DP-203 Practice Question: Secure, monitor, and optimize data storage and data processing
Your team has deployed an Azure Stream Analytics job that writes output to Azure Cosmos DB. You need to monitor the job for data latency and ensure it meets a service-level agreement (SLA) of under 10 seconds from input to output. Which metric should you track in Azure Monitor?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Watermark delay.
Watermark delay is the correct metric to monitor for data latency because it measures the maximum time between an input event being received and the corresponding output being produced. A watermark delay consistently under 10 seconds ensures the SLA is met. Output events (A) track the number of output events, not latency. Runtime errors (B) indicate failures, not latency. Input events (D) track the number of input events, not latency.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Output events.
Why it's wrong here
Output events counts records written to Cosmos DB, revealing throughput but not the elapsed time each record took from input to output. It is tempting because output volume confirms the job is producing results, and would be correct for detecting write failures or throughput drops rather than measuring latency.
- ✗
Runtime errors.
Why it's wrong here
Runtime errors counts failed operations, not the elapsed time between input arrival and output write, so it cannot evidence the 10-second SLA. It is tempting because error metrics are the standard first check for job health, and would be the right choice when diagnosing why a job is failing rather than measuring latency.
- ✓
Watermark delay.
Why this is correct
Watermark delay measures the difference between the latest event processed and the newest event received, expressed in seconds. It directly quantifies end-to-end processing lag, so comparing it against the 10-second SLA threshold tells you whether the job meets the required latency.
- ✗
Input events.
Why it's wrong here
Input events counts records read from the source; it measures throughput, not the end-to-end duration from input to output. It is tempting because input volume is a common capacity signal, and would be correct when sizing streaming units or detecting source backlogs, not for verifying a latency SLA.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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