Microsoft AZ-400 Practice Test 92

Question 910

After the team applies this response: Enable appropriate Azure Monitor Agent and VM Insights or supported guest data collection rules. In the situation—An Azure VM fleet has memory pressure but Azure Monitor receives only basic platform metrics and no guest data. Which evidence BEST demonstrates the response is effective?

Recompute a sample error-rate metric from raw total and failed request counts.
Inspect resource time series, pod restarts and request errors around the onset of saturation.
Trigger a relevant change and verify mandatory scan jobs execute and contribute to the status gate.
Inspect collected guest performance counters and confirm expected machines and periods appear in Logs.

Explanation

Correct Answer

The governing engineering principle is: Guest telemetry requires appropriate agent collection and permissions beyond platform-level resource metrics.

Incorrect Answers

Service reliability ratios require clearly scoped compatible populations and time intervals. Instead, the response must inspect collected guest performance counters and confirm expected machines and periods appear in Logs.

Resource saturation and application symptoms must be correlated before assigning root cause. The relevant control is to inspect collected guest performance counters and confirm expected machines and periods appear in Logs.

Instrumentation must measure the presence of required checks as well as the outcome of executed checks. The needed step here is to inspect collected guest performance counters and confirm expected machines and periods appear in Logs.

Question 911

A second service cannot diagnose disk usage or guest process exhaustion using host-only Azure resource charts. Which engineering principle BEST explains the relevant requirement?

Guest telemetry requires appropriate agent collection and permissions beyond platform-level resource metrics.
Operational metrics should distinguish waiting for capacity from work actively being performed.
Resource saturation and application symptoms must be correlated before assigning root cause.
Reliable operational analysis requires visibility into exclusions that can change apparent performance.

Explanation

Correct Answer

The second engineering situation illustrates the control boundary captured by this principle: Guest telemetry requires appropriate agent collection and permissions beyond platform-level resource metrics. The scenario is not resolved by an unrelated pipeline or policy activity.

Incorrect Answers

Operational metrics should distinguish waiting for capacity from work actively being performed. That principle concerns analyze metrics from instrumentation, whereas this specific incident is governed by: Guest telemetry requires appropriate agent collection and permissions beyond platform-level resource metrics.

Resource saturation and application symptoms must be correlated before assigning root cause. That principle concerns analyze metrics from instrumentation, whereas this specific incident is governed by: Guest telemetry requires appropriate agent collection and permissions beyond platform-level resource metrics.

Reliable operational analysis requires visibility into exclusions that can change apparent performance. That principle concerns analyze metrics from instrumentation, whereas this specific incident is governed by: Guest telemetry requires appropriate agent collection and permissions beyond platform-level resource metrics.

Question 912

A second service cannot diagnose disk usage or guest process exhaustion using host-only Azure resource charts. Which unsupported assumption should the engineer challenge FIRST?

A VM resource appearing in Azure Monitor does not mean every needed guest operating system counter is collected.
Counting every log message is not the same as counting distinct failed pipeline executions.
Fewer visible alerts do not necessarily mean the underlying application error rate has decreased.
Dividing errors by only successful requests is not equivalent to errors divided by all attempted requests.

Explanation

Correct Answer

A VM resource appearing in Azure Monitor does not mean every needed guest operating system counter is collected. This is the relevant unsupported assumption because guest telemetry requires appropriate agent collection and permissions beyond platform-level resource metrics.

Incorrect Answers

This caution is valid in analyze metrics from instrumentation, but it is not the failure assumption illustrated by this scenario. The directly relevant risk is: A VM resource appearing in Azure Monitor does not mean every needed guest operating system counter is collected.

Question 913

A storage-backed document system has increasing request failures but no storage-specific monitoring dashboards. Which design or operational change MOST directly satisfies the requirement?

Validate metric definitions, suppression policies and raw telemetry before treating reduced alerts as reliability improvement.
Configure Azure Monitor for Storage metrics and diagnostic data appropriate to the account and workload.
Use consistent numerator and denominator definitions over the same time window and request population.
Correlate container CPU, memory, throttling and application response metrics with scaling events.

Explanation

Correct Answer

Dependency-specific monitoring distinguishes storage service failures from application-level symptoms. The most directly responsive design or operational step is: Configure Azure Monitor for Storage metrics and diagnostic data appropriate to the account and workload. Confirm the result by: Reproduce a controlled storage failure and verify latency, availability and throttling telemetry is accessible.

Incorrect Answers

Reliable operational analysis requires visibility into exclusions that can change apparent performance. The appropriate remedy is to configure Azure Monitor for Storage metrics and diagnostic data appropriate to the account and workload.

