- Datadog
- IBM Instana Observability (Instana)
- Sumo Logic
- Splunk
IBM Instana Observability (Instana) is specifically designed for the complexities and dynamic nature of microservices and cloud-native applications. It offers automated monitoring, tracing, and performance management to provide real-time observability into these types of environments.
- Configure loggers
- Set up a centralized logging server
- Monitor your logs
- Define log retention policies
All the listed options (configure loggers, set up a centralized logging server, monitor your logs, and define log retention policies) are crucial steps to implement distributed logging.
- Configure Loggers: This involves integrating the chosen logging framework within your applications. You'll define what gets logged, the log level (debug, info, error etc.), and the format of the log messages.
- Set up a Centralized Logging Server: Here, you establish a central location to aggregate logs from all your distributed applications. This could be a dedicated log server, a cloud-based service, or even a distributed file system.
- Monitor Your Logs: Once logs are collected centrally, set up log monitoring tools to analyze and identify any issues. This could involve creating dashboards or alerts based on specific log patterns.
- Define Log Retention Policies: Determine how long you want to store your logs. Factors to consider include compliance regulations and the value of the data for troubleshooting purposes. Implement a strategy to archive or delete logs after a set period.
Therefore, while all options are important, the most logical next step after choosing a logging framework would be: Configure Loggers within your applications.
- Testing and adjustment of the logging setup
- Output options such as console or file
- Timestamp and actual message being logged
- Programming language and needs
Formatted log messages typically don't include information about testing and adjustments or the programming language used. Their focus is on providing context and details about the logged event.
Here's what you'll normally find in a formatted log message:
- Timestamp: This indicates the exact time the event occurred, crucial for troubleshooting and sequencing events.
- Log Level: This categorizes the severity of the event (e.g., debug, info, warning, error, critical).
- Source: This identifies the application or component that generated the log message.
- Message: This is the actual content of the log, describing the event in detail. It may include relevant data or error codes.
- Additional Context (Optional): Some logging frameworks allow including supplementary details like thread IDs, hostnames, or user information for further troubleshooting.
- Log parsing converts log files into a readable format for log management systems.
- Log parsing is a feature only available in certain log management solutions.
- Log parsing can be performed without explicitly determining the format of log files.
- Log parsing is the final step in the process of log formatting.
Log parsing involves analyzing log files and converting them into a structured, readable format that can be ingested and utilized by log management systems. This process often includes extracting specific fields from the logs, such as timestamps, log levels, messages, and other relevant data, so that the logs can be effectively searched, filtered, and analyzed.
Q9. Which of the following is the correct consideration for the log retention policies regarding the criticality and security dimensions?
- Services tracking customer behavior for improvement purposes can have a shorter retention policy.
- Applications involving sensitive or personal data should have a shorter retention policy.
- Retention policies should be the same for all parts of the system, despite their criticality.
- Critical components of a system can have a shorter retention policy.
Less critical data for non-sensitive purposes can have a shorter storage duration.
- Visualize your log data over a time period
- Easily surface the data from logs
- Get notifications about the system’s activity
- Set dynamic thresholds and alerts
This feature helps in setting thresholds and alerts dynamically to protect against sudden spikes or anomalies in the system's activity, allowing users to proactively monitor and respond to potential issues.
- Faster identification and resolution of issues
- Ensure that an application or service is up and responding
- Identify issues, track trends, and make informed decisions
- Ensure that applications and web pages are responsive
Observability goes beyond simply knowing if something is wrong. It allows you to see how the system is behaving over time, identify patterns, and make data-driven decisions for improvement.
- Metrics can debug issues in a distributed system
- Metrics are an extremely easy format to generate
- Metrics are highly quantitative to associate with alerting thresholds
- The granular information from metrics allows retrospective replaying of support incidents
Quantitative metrics allow you to set precise alerting thresholds. For example:
- If the error rate exceeds 5%, trigger an alert.
- If CPU utilization exceeds 80%, send a notification.
These thresholds are based on specific numeric values, making it easier to define actionable alerts.
