Question 1mediummultiple choice
Full question →mediummultiple choiceObjective-mapped
A financial services company stores petabytes of transaction data in Parquet format in Azure Data Lake Storage Gen2. Data analysts need to run complex SQL queries that join multiple large tables and aggregate billions of rows, with results expected within seconds. The company wants to use a massively parallel processing (MPP) engine that supports T-SQL and can be paused to reduce costs during off-hours. They also need native integration with Azure Data Factory and Power BI. Which Azure service should they use?
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
Azure Synapse Analytics
Synapse Analytics provides MPP architecture, T-SQL support, pause capability, and tight integration with Azure Data Factory and Power BI, making it ideal for large-scale data warehousing.
B
Distractor review
Azure HDInsight
HDInsight is a managed Hadoop/Spark cluster that uses Hive or Spark SQL, not native T-SQL. It does not have the same pause capability and is more complex to manage for SQL-oriented analysts.
C
Distractor review
Azure Databricks
Databricks is a Spark-based analytics platform. While it can handle large datasets, it does not provide a native T-SQL interface or the ability to pause compute resources like Synapse Analytics.
D
Distractor review
Azure SQL Database
Azure SQL Database is an OLTP database, not built for petabyte-scale MPP queries. It cannot pause compute and would not provide the performance needed for massive aggregations.
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: Azure Synapse Analytics — Azure Synapse Analytics (formerly SQL Data Warehouse) is a cloud-based MPP engine designed for petabyte-scale analytics. It supports T-SQL queries, can pause compute to save costs, and integrates natively with Azure Data Factory for orchestration and Power BI for visualization. Azure HDInsight is a Spark/Hadoop cluster, not a T-SQL MPP engine. Azure Databricks is Apache Spark-based and does not natively support T-SQL or pause compute in the same way. Azure SQL Database is not designed for petabyte-scale data warehousing and does not support MPP architecture.
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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