The correct action is to increase the data warehouse units (DWUs) for the dedicated SQL pool. Scaling DWUs directly boosts the compute resources—CPU, memory, and I/O bandwidth—available to the pool, enabling greater parallelism for large-scale batch processing jobs. This reduces execution time because the workload is distributed across more compute nodes, allowing the 6-hour job to finish within the 4-hour window. On the DP-900 exam, this scenario tests your understanding of scaling compute versus scaling storage; a common trap is confusing DWU scaling with increasing storage size or pausing the pool. Remember that for batch job performance improvement, you scale compute, not storage. A useful memory tip: “DWU = Do Work Units” — more DWUs mean more workers finishing the job faster.
DP-900 Describe an analytics workload on Azure Practice Question
This DP-900 practice question tests your understanding of describe an analytics workload on azure. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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.
A company uses Azure Synapse Analytics to run large-scale batch processing jobs every night. The jobs currently take 6 hours to complete, but the business requires completion within 4 hours. Which action should the company take to improve job performance?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the data warehouse units (DWUs) for the dedicated SQL pool.
Increasing the data warehouse units (DWUs) for the dedicated SQL pool scales the compute resources (CPU, memory, and I/O bandwidth) available to the Synapse SQL pool. This directly reduces the execution time of batch processing jobs by allowing more parallel processing, enabling the 6-hour job to complete within the required 4-hour window.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Replace PolyBase with Azure Data Factory for data movement.
Why it's wrong here
PolyBase is used for data loading; changing the loading tool does not accelerate batch job execution.
✗
Migrate from serverless SQL pool to dedicated SQL pool.
Why it's wrong here
The company already uses Synapse Analytics; switching pool types does not inherently improve performance for existing jobs.
✗
Move the underlying data to Azure Data Lake Storage Gen2.
Why it's wrong here
Storage tier change does not directly reduce compute time for batch processing.
✓
Increase the data warehouse units (DWUs) for the dedicated SQL pool.
Why this is correct
Scaling up DWUs allocates more compute resources, reducing job duration.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse storage optimization (e.g., moving to ADLS Gen2) with compute scaling, or assume that changing data movement tools (PolyBase vs. Data Factory) will fix performance, when the core issue is insufficient compute capacity for the batch workload.
Detailed technical explanation
How to think about this question
Dedicated SQL pool in Azure Synapse uses a Massively Parallel Processing (MPP) architecture where DWUs define the compute nodes and their resources. Scaling DWUs adjusts the number of nodes or the resources per node, directly impacting the degree of parallelism (DOP) for query execution. In practice, doubling DWUs often yields near-linear performance improvements for large-scale batch jobs, though the exact gain depends on the workload's parallelism and data distribution.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-900 question in full detail.
Describe an analytics workload on Azure — This question tests Describe an analytics workload on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the data warehouse units (DWUs) for the dedicated SQL pool. — Increasing the data warehouse units (DWUs) for the dedicated SQL pool scales the compute resources (CPU, memory, and I/O bandwidth) available to the Synapse SQL pool. This directly reduces the execution time of batch processing jobs by allowing more parallel processing, enabling the 6-hour job to complete within the required 4-hour window.
What should I do if I get this DP-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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