Q3. How does implementing a robust observability solution provide an advantage in terms of reducing Mean Time to Repair (MTTR)?
- By enabling a reactive approach to troubleshoot
- By gaining insights into system resource utilization
- By monitoring individual systems effectively
- By identifying correlations between events for expedited troubleshooting
Observability tools correlate logs, metrics, and traces across distributed systems, providing a holistic view of the environment. This correlation helps quickly pinpoint the root cause of issues, significantly speeding up the troubleshooting process.
Q4. Which of the following cloud observability tools enables unlimited storage capacity for Prometheus deployments?
- Thanos
- FluentD
- New Relic
- AWS CloudWatch
Thanos is designed to extend Prometheus with features like unlimited storage capacity, global querying, and high availability. It allows for long-term storage of metrics and seamless scaling of Prometheus.
- Sampling accurately represents the actual data.
- Sampling always makes it easy to diagnose complex performance issues.
- Sampling reduces computational overhead and storage requirements.
- Sampling provides high resolution in monitoring systems
Sampling helps manage large volumes of data by reducing the amount of data collected and stored. This approach lowers computational overhead and storage requirements while still providing a representative view of the system's performance.
- Usage telemetry
- Performance telemetry
- Error telemetry
- Security telemetry
Usage telemetry collects data on how users interact with the application, such as which features are used most often, user workflows, and user behavior patterns. This helps developers understand how the application is being utilized and can guide improvements and enhancements.
Q2. Which of the following distributed tracing tools’ visibility into the request flow between microservices allows developers to analyze the performance and behavior of their applications?
- New Relic
- Jaeger
- Sumo Logic
- Instana
Jaeger is a distributed tracing tool specifically designed to monitor and troubleshoot microservices-based applications. It provides detailed visibility into the request flow between microservices, helping developers analyze performance and behavior issues within their applications.
- Instrumentation, Trace context, Metrics and metadata, Analysis and visualization
- Tags, Queries, Stack traces, Logs, Context-giving events
- Allocate unique external request ID, Pass ID to all services handling the request, Include ID in log messages, Record information about requests and operations
- Latency, Traffic, Errors, Saturation
Visualizing data and monitoring end-to-end requests involves several key steps:
- Instrumentation: Adding code to your application to collect data on requests and performance.
- Trace context: Ensuring that tracing information is propagated across different services and components.
- Metrics and metadata: Collecting detailed metrics and metadata about the requests.
- Analysis and visualization: Analyzing the collected data and visualizing it to understand system performance and behavior.
- Observability involves looking at predefined metrics or logs to detect problems or anomalies over time.
- Observability is a reactive approach that provides a limited system view.
- Observability provides visibility into the external workings of a system.
- Observability allows teams to identify and resolve complex system issues quickly.
Observability allows teams to gain insights into the internal state of a system from the data it produces, helping them identify and resolve complex issues quickly.
Q2. By leveraging the 3 pillars of observability, you can gain which of the following to improve customer experience?
- Understanding of problem indicators in an application
- Understanding of request processing stages
- Understanding of management tasks
- Understanding of the performance issues in microservices
The three pillars of observability — logs, metrics, and traces—provide a comprehensive understanding of performance issues, especially in microservices architectures, thereby improving customer experience.
- Improve transmission of data between systems
- Collection of data from devices for analysis and optimization purposes
- Enhance communication between systems for optimization purposes
- Monitor and analyze collected data for optimization purposes
Telemetry refers to the automated collection of data from devices, which is then used for analysis and optimization purposes.
Q4. Which of the following techniques can enhance application performance and identify problems before they become critical?
- Distributed tracing
- Telemetry
- Both logging and tracing
- Distributed logging
Both logging and tracing provide comprehensive insights that can enhance application performance and identify potential issues before they become critical.
- Providing deep analysis without context
- Automating system modifications
- Revealing interconnections between application components and services
- Monitoring individual systems effectively
Enterprise observability reveals interconnections between application components and services, which is crucial for optimizing resource performance.
