Question 1hardmultiple choice
Full question →hardmultiple choiceObjective-mapped
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?
Answer choices
Why each option matters
Good practice is not just finding the correct option. The wrong answers often show the exact trap the exam wants you to fall into.
A
Distractor review
Increase the number of compute nodes
Adding compute nodes increases parallelism but the overhead of reading many tiny files remains, limiting the improvement.
B
Distractor review
Use columnstore indexes on external tables
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
Best answer
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
Distractor review
Change the partition column to a different date granularity
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 trap
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Technical deep dive
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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.
Related practice questions
Related DP-900 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
More questions from this exam
Keep practising from the same exam bank, or move into a focused topic page if this question exposed a weak area.
Question 1
A data engineer needs to process streaming data from IoT devices and store the results in Azure Data Lake Storage for long-term analytics. The data must be processed in near real-time to detect anomalies and trigger alerts. Which Azure service should the engineer use for stream processing?
Question 2
A data engineer needs to query data stored in CSV files in Azure Data Lake Storage Gen2 using T-SQL in Azure Synapse Analytics, without loading the data into the database. Which feature should they use?
Question 3
A data engineer needs to process raw clickstream data from multiple websites that is stored in Azure Blob Storage as JSON files. The processing must run automatically every hour, transform the data into a structured format for reporting, and handle schema changes in the source data without manual intervention. Which Azure service should be used?
Question 4
A data engineer is designing a data lake architecture in Azure. They plan to first ingest raw data from various sources into a landing zone in Azure Data Lake Storage Gen2. Then they will clean, validate, and deduplicate that data in a second zone. Finally, they will create aggregated, business-ready datasets in a third zone for analysts. This layered approach is known as which architecture?
Question 5
A data engineer needs to transform large datasets stored in Azure Data Lake Storage Gen2 using Python and Apache Spark. They want a serverless compute option that automatically scales and requires no cluster management. Which Azure service should they use?
Question 6
A company collects customer feedback forms. Each form contains always-present fields like CustomerID and SubmissionDate, but also a free-text Comments field and optional fields like Rating or ProductCategory that vary between forms. How should this data be classified?
FAQ
Questions learners often ask
What does this DP-900 question test?
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 — The overhead of opening and reading many small files significantly degrades query performance in Azure Synapse Analytics (and other big data engines). Compacting (merging) small files into larger ones (ideally 256 MB or more) reduces the number of file operations and improves throughput. Increasing compute nodes would add parallel processing but does not address the file size problem. Columnstore indexes are used on database tables, not on external files - external tables use the file format as is. Changing the partition column might help if data skew exists, but the core issue is the small file problem, not partition granularity.
What should I do if I get this DP-900 question wrong?
Then try more questions from the same exam bank and focus on understanding why the wrong options are tempting.
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