- A
Increase the number of compute nodes
Why wrong: Adding compute nodes increases parallelism but the overhead of reading many tiny files remains, limiting the improvement.
- B
Use columnstore indexes on external tables
Why wrong: Columnstore indexes apply to tables in the database, not to external files. External tables read data directly from the file format (Parquet) which already uses columnar storage.
- C
Compact small files into larger ones before querying
Compacting small files into larger ones (e.g., 256 MB) reduces file open operations and I/O overhead, significantly improving scan performance in distributed query engines.
- D
Change the partition column to a different date granularity
Why wrong: Changing partition granularity does not address the small file problem; it may even create more or fewer partitions but the files themselves remain small.
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 complex queries against large datasets stored in Parquet files in Azure Data Lake Storage Gen2. They notice that queries scanning entire partitions are slow due to high I/O overhead on the compute nodes. Investigation shows each daily partition contains thousands of small files (under 1 MB each). Which optimization should be implemented first to improve query performance?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"first"Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
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
Compact small files into larger ones before querying
Option C is correct because the high I/O overhead is caused by the thousands of small files per partition. When Synapse compute nodes read many small files, the overhead of opening, reading metadata, and closing each file dominates, even though the total data volume is small. Compacting these small files into fewer, larger files (e.g., 128 MB or more) reduces the number of file operations, improves read throughput, and allows more efficient predicate pushdown and parallelism.
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.
- ✗
Increase the number of compute nodes
Why it's wrong here
Adding compute nodes increases parallelism but the overhead of reading many tiny files remains, limiting the improvement.
- ✗
Use columnstore indexes on external tables
Why it's wrong here
Columnstore indexes apply to tables in the database, not to external files. External tables read data directly from the file format (Parquet) which already uses columnar storage.
- ✓
Compact small files into larger ones before querying
Why this is correct
Compacting small files into larger ones (e.g., 256 MB) reduces file open operations and I/O overhead, significantly improving scan performance in distributed query engines.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Change the partition column to a different date granularity
Why it's wrong here
Changing partition granularity does not address the small file problem; it may even create more or fewer partitions but the files themselves remain small.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse scaling out compute nodes (Option A) with solving a data layout problem, or mistakenly think columnstore indexes (Option B) apply to external tables, when in fact the issue is purely about file size and count in the storage layer.
Detailed technical explanation
How to think about this question
Under the hood, Azure Synapse uses a distributed query engine that relies on file-level metadata operations; each small file triggers a separate read request to Azure Data Lake Storage, incurring latency from REST API calls and metadata lookups. Compacting files to a target size of 128 MB or more aligns with the optimal block size for Parquet and allows the engine to use sequential I/O and column pruning more effectively. In real-world scenarios, a pipeline using Azure Data Factory or a Spark job can compact small files into larger ones as a routine maintenance step, dramatically reducing query execution times.
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.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this DP-900 question test?
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: Compact small files into larger ones before querying — Option C is correct because the high I/O overhead is caused by the thousands of small files per partition. When Synapse compute nodes read many small files, the overhead of opening, reading metadata, and closing each file dominates, even though the total data volume is small. Compacting these small files into fewer, larger files (e.g., 128 MB or more) reduces the number of file operations, improves read throughput, and allows more efficient predicate pushdown and parallelism.
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.
Are there clue words in this question I should notice?
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
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