- Determine CPU usage of the applications
- Identify bottlenecks and other issues in the system
- Collect logs from various sources to identify unusual trends
- Analyze user behavior and improve the user experience
The main objective of sampling tracing data is to identify bottlenecks and other issues within the system to ensure smooth operation.
- It can merely monitor application performance.
- It is a must-have tool to process and monitor real-time, data-heavy, and new-age digital systems.
- It applies to smaller and more compact digital ecosystems.
- It applies to e-commerce companies and social media platforms.
Cloud-native observability tools are crucial for managing the complexity of modern applications, particularly those built with a cloud-native approach. Here's why:
- Real-time and data-heavy: Cloud-native applications are dynamic and generate a vast amount of data. Observability tools enable real-time monitoring of this data to identify issues and optimize performance.
- New-age digital systems: Microservices architectures, containerization, and serverless functions are common in cloud-native deployments. Observability tools are designed to handle the distributed nature and dynamic scaling of these systems.
Q8. Which of the following is the important factor to consider when selecting cloud native observability tools?
- It must enable you to focus primarily on the health of the local business division.
- It works well with highly distributed cloud native applications and adopts new ways to handle huge data sets.
- It is far from leveraging the power of AI and business intelligence in a cloud native environment.
- It helps you monitor the transactions and events instead of diving into the root cause of the issues.
The important factor to consider when selecting cloud-native observability tools is: It works well with highly distributed cloud-native applications and adopts new ways to handle huge data sets.
Here's why this is the most crucial factor:
- Highly distributed applications: Cloud-native deployments often involve microservices architectures and distributed systems. The observability tool needs to be able to collect data and provide insights across these distributed components.
- Huge data sets: Cloud-native applications generate a significant amount of data, including logs, metrics, and traces. The tool should efficiently handle this data volume for real-time analysis.
Q9. Website monitoring is an important tool for understanding digital user experience. How does Instana support website monitoring?
- By analyzing actual URL request times
- By tag-based filtering, grouping, and visualization
- By analyzing actual browser request times and route loading times
- By using an iOS/Android agent, which is installed as a dependency on mobile apps
Instana supports website monitoring by analyzing actual browser request times and route loading times, providing insights into user experience.
Q10. Select the correct sequence of the five steps followed to implement tracing for container-based applications.
- Choose a tracing tool → Configure the tracing agent → Verify functionality → Instrument your code → Deploy the tracer
- Choose a tracing tool → Verify functionality → Instrument your code → Configure the tracing agent → Deploy the tracer
- Choose a tracing tool → Configure the tracing agent → Instrument your code → Deploy the tracer → Verify functionality
- Choose a tracing tool → Instrument your code → Configure the tracing agent → Deploy the tracer → Verify functionality
The correct sequence for implementing tracing in container-based applications is as follows:
-
Choose a Tracing Tool: Begin by selecting an appropriate tracing tool. There are various options available, such as OpenTelemetry, Jaeger, or Zipkin. Choose one that aligns with your application's requirements and ecosystem.
-
Instrument Your Code: Next, instrument your application code. This involves adding tracing instrumentation to your services, APIs, and other components. Instrumentation allows the tracing tool to collect data about requests, spans, and traces.
-
Configure the Tracing Agent: Configure the tracing agent (which is part of the chosen tracing tool) to communicate with your application. Set up the agent to collect and transmit trace data to the backend.
-
Deploy the Tracer: Deploy the tracing agent alongside your application. Ensure it runs within the same containerized environment. The tracer will intercept requests and generate trace data.
-
Verify Functionality: Finally, verify that tracing is functioning correctly. Monitor the traces, spans, and any reported issues. Use the tracing dashboard or visualization tools to analyze the data and identify bottlenecks or performance problems.
Q1. Your organization has experienced several issues with performance, outages and is starting to see some loss in revenue. You have been tasked with finding a solution to help identify and resolve issues quickly and minimize downtime. What type of solution should you look for?
