Question 1hardmultiple choice
Full question →hardmultiple choiceObjective-mapped
A financial institution runs complex analytical queries on trading data stored in Parquet files in Azure Data Lake Storage Gen2. The data is partitioned by date and contains billions of rows. Analysts frequently query within a specific date range, and the queries must return results in under 5 seconds. The current solution uses Azure Synapse Serverless SQL pool, but queries are slow because the serverless pool scans all partitions even when the WHERE clause filters on the date column. Which optimization should be implemented 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
Best answer
Switch to Azure Synapse dedicated SQL pool with proper table partitioning
Correct. Dedicated SQL pools support partition elimination, allowing queries to skip scanning partitions that don't match the filter, dramatically improving performance.
B
Distractor review
Create a clustered columnstore index on the external table
Incorrect. Clustered columnstore indexes cannot be created on external tables in Synapse Serverless; they apply only to tables in a dedicated pool.
C
Distractor review
Convert the Parquet files to CSV format
Incorrect. CSV files lack columnar compression and encoding, making queries slower than Parquet, especially with billions of rows.
D
Distractor review
Use Azure Databricks with Delta Lake for querying
Incorrect. While Databricks can improve performance with Delta Lake, this changes the query tool and does not address the partition elimination issue in Synapse Serverless as directly.
Common exam trap
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Technical deep dive
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
- Static NAT maps one inside address to one outside address.
- PAT allows many inside hosts to share one public address using ports.
- Inside local and inside global describe the private and translated addresses.
- NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
- Identify inside and outside interfaces first.
- Check whether the scenario needs static NAT, dynamic NAT or PAT.
- Do not confuse NAT matching ACLs with normal packet-filtering intent.
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?
Static NAT maps one inside address to one outside address.
What is the correct answer to this question?
The correct answer is: Switch to Azure Synapse dedicated SQL pool with proper table partitioning — Azure Synapse Serverless SQL pool does not natively support partition elimination on external tables; it often scans all partitions. Switching to a dedicated SQL pool enables partition pruning because dedicated pools have a distributed architecture that allows skipping irrelevant partitions based on the filter. Creating a clustered columnstore index will not help because serverless pools work with external data and cannot create indexes on external tables. Converting to CSV would make queries slower (no columnar benefits). Using Azure Databricks with Delta Lake could be an alternative, but the question asks for direct optimization for T-SQL queries on Synapse. The most straightforward optimization is to use a dedicated SQL pool which supports partition elimination.
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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