DP-900 Describe an analytics workload on Azure Practice Question
A data analytics team stores sales transaction data in Parquet files in Azure Data Lake Storage Gen2. They want to run complex analytical queries that join this data with dimension tables stored in Azure Synapse Analytics dedicated SQL pool. The team prefers not to move or copy the data from the data lake. Which feature should they use to query the data lake data directly?
⚠ Common exam trap
Many exam-takers confuse PolyBase with Azure Data Factory pipelines, thinking that any query across data lake and Synapse requires a data movement pipeline, but PolyBase provides direct T-SQL querying without copying data.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
PolyBase external tables
PolyBase external tables in Azure Synapse Analytics dedicated SQL pool allow you to query data stored in Azure Data Lake Storage Gen2 (ADLS Gen2) directly using T-SQL, without moving or copying the data. This is the correct feature because it enables complex analytical joins between the Parquet files in the data lake and the dimension tables in the dedicated SQL pool, leveraging the external table's ability to read Parquet format natively.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Azure Data Factory pipelines
Why it's wrong here
Azure Data Factory is an orchestration and ETL service that moves or transforms data between locations by launching activities, such as Copy Data, but it does not provide a SQL query engine that can run interactive SELECT statements on Parquet files. ADF can copy the Parquet files into a Synapse staging table or trigger a pipeline, but the act of querying in place would still require a separate compute like PolyBase. Since the team wants to query the files directly, ADF alone is insufficient—it is a data mover, not a query layer.
When this WOULD be correct
A question where the requirement is to schedule and automate the transfer of data from Azure Data Lake Storage Gen2 to Azure Synapse Analytics dedicated SQL pool on a recurring basis, without needing real-time querying.
- ✓
PolyBase external tables
Why this is correct
PolyBase external tables in Azure Synapse dedicated SQL pool use the T-SQL language to create an external table pointing at Parquet files in Azure Data Lake Storage, allowing instant querying without moving the underlying data. PolyBase performs schema inference and can push down filtering operations to the file format, so it is the native mechanism for reading file data directly from Synapse. This matches the requirement of querying stored transaction data in place.
- ✗
Azure Stream Analytics
Why it's wrong here
Azure Stream Analytics is built for real-time event processing over streaming sources like Azure Event Hubs or IoT Hub, using sliding windows to analyze data in motion. It does not support connecting to static Parquet files in ADLS for on-demand batch queries, and its query model is temporal and event-based rather than set-based over stored warehouse data. Therefore, Stream Analytics is fundamentally misaligned with analyzing already persisted sales transactions.
When this WOULD be correct
A question where the requirement is to process real-time streaming data (e.g., IoT sensor data, clickstreams) and perform windowed aggregations or pattern matching before storing results in a sink like Azure Synapse or Power BI.
- ✗
Azure Databricks notebooks
Why it's wrong here
Azure Databricks notebooks run on Apache Spark clusters that are separate from Azure Synapse dedicated SQL pool compute; they do not register as external tables in the pool and cannot be called by a Synapse query. To query Parquet with Databricks, a user would need to spin up a Spark cluster, write Scala/Python/SQL code, and manage a separate environment—this is not a direct query capability from within Synapse. Even though both can read the same files, Databricks lacks the T-SQL endpoint of Synapse, so it does not fulfill the stated requirement.
When this WOULD be correct
When the team needs to perform advanced analytics, machine learning, or data transformation using Apache Spark, and they can read data from Data Lake Storage Gen2 directly into Databricks notebooks for processing.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The DP-900 exam frequently reuses these exact scenarios with slightly different constraints.
✓PolyBase external tablesCorrect answer▾
Why this is correct
PolyBase external tables in Azure Synapse dedicated SQL pool use the T-SQL language to create an external table pointing at Parquet files in Azure Data Lake Storage, allowing instant querying without moving the underlying data. PolyBase performs schema inference and can push down filtering operations to the file format, so it is the native mechanism for reading file data directly from Synapse. This matches the requirement of querying stored transaction data in place.
✗Azure Data Factory pipelinesWrong answer — click to see why▾
Why this is wrong here
Azure Data Factory pipelines are used for data movement and orchestration, not for directly querying data in place. The team wants to query data without moving it, so pipelines would involve copying data, which they want to avoid.
★ When this WOULD be the correct answer
A question where the requirement is to schedule and automate the transfer of data from Azure Data Lake Storage Gen2 to Azure Synapse Analytics dedicated SQL pool on a recurring basis, without needing real-time querying.
Why candidates choose this
Candidates may think Data Factory can query data directly because it can transform data during copy, but its primary purpose is data integration and movement, not ad-hoc querying.
✗Azure Stream AnalyticsWrong answer — click to see why▾
Why this is wrong here
Azure Stream Analytics is designed for real-time stream processing, not for running complex analytical queries on static Parquet files in a data lake. It cannot directly query Parquet files in Azure Data Lake Storage Gen2 for ad-hoc analytical joins.
★ When this WOULD be the correct answer
A question where the requirement is to process real-time streaming data (e.g., IoT sensor data, clickstreams) and perform windowed aggregations or pattern matching before storing results in a sink like Azure Synapse or Power BI.
Why candidates choose this
Candidates may confuse Stream Analytics with a general-purpose query tool because it can use SQL-like syntax, and they might think it can handle batch queries on stored data, not just streaming data.
✗Azure Databricks notebooksWrong answer — click to see why▾
Why this is wrong here
Azure Databricks notebooks are for interactive data analytics and machine learning, not for querying data lake data directly from Synapse SQL pool without moving data.
★ When this WOULD be the correct answer
When the team needs to perform advanced analytics, machine learning, or data transformation using Apache Spark, and they can read data from Data Lake Storage Gen2 directly into Databricks notebooks for processing.
Why candidates choose this
Candidates may think Databricks can query data lake data directly, but the question specifies querying from Synapse SQL pool, which requires PolyBase external tables for direct querying without data movement.
Analysis generated from the official DP-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
Key term
Azure Synapse Analytics
Azure Synapse Analytics is a cloud-based data integration, warehousing, and analytics service that brings together big data and data warehouse capabilities under one platform.
Key term
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
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