- Application monitoring
- Incident prevention
- Dynamic analysis
- Alerting
Application monitoring helps identify and resolve performance issues and outages quickly, thereby minimizing downtime and potential revenue loss.
Q2. Your company is looking for a way to gain a deeper insight into their business systems to understand how they perform and how customers utilize their web apps. Vast amounts of data are collected from business systems, but how can they make sense of it?
- Application metrics
- Performance indicators
- System event logs
- Visualization
Visualization tools help make sense of vast amounts of data by turning it into comprehensible and actionable insights, aiding in understanding performance and customer utilization of web apps.
Q3. Your organization is experiencing rapid growth with several new systems and applications deployed. Your staff and budget haven’t expanded, and it’s becoming increasingly difficult to stay on top of issues and cumbersome trying to identify the root causes of problems. What solution can you implement to help you become more proactive and less reactive in this situation?
- A monitoring and alerting system
- An alerting system could call immediate attention to problems
- Hiring additional staff to help manage and respond to issues
- A log analysis system could help pinpoint problems in applications
Implementing a monitoring and alerting system helps proactively identify and address issues, which is crucial when dealing with rapid growth and limited resources.
Q4. When looking into monitoring systems for your organization, what is an essential component that your monitoring solution should provide?
- Event-driven architecture
- Metrics
- Component-based monitoring
- Service orientation
Metrics are essential for a monitoring solution as they provide quantifiable data to track and analyze the performance of systems and applications.
Q5. Your monitoring system notifies you of a latency issue with your web application. Instead of searching through hundreds of possible causes, what can help you quickly speed up the process?
- Viewing system logs to see what might be causing the latency
- Analysing log file timestamps to see where the latency is coming from
- Using alerts to notify you about latency
- Using Golden Signals to help identify the issue
Golden Signals (latency, traffic, errors, and saturation) help quickly pinpoint the cause of latency issues by focusing on the most critical aspects of system performance.
Q6. Your organization currently monitors all of its systems. You are trying to make the case that simply monitoring systems isn’t enough. What is your justification for using more than just system monitoring to give your organization more insight into its systems and infrastructure?
- Security monitoring can collect and analyze log data to block potential threats before they happen
- Real user monitoring could provide perspective on how users perceive and respond to application performance
- Application monitoring could give your organization insight into metrics and app performance and send alerts to warn of problems
- There are many types of monitoring tools that can help your organization
To justify the need for more than just system monitoring in your organization, consider the following points:
-
Security Monitoring:
- Security monitoring is essential because it not only collects and analyzes log data but also proactively identifies and blocks potential threats before they can cause damage. This capability is crucial for preventing data breaches, mitigating cyber attacks, and ensuring compliance with security regulations.
-
Real User Monitoring (RUM):
- RUM provides invaluable insights into the actual experience of users interacting with your applications. It measures how real users perceive and respond to application performance, helping identify performance bottlenecks, usability issues, and areas for improvement. This user-centric approach ensures that the applications meet user expectations, leading to better user satisfaction and retention.
-
Application Monitoring:
- Application monitoring goes beyond basic system monitoring by offering detailed metrics and performance data related to specific applications. It can alert your organization to issues such as slow response times, high error rates, or resource bottlenecks. This proactive alerting allows for quicker resolution of problems, minimizing downtime and maintaining high levels of application performance and availability.
-
Diverse Monitoring Tools:
- Relying on a variety of monitoring tools provides a comprehensive understanding of your IT environment. Different tools can focus on various aspects such as network performance, database health, security events, and user experience. This holistic approach ensures that all potential issues are detected and addressed promptly, leading to more stable and efficient system operations.
By implementing a multi-faceted monitoring strategy, your organization can gain deeper insights into its systems and infrastructure, leading to improved performance, security, and user satisfaction. This comprehensive approach ensures that all aspects of the IT environment are monitored, analyzed, and optimized effectively.
Q7. Your company is currently using application monitoring to gather metrics about application performance and for alerting. Your director wants you to implement an evaluation solution, too. Why would your company think evaluation is necessary? Isn’t monitoring sufficient?
