Question 1hardmultiple choice
Full question →hardmultiple choiceObjective-mapped
A company uses Azure Synapse Analytics dedicated SQL pool for a large data warehouse. The fact table contains billions of rows and is hash-distributed on ProductID. Frequent queries join this fact table with a small Store dimension table (10,000 rows) and a medium-sized Product dimension table (500,000 rows). The queries aggregate sales by store and product for recent months, but run slowly due to data movement during joins. Which design change will most reduce data movement and 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
Replicate the Store dimension table
Correct. Replicating a small dimension table (less than 1 GB) copies it to all distributions, eliminating data movement when joining with the fact table. This directly addresses the performance issue.
B
Distractor review
Change the distribution of the fact table to round-robin
Incorrect. Round-robin distribution distributes data evenly but does not colocate data for joins, likely increasing data movement and slowing queries further.
C
Distractor review
Change the distribution key of the fact table to StoreID
Incorrect. This would require rebuilding the entire table and may not help because the fact table joins with both Store and Product dimensions. Data movement may still occur for the Product join.
D
Distractor review
Add a nonclustered index on the StoreID column in the fact table
Incorrect. Indexes in Synapse dedicated SQL pool are used differently (columnstore is default). A nonclustered index on a hash-distributed table does not reduce data movement, which is the primary cause of slow joins.
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: Replicate the Store dimension table — Replicating the small Store dimension table across all distributions eliminates the need to shuffle data when joining with the fact table. The Product dimension is medium-sized (500k rows) and might still cause some data movement, but the biggest gain comes from replicating the very small Store table. Changing distribution of the fact table would be a massive operation with limited benefit. Using round-robin would worsen performance. Replication is the recommended strategy for small dimension tables in Synapse dedicated SQL pool.
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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