Question 1hardmultiple choice
Full question →hardmultiple choiceObjective-mapped
A data engineering team is building a batch analytics pipeline. Raw clickstream data is stored as Parquet files in Azure Data Lake Storage Gen2. The team needs to transform the data using Apache Spark (Python code) and then load the results into Azure Synapse Analytics for high-performance reporting. They want to use a serverless compute option for Spark to avoid managing clusters. Which combination of Azure services should they use for the transformation and loading?
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
Use Azure Databricks with a serverless cluster for transformations and load into Azure SQL Database.
Azure Databricks can run Spark transformations but the target in the scenario is Azure Synapse Analytics, not Azure SQL Database. Synapse offers better integration for large-scale analytics.
B
Best answer
Use Azure Synapse Analytics serverless Spark pools for transformations and load into the Synapse dedicated SQL pool.
Synapse Analytics provides serverless Spark pools that automatically scale and can read from ADLS Gen2. The transformed data can be loaded into the dedicated SQL pool for high-performance queries, all within a single integrated service.
C
Distractor review
Use Azure Data Factory with a Spark activity to run transformations and load into Azure Synapse Analytics.
Azure Data Factory can orchestrate pipelines and run Spark activities on HDInsight or Databricks, but it does not provide a serverless Spark compute itself. It would require managing a separate Spark cluster.
D
Distractor review
Use Azure HDInsight with Apache Spark for transformations and load into Azure Blob Storage.
HDInsight requires managing a cluster (non-serverless) and the target should be Azure Synapse Analytics, not Blob Storage. This option does not meet the serverless requirement.
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: Use Azure Synapse Analytics serverless Spark pools for transformations and load into the Synapse dedicated SQL pool. — Azure Synapse Analytics provides a serverless Apache Spark pool that can run Spark code on demand without managing a cluster. The transformed data can be loaded into a dedicated SQL pool (or serverless SQL pool) for reporting. Azure Data Factory cannot run Spark transformations natively; it can orchestrate pipelines but not execute Spark code. Azure Databricks can run Spark but is not serverless by default (though it has serverless options, the question specifically asks for a service that is serverless and integrates with Synapse). The best answer is to use Synapse serverless Spark pools for transformation and then load into Synapse dedicated SQL pool. Alternatively, you could use Databricks with serverless compute, but the question implies integration with Azure Synapse Analytics for loading. The most direct answer: Use Azure Synapse Analytics serverless Spark pools for transformation and Synapse dedicated SQL pool for the target.
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.
Discussion
Loading comments…
Sign in to join the discussion.