- Your company wants to know how well an application is performing
- Implementing evaluation should have been done during the design phase of the application, and the development team overlooked it
- The CIO believes an evaluation solution is needed
- The monitoring system should be sufficient because it can identify, measure, and evaluate your application’s performance
Evaluation solutions are necessary to assess application performance comprehensively, ensuring that the application meets expected standards and performs optimally.
Q8. Your monitoring system has an alerting feature that you’ve configured to the best of your ability. It is working; however, it isn’t detecting some problems quickly enough. At other times, alarms are triggered by normal operations. What could be the cause?
- You receive genuine and false alerts indicating that the alert feature works. Perhaps you need to calibrate your thresholds.
- The servers you’re monitoring are overloaded, need upgrades, and are experiencing legitimate issues – causing the alerting feature to misfire
- You’re unsure because you’ve set up alerts to catch as many problems as possible
- There are legitimate issues occurring that are triggering alarms, and you haven’t figured out what’s causing false alarms
Calibration of thresholds is crucial to minimize false positives and ensure that genuine issues are detected promptly.
Q9. Your company is using Prometheus, an open-source monitoring and alerting system. You also have a time-series database and multiple other data sources and need to integrate them into a single, organized view. In conjunction with Prometheus, what could you use to make that happen?
- Stride
- Flask
- Grafana
- Snyk
Grafana integrates with Prometheus and other data sources to provide a unified view through rich, customizable dashboards.
Q10. Your organization is interested in collecting metrics to evaluate the health and performance of an individual machine, such as CPU usage or disk space, rather than its application stacks and services. What kind of metrics is your organization trying to gather?
- Network and connectivity
- Application
- Server pool
- Host-based
Host-based metrics focus on individual machine performance indicators like CPU usage and disk space, distinct from application stack metrics.
Q11. After defining your telemetry goals and choosing the right data collection tools, what is the next step involved in implementing telemetry in application development?
- Design your telemetry schema
- Analyze the collected data
- Iterate on the design
- Implement telemetry collection
After defining goals and choosing tools, the next step is to design the telemetry schema, which structures how data will be collected and stored.
Q12. You are a DevOps engineer working with an organization that has recently implemented a leading log monitoring tool. Which of the following tools enables your firm to ingest all of their log data to a single platform, normalize it, and route it to the appropriate teams?
- Mezmo
- Instana
- Datadog
- Sumo Logic
Mezmo (formerly known as LogDNA) enables the ingestion, normalization, and routing of log data to the appropriate teams on a single platform.
Q13. Mark is an IT administrator for a medium-sized organization that handles sensitive customer data, which needs to be managed and analyzed effectively. He is responsible for implementing a log storage system that adheres to best practices. Considering the scenario, which of the following options would be the most suitable for storing logs in this organization?
- Storing logs on individual servers or applications
- Using a cloud-based solution like AWS CloudWatch
- Allowing unrestricted access to log data
- Rotating logs regularly to prevent storage space issues
Cloud-based log storage solutions like AWS CloudWatch adhere to best practices by providing scalable, secure, and manageable log storage.
Q14. TecNet Company wants to implement sampling in their logging system to reduce storage requirements and improve analysis efficiency. Which sampling strategy should they consider?
- Time-based sampling
- Event-based sampling
- Random sampling
- Size-based sampling
For TecNet Company to implement sampling in their logging system to reduce storage requirements and improve analysis efficiency, the most appropriate sampling strategy to consider would be:
- Event-based sampling
Here’s why event-based sampling is the most suitable option:
-
Relevance of Data:
- Event-based sampling allows the company to focus on capturing logs related to specific events that are of higher importance or interest. This ensures that the most critical and relevant data is retained, making the analysis more meaningful and efficient.
-
Storage Efficiency:
- By only logging significant events, the amount of data stored is reduced significantly. This leads to lower storage requirements and costs, as only the most pertinent information is being kept.
-
Improved Analysis:
- Analyzing event-specific logs can be more straightforward and insightful as it filters out the noise of less important logs. This helps in quicker identification and resolution of issues, and more accurate trend analysis.
