Question 788 of 846

Develop data processing →easyMultiple ChoiceObjective-mapped

DP-203 Develop data processing Practice Question

This DP-203 practice question tests your understanding of develop data processing. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.

You are a data engineer at a retail company. You need to develop a data processing solution in Azure Synapse Analytics that reads sales transactions from Parquet files stored in Azure Data Lake Storage Gen2, transforms the data by aggregating daily sales per store, and writes the results to a dedicated SQL pool table for reporting. The transformation logic must be reusable and maintained in a source control system. You want to minimize administrative overhead and leverage serverless resources where possible. Which approach should you recommend?

Clue words in this question

Noticing these words before you look at the options changes how you read each choice.

Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.

Question 1easymultiple choice

Full question →

A
Use Azure Data Factory with Mapping Data Flows to read Parquet files, perform aggregations, and write to the dedicated SQL pool.
Why wrong: Mapping Data Flows run on Spark, requiring a Spark cluster and increasing complexity.
B
Use a serverless SQL pool to query the Parquet files via OPENROWSET, then use CETAS to write the aggregated results to the dedicated SQL pool using an external table.
Serverless SQL pool can read Parquet natively, and CETAS allows writing to dedicated SQL pool via external table.
C
Create an Azure Synapse Spark notebook that reads Parquet files, performs aggregation using PySpark, and writes the results to the dedicated SQL pool using the Spark Synapse connector.
Why wrong: Requires provisioning and managing a Spark pool.
D
Use PolyBase in a dedicated SQL pool to create external tables over the Parquet files, then use INSERT...SELECT to load aggregated data into the target table.
Why wrong: Dedicated SQL pool cannot read Parquet directly; external tables require external data sources and file formats supported by PolyBase, but Parquet is not directly supported without using serverless.

Full breakdown with real-world context →

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

✓

Use a serverless SQL pool to query the Parquet files via OPENROWSET, then use CETAS to write the aggregated results to the dedicated SQL pool using an external table.

Option C is correct because a serverless SQL pool can query Parquet files directly, and the CREATE EXTERNAL TABLE AS SELECT (CETAS) statement can transform and store results in a dedicated SQL pool table using PolyBase. This approach uses serverless resources for transformation and avoids managing Spark pools. Option A is wrong because dedicated SQL pool cannot directly read Parquet without PolyBase or external tables. Option B is wrong because Azure Data Factory with