Quick Answer
The answer is Data Integration Unit (DIU) Consumption, Pipeline Succeeded Rerun Count, and Pipeline Failed Rerun Count. DIU Consumption is the primary metric for measuring the compute resources consumed during an activity, where a sustained high value relative to your pipeline’s baseline directly indicates a bottleneck in data movement or transformation throughput. Pipeline Succeeded Rerun Count and Pipeline Failed Rerun Count are critical because a high number of reruns—especially successful ones after failures—signals that the pipeline is repeatedly retrying due to throttling, resource contention, or transient errors, which degrades overall performance. On the DP-203 exam, this tests your ability to correlate Azure Monitor metrics with specific Data Factory performance issues; a common trap is focusing only on DIU Consumption while ignoring rerun counts, which reveal retry-induced latency. Remember the mnemonic “DIU for load, Reruns for pain”—DIU tells you how hard the system is working, while rerun metrics tell you where it’s failing and retrying, pinpointing the bottleneck.
This DP-203 practice question tests your understanding of monitor and optimize data storage and processing. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which THREE metrics from Azure Monitor should be used to diagnose performance bottlenecks in an Azure Data Factory pipeline?
Pipeline Succeeded Rerun Count
High rerun count indicates failures and potential bottlenecks.
Blob Capacity
Why wrong: Storage metric, not pipeline performance.
Activity Duration
Directly measures execution time of each activity.
SQL Pool DWU Used
Why wrong: Metric for Synapse SQL pool, not ADF.
Data Integration Unit (DIU) Consumption
Indicates if copy activity is resource constrained.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Pipeline Succeeded Rerun Count
Pipeline Succeeded Rerun Count (A) is correct because a high number of reruns indicates that the pipeline is repeatedly failing and retrying, which directly points to a performance bottleneck such as resource contention or throttling. This metric helps identify pipelines that are not completing successfully on the first attempt, signaling underlying issues that degrade throughput.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Pipeline Succeeded Rerun Count
Why this is correct
High rerun count indicates failures and potential bottlenecks.
Related concept
Read the scenario before looking for a memorised answer.
Blob Capacity
Why it's wrong here
Storage metric, not pipeline performance.
Activity Duration
Why this is correct
Directly measures execution time of each activity.
Related concept
Read the scenario before looking for a memorised answer.
SQL Pool DWU Used
Why it's wrong here
Metric for Synapse SQL pool, not ADF.
Data Integration Unit (DIU) Consumption
Why this is correct
Indicates if copy activity is resource constrained.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates often confuse storage-level metrics (like Blob Capacity) or data warehouse metrics (like DWU Used) with pipeline-specific performance indicators, but the question explicitly asks for metrics that diagnose bottlenecks in the pipeline execution itself, not in downstream storage or compute services.
Detailed technical explanation
Activity Duration (C) measures the time taken for each individual activity within a pipeline, allowing you to pinpoint which specific step (e.g., Copy, Lookup, Stored Procedure) is causing the delay. Data Integration Unit (DIU) Consumption (E) reflects the compute resources allocated to a Copy activity; if DIU consumption is consistently low while data volumes are high, it suggests the source or sink is throttling the operation, limiting parallelism. Under the hood, Azure Monitor collects these metrics via the Azure Data Factory diagnostic settings, emitting them as platform metrics with a 1-minute granularity.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
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FAQ
Monitor and optimize data storage and processing — This question tests Monitor and optimize data storage and processing — Read the scenario before looking for a memorised answer..
The correct answer is: Pipeline Succeeded Rerun Count — Pipeline Succeeded Rerun Count (A) is correct because a high number of reruns indicates that the pipeline is repeatedly failing and retrying, which directly points to a performance bottleneck such as resource contention or throttling. This metric helps identify pipelines that are not completing successfully on the first attempt, signaling underlying issues that degrade throughput.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
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