Q15. Which of the following processes explains how information about a trace is passed between different services and systems?
- Distributed logging
- Parent/Child relationship
- Instrumentation
- Context propagation
The process that explains how information about a trace is passed between different services and systems is context propagation.
Context propagation ensures that relevant contextual information (such as trace identifiers, correlation IDs, and other metadata) is carried along as requests flow through various components of a distributed system.
Q16. You want to implement the synthetic monitoring solution to monitor your application's or website's performance. Which of the following statements is true about synthetic monitoring?
- Synthetic monitoring fails to capture the diversity of page users.
- Synthetic monitoring provides long-term performance trends.
- Synthetic monitoring relies on real user transactions to collect performance data.
- Synthetic monitoring uses a snippet of JavaScript code to track individual users and report performance metrics.
I'll provide a response combining the strengths of previous answers and addressing potential shortcomings:
Correct Answer:
- Synthetic monitoring provides long-term performance trends.
Explanation:
Synthetic monitoring offers a proactive approach to monitoring application and website performance. It simulates user actions through scripted transactions, enabling you to:
- Establish performance baselines.
- Track trends over time to identify performance improvements or regressions.
- Gain insights into how performance varies across different factors:
- Geographic locations
- Device types
- Network conditions
- Time of day
Q17. You run an online store with a website with significant bounce rates and sluggish page load times. The development team used the three observability pillars to overcome these problems. Which of the following correctly defines the three pillars of observability?
- Logs: Real-time data of events that show the health of system components. Metrics: Real-time operating data generated by platform software, including middleware and operating systems. Traces: Records that capture detailed information about individual events or transactions.
- Logs: Records to identify the component or step in which the problem is occurring. Metrics: Real-time providing context-specific details about a system's behavior. Traces: Records of information for explicit integration of network components.
- Logs: Records of events, in textual or human-readable form, generated by infrastructure elements. Metrics: Real-time operating data, typically accessed through an API using a pull or polling strategy. Traces: Records of the information pathways or workflows created to follow a work item.
- Logs: Records of events, typically accessed through an API using a pull or polling strategy xyz Metrics: Real-time operating data generated by platform software, including middleware and operating systems. Traces: Records containing granular details of an application’s request processing stages.
This definition accurately describes the three pillars of observability:
- logs for event records,
- metrics for real-time data,
- traces for tracking information pathways.
Q18. You are using tracing tools to instrument your code with markers, revealing the intricate paths it takes during execution. Which of the following is true regarding tracing tools?
- They allow you to manage the performance and availability of software applications.
- They automatically collect, transmit, and measure data from remote sources.
- They allow instrumenting code to emit trace data at key points in the application.
- They allow you to measure the current state of system-based generated information.
Tracing tools empower you to strategically insert markers (spans or annotations) within your code at crucial execution points. These markers capture timing and contextual information, forming a trace that depicts the path your code takes through the application:
- Function calls
- Database interactions
- External API calls
- Other key events in the application's workflow
Q19. Identify the correct order of the steps for implementing application monitoring from the steps listed below.
- Define the key metrics
- Choose a monitoring tool
- Identify monitoring goals
- Instrumentation
- Test and validate
- Set up alerts
- Data storage and visualization
- 3>2>1>7>6>4>5
- 2>1>4>5>3>7>6
- 3>2>1>4>6>7>5
- 2>1>3>4>7>5>6
The correct order starts with identifying goals, choosing tools, defining metrics, setting up instrumentation, alerts, data storage and visualization, and finally testing and validation.
Q20. Which of the following statements about an ideal cloud native observability tool that empowers organizations is true?
- Leverage AI and business intelligence
- Focus on specific aspects of the business
- Support selected teams, including IT and engineering
- Minimize the uses of modern applications
Cloud-native observability tools often incorporate AI and machine learning to analyze large volumes of data, detect anomalies, and provide actionable insights. These technologies enhance observability by automating tasks and identifying patterns that might be missed manually. Therefore, this statement is true.