Monitoring u0026 Logging in Serverless Applications Quiz Quiz

Explore key concepts and practices involved in monitoring and logging for serverless applications. This quiz helps reinforce understanding of event tracking, error detection, log analysis, and best monitoring strategies in serverless environments.

  1. Understanding Cold Starts

    Which monitoring metric is most useful for identifying cold start issues in serverless functions that handle user authentication events?

    1. Function initialization duration
    2. Log file size
    3. CPU utilization percentage
    4. Network packet loss rate

    Explanation: Function initialization duration directly indicates the time it takes for a serverless function to start, which helps identify cold start problems. CPU utilization percentage is less relevant to start-up delays in serverless. Network packet loss rate does not correlate with function initialization. Log file size tells you nothing about the time functions take to start.

  2. Tracing Event Flows

    When a serverless application processes orders by invoking several functions in sequence, which logging technique helps reconstruct the entire event journey across functions?

    1. Distributed tracing with correlation IDs
    2. Error code counting
    3. Single function log output
    4. Network address translation logs

    Explanation: Distributed tracing with correlation IDs allows you to track requests as they move across multiple functions, making it possible to reconstruct the full path of an event. Logging only single function outputs misses inter-function relationships. Network address translation logs are unrelated to serverless event flows. Counting error codes only gives you totals, not the path taken.

  3. Detecting Anomalies in Metrics

    Which monitoring approach is most effective for automatically detecting unusual spikes in function invocation errors during a flash sale?

    1. Tracking database schema changes
    2. Counting API keys in configuration files
    3. Setting up alerting rules with anomaly detection
    4. Manually scanning daily log exports

    Explanation: Alerting rules combined with anomaly detection tools can identify sudden, unexpected increases in errors in real-time. Manually scanning logs is time-consuming and not practical for rapid detection. Counting API keys is unrelated to error spikes, and tracking database schema changes addresses persistence, not function errors.

  4. Choosing Log Levels Appropriately

    In a serverless billing module, which log level is best suited for capturing information about routine successful operations without cluttering logs with unnecessary details?

    1. Error
    2. Debugg
    3. Fatal
    4. Info

    Explanation: The Info log level is ideal for recording successful and usual operations, providing enough insight without excessive detail. Debugg (even if misspelled) is used for more detailed troubleshooting and can clutter logs. Error and Fatal are reserved for issues and failures, not successful actions.

  5. Log Retention Strategies

    Why is it important to define a log retention policy when monitoring a serverless web application that handles customer registrations?

    1. To manage storage costs and comply with data regulations
    2. To increase the maximum number of concurrent executions
    3. To reduce CPU usage during runtime
    4. To ensure APIs respond more quickly

    Explanation: Defining a log retention policy helps control storage expenses and ensures compliance with legal data retention requirements. Reducing CPU usage, increasing concurrency, or improving API speed are not direct outcomes of log retention policies. Unmanaged logs can lead to unnecessary expenses and regulatory issues.