Question 1hardmultiple choice
Full question →hardmultiple choiceObjective-mapped
A company uses Azure Synapse Analytics dedicated SQL pool for large-scale data warehousing. They have a fact table with billions of rows and frequently run queries that filter by a date range and join with a product dimension table. Which table distribution and partitioning strategy will minimize 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
Distractor review
Round-robin distribution with no partitioning
Round-robin distributes data uniformly but does not co-locate related rows, so joins require data movement across distributions. No partitioning means all data must be scanned for date filters.
B
Best answer
Hash-distribute on ProductID with partitioning on Date
Hash-distribution on ProductID co-locates rows with the same ProductID, enabling efficient joins with the product dimension. Partitioning on the Date column enables partition elimination for date range queries, reducing the amount of data scanned.
C
Distractor review
Replicate the fact table on all distributions and partition on ProductID
Replicating a large fact table is impractical due to storage overhead and data duplication. Replication is recommended only for small dimension tables. Partitioning on ProductID does not directly help with date range filtering.
D
Distractor review
Hash-distribute on Date with partitioning on ProductID
Hash-distributing on Date would spread rows of the same product across distributions, causing data movement during joins. Partitioning on ProductID would not effectively prune data for date range queries, as partitions would be large and contain many dates.
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: Hash-distribute on ProductID with partitioning on Date — For large fact tables, hash-distribution on a column that is frequently used in joins (e.g., ProductID) ensures that rows with the same ProductID are co-located on the same distribution, reducing data shuffling during joins. Partitioning on the date column (e.g., SaleDate) allows partition elimination, where queries only scan relevant partitions based on the date range filter. Round-robin distributes data evenly but causes more data movement during joins. Replication is better for small dimension tables, not large fact tables. Hash-distributing on the date column would not help join performance and could lead to uneven data distribution.
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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