Quick Answer
The answer is to use serverless SQL pools to query files in place and PolyBase to load data from external tables. These two strategies reduce data movement in Azure Synapse Analytics pipelines because serverless SQL pools process queries directly against files in Azure Data Lake Storage using OPENROWSET or CREATE EXTERNAL TABLE syntax, eliminating the need to copy data into a dedicated SQL pool. PolyBase similarly minimizes movement by enabling T-SQL queries to read external data sources without staging. On the DP-203 exam, this tests your understanding of Synapse compute models and pipeline optimization—a common trap is assuming all data must land in a dedicated pool before querying. Remember that serverless SQL is for ad-hoc, on-demand analysis of raw files, while PolyBase is for bulk loading with minimal transfer. Memory tip: "Serverless stays in place, PolyBase loads with grace."
This DP-203 practice question tests your understanding of develop data processing. 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.
Which TWO strategies reduce data movement in Azure Synapse Analytics pipelines? (Choose two.)
Use Data Flow to transform data before loading
Why wrong: Data Flow involves movement.
Use Stored Procedure activity to insert data
Why wrong: Still moves data into the pool.
Use serverless SQL pool to query data in place
Queries without moving data.
Use Copy activity to move data from source to staging
Why wrong: This is data movement.
Use PolyBase to load data from external tables
Loads directly, minimizing movement.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Use serverless SQL pool to query data in place
Option C is correct because serverless SQL pools in Azure Synapse Analytics allow you to query data directly from files in Azure Data Lake Storage or other external sources without moving the data into a dedicated SQL pool. This eliminates data movement entirely by using the compute resources of the serverless pool to process queries in place, leveraging the OPENROWSET or CREATE EXTERNAL TABLE syntax to read data from its original location.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Use Data Flow to transform data before loading
Why it's wrong here
Data Flow involves movement.
Use Stored Procedure activity to insert data
Why it's wrong here
Still moves data into the pool.
Use serverless SQL pool to query data in place
Why this is correct
Queries without moving data.
Related concept
Read the scenario before looking for a memorised answer.
Use Copy activity to move data from source to staging
Why it's wrong here
This is data movement.
Use PolyBase to load data from external tables
Why this is correct
Loads directly, minimizing movement.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates confuse 'reducing data movement' with 'optimizing data movement'—they may think Data Flow or Copy activity with staging reduces movement when in fact they still move data, whereas serverless SQL and PolyBase query data in place without relocation.
Detailed technical explanation
Serverless SQL pools use a distributed query engine that reads data directly from Parquet, CSV, or Delta Lake files via the T-SQL OPENROWSET function with BULK options, leveraging pushdown predicates to filter data at the storage layer. This approach is particularly effective for ad-hoc exploration or ELT patterns where raw data remains in the data lake, avoiding the cost and latency of loading into a dedicated pool. A real-world scenario is querying terabytes of log files for anomaly detection without provisioning dedicated compute or copying data.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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.
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FAQ
Develop data processing — This question tests Develop data processing — Read the scenario before looking for a memorised answer..
The correct answer is: Use serverless SQL pool to query data in place — Option C is correct because serverless SQL pools in Azure Synapse Analytics allow you to query data directly from files in Azure Data Lake Storage or other external sources without moving the data into a dedicated SQL pool. This eliminates data movement entirely by using the compute resources of the serverless pool to process queries in place, leveraging the OPENROWSET or CREATE EXTERNAL TABLE syntax to read data from its original location.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
This DP-203 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DP-203 exam.
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