The answer is PolyBase external tables, which enable you to query Azure Data Lake with PolyBase in Synapse without moving or copying the data. This is correct because PolyBase external tables in Azure Synapse Analytics dedicated SQL pool can natively read Parquet files stored in Azure Data Lake Storage Gen2, allowing T-SQL queries to join that data directly with dimension tables in the pool. On the Microsoft Azure Data Fundamentals DP-900 exam, this tests your understanding of how to integrate data lakes with dedicated SQL pools for complex analytics, and a common trap is confusing PolyBase with Azure Data Factory or COPY INTO—remember that PolyBase is for direct T-SQL querying, not data movement. A helpful memory tip: think of PolyBase as a "polyglot base" that speaks T-SQL to data lakes, letting you query in place without copying.
DP-900 Describe an analytics workload on Azure Practice Question
This DP-900 practice question tests your understanding of describe an analytics workload on azure. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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
Data Factory is used for ETL/ELT pipelines to move data, not for direct querying of external data from within Synapse.
✓
PolyBase external tables
Why this is correct
PolyBase enables Synapse to create external tables that query data in the data lake without moving it.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure Stream Analytics
Why it's wrong here
Stream Analytics is designed for real-time stream processing, not for batch queries on stored data.
✗
Azure Databricks notebooks
Why it's wrong here
Databricks is a separate analytics platform; it does not provide direct query capabilities from within Synapse dedicated SQL pool.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often 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.
Detailed technical explanation
How to think about this question
PolyBase external tables use the T-SQL CREATE EXTERNAL TABLE statement with a LOCATION pointing to the ADLS Gen2 path and a DATA_SOURCE referencing a storage account credential (e.g., via managed identity or storage account key). Under the hood, PolyBase pushes down predicate filtering and projection to the storage layer when possible, reducing data movement, and supports reading Parquet files with schema inference, enabling efficient columnar access for complex joins.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-900 question in full detail.
Describe an analytics workload on Azure — This question tests Describe an analytics workload on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this DP-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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