Service reliability ratios require clearly scoped compatible populations and time intervals. Instead, the response must configure Azure Monitor for Storage metrics and diagnostic data appropriate to the account and workload.

Resource saturation and application symptoms must be correlated before assigning root cause. The relevant control is to configure Azure Monitor for Storage metrics and diagnostic data appropriate to the account and workload.

Question 914

After the team applies this response: Configure Azure Monitor for Storage metrics and diagnostic data appropriate to the account and workload. In the situation—A storage-backed document system has increasing request failures but no storage-specific monitoring dashboards. Which evidence BEST demonstrates the response is effective?

Reproduce a controlled storage failure and verify latency, availability and throttling telemetry is accessible.
Run a known test failure and confirm it appears exactly once in the scoped query with matching time and resource.
Compare summarized failure counts with raw distinct run IDs and deployment audit records.
Compare raw run phase timestamps to the dashboard and identify the true critical-path stage.

Explanation

Correct Answer

The governing engineering principle is: Dependency-specific monitoring distinguishes storage service failures from application-level symptoms.

Incorrect Answers

KQL filtering is useful when query scope and timestamps match the intended evidence population. The needed step here is to reproduce a controlled storage failure and verify latency, availability and throttling telemetry is accessible.

Log aggregation requires correct grouping keys to avoid duplicate or omitted incidents. The appropriate remedy is to reproduce a controlled storage failure and verify latency, availability and throttling telemetry is accessible.

Operational metrics should distinguish waiting for capacity from work actively being performed. Instead, the response must reproduce a controlled storage failure and verify latency, availability and throttling telemetry is accessible.

Question 915

Another company tracks app errors without correlating them to Azure Storage latency and throttling behavior. Which engineering principle BEST explains the relevant requirement?

KQL filtering is useful when query scope and timestamps match the intended evidence population.
Instrumentation must measure the presence of required checks as well as the outcome of executed checks.
Resource saturation and application symptoms must be correlated before assigning root cause.
Dependency-specific monitoring distinguishes storage service failures from application-level symptoms.

Explanation

Correct Answer

The second engineering situation illustrates the control boundary captured by this principle: Dependency-specific monitoring distinguishes storage service failures from application-level symptoms. The scenario is not resolved by an unrelated pipeline or policy activity.

Incorrect Answers

KQL filtering is useful when query scope and timestamps match the intended evidence population. That principle concerns analyze metrics from instrumentation, whereas this specific incident is governed by: Dependency-specific monitoring distinguishes storage service failures from application-level symptoms.

Instrumentation must measure the presence of required checks as well as the outcome of executed checks. That principle concerns analyze metrics from instrumentation, whereas this specific incident is governed by: Dependency-specific monitoring distinguishes storage service failures from application-level symptoms.

Resource saturation and application symptoms must be correlated before assigning root cause. That principle concerns analyze metrics from instrumentation, whereas this specific incident is governed by: Dependency-specific monitoring distinguishes storage service failures from application-level symptoms.

Question 916

Another company tracks app errors without correlating them to Azure Storage latency and throttling behavior. Which unsupported assumption should the engineer challenge FIRST?

A green workflow summary is not proof every intended security check actually ran.
A low average response time cannot rule out severe latency for a minority of requests.
High CPU usage alone does not prove CPU is the primary cause of every observed request failure.
An application HTTP 500 spike alone cannot isolate whether storage throttling caused the failure.

Explanation

Correct Answer

An application HTTP 500 spike alone cannot isolate whether storage throttling caused the failure. This is the relevant unsupported assumption because dependency-specific monitoring distinguishes storage service failures from application-level symptoms.

Incorrect Answers

This caution is valid in analyze metrics from instrumentation, but it is not the failure assumption illustrated by this scenario. The directly relevant risk is: An application HTTP 500 spike alone cannot isolate whether storage throttling caused the failure.

Question 917

A hub-and-spoke network intermittently drops requests but the team monitors only VM CPU and application exception counts. Which design or operational change MOST directly satisfies the requirement?

Separate queue latency, job execution and external dependency time when analyzing pipeline metrics.
Use a scoped KQL query that filters the relevant table by timestamp, service identifier and error conditions.
Use Azure Monitor Network Insights or suitable network diagnostic telemetry alongside application measurements.
Join on stable scoped identifiers and verify row cardinality and duplicates before interpreting results.

