DP-900 Describe an analytics workload on Azure Practice Question
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?
⚠ Common exam trap
Watch out — candidates often 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.
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
✓
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.
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 a data loading and external table technology that connects Synapse SQL to data in Azure Storage or Hadoop; it does not execute the batch jobs themselves. Azure Data Factory is an orchestration/ETL service, so replacing PolyBase with ADF only changes how data is moved into the warehouse, not how efficiently the warehouse processes those large-scale batches. Batch job duration is governed by the SQL pool's allocated compute resources (DWUs), query plans, and data distribution, not by the loading tool.
- ✗
Migrate from serverless SQL pool to dedicated SQL pool.
Why it's wrong here
The scenario already states the company uses Azure Synapse Analytics, and simply switching between a serverless SQL pool and a dedicated SQL pool is not an automatic performance fix. Serverless SQL pools are designed for on-demand, ad hoc querying of files in the data lake, while a dedicated SQL pool requires explicit DWU sizing and is optimized for sustained, provisioned workloads; migrating between them does not tune an existing batch workload. Performance bottlenecks such as skewed distributions, outdated statistics, or inefficient table scans would remain after migration, so this change does not directly reduce large-scale job duration.
- ✗
Move the underlying data to Azure Data Lake Storage Gen2.
Why it's wrong here
Moving the underlying data to Azure Data Lake Storage Gen2 (ADLS Gen2) changes where files reside, but the batch jobs are executed in the Synapse SQL pool, and storage location alone does not reduce CPU or memory work. ADLS Gen2 is already the common storage backend for Synapse, so if the data is there now this option changes nothing; if it isn't, the gain is limited to file access speed, not parallel query execution. To speed up large-scale batch processing, you need to increase compute resources (DWUs) or optimize data distribution and file formats—not simply relocate the storage.
- ✓
Increase the data warehouse units (DWUs) for the dedicated SQL pool.
Why this is correct
Increasing the Data Warehouse Units (DWUs) for a dedicated SQL pool is the direct way to scale compute capacity, because DWU bundles CPU, memory, and I/O resources into a single performance measure. A higher DWU level provisions more compute nodes and increases the degree of parallelism for large scans, aggregations, and joins, which reduces batch job duration. This is a supported, elastic operation in Azure Synapse Analytics, although it raises cost and may require a brief scale operation.
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
Key term
Data warehouse
A data warehouse is a central repository that stores large amounts of structured data from multiple sources, optimized for querying and analysis rather than day-to-day transactions.
Key term
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
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