Explanation

Correct Answer

Network observability requires transport and topology signals distinct from server resource health. The most directly responsive design or operational step is: Use Azure Monitor Network Insights or suitable network diagnostic telemetry alongside application measurements. Confirm the result by: Trigger a controlled connectivity issue and confirm relevant network and workload signals can be correlated.

Incorrect Answers

Operational metrics should distinguish waiting for capacity from work actively being performed. The relevant control is to use Azure Monitor Network Insights or suitable network diagnostic telemetry alongside application measurements.

KQL filtering is useful when query scope and timestamps match the intended evidence population. The needed step here is to use Azure Monitor Network Insights or suitable network diagnostic telemetry alongside application measurements.

Log correlation depends on stable identity and the correct join granularity. The appropriate remedy is to use Azure Monitor Network Insights or suitable network diagnostic telemetry alongside application measurements.

Question 918

After the team applies this response: Use Azure Monitor Network Insights or suitable network diagnostic telemetry alongside application measurements. In the situation—A hub-and-spoke network intermittently drops requests but the team monitors only VM CPU and application exception counts. Which evidence BEST demonstrates the response is effective?

Run a known test failure and confirm it appears exactly once in the scoped query with matching time and resource.
Trigger a controlled connectivity issue and confirm relevant network and workload signals can be correlated.
Open the trace waterfall and compare its end-to-end duration with overlapping dependency spans.
Compare raw run phase timestamps to the dashboard and identify the true critical-path stage.

Explanation

Correct Answer

The governing engineering principle is: Network observability requires transport and topology signals distinct from server resource health.

Incorrect Answers

KQL filtering is useful when query scope and timestamps match the intended evidence population. The appropriate comparison is: Trigger a controlled connectivity issue and confirm relevant network and workload signals can be correlated.

Distributed trace timing reflects concurrency and call structure; child durations may overlap. The relevant evidence instead is: Trigger a controlled connectivity issue and confirm relevant network and workload signals can be correlated.

Operational metrics should distinguish waiting for capacity from work actively being performed. The acceptance criterion here is: Trigger a controlled connectivity issue and confirm relevant network and workload signals can be correlated.

Question 919

Another enterprise must distinguish VPN tunnel health and network connectivity from application errors. Which engineering principle BEST explains the relevant requirement?

Instrumentation must measure the presence of required checks as well as the outcome of executed checks.
Tail latency can reveal degraded user experience hidden by a healthy average.
Network observability requires transport and topology signals distinct from server resource health.
Operational metrics should distinguish waiting for capacity from work actively being performed.

Explanation

Correct Answer

The second engineering situation illustrates the control boundary captured by this principle: Network observability requires transport and topology signals distinct from server resource health. The scenario is not resolved by an unrelated pipeline or policy activity.

Incorrect Answers

Instrumentation must measure the presence of required checks as well as the outcome of executed checks. That principle concerns analyze metrics from instrumentation, whereas this specific incident is governed by: Network observability requires transport and topology signals distinct from server resource health.

Tail latency can reveal degraded user experience hidden by a healthy average. That principle concerns analyze metrics from instrumentation, whereas this specific incident is governed by: Network observability requires transport and topology signals distinct from server resource health.

Operational metrics should distinguish waiting for capacity from work actively being performed. That principle concerns analyze metrics from instrumentation, whereas this specific incident is governed by: Network observability requires transport and topology signals distinct from server resource health.

Question 920

Another enterprise must distinguish VPN tunnel health and network connectivity from application errors. Which unsupported assumption should the engineer challenge FIRST?

A query returning no rows is not proof there were no errors when its time filter excludes the incident.
A green workflow summary is not proof every intended security check actually ran.
Dividing errors by only successful requests is not equivalent to errors divided by all attempted requests.
An application process running normally does not rule out a routing or network connectivity failure.

Explanation

Correct Answer

An application process running normally does not rule out a routing or network connectivity failure. This is the relevant unsupported assumption because network observability requires transport and topology signals distinct from server resource health.

Incorrect Answers

This caution is valid in analyze metrics from instrumentation, but it is not the failure assumption illustrated by this scenario. The directly relevant risk is: An application process running normally does not rule out a routing or network connectivity failure.

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Pass Your Microsoft AZ-400 Exam Easy!

Question 910

Explanation

Correct Answer

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Question 911

Explanation

Correct Answer

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Question 912

Explanation

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Question 913

Explanation

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Question 914

Explanation

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Question 915

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Question 916

Explanation

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Question 917

Explanation

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Question 918

Explanation

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Question 919

Explanation

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Question 920

Explanation

Correct Answer

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Designing and Implementing Microsoft DevOps Solutions (AZ-400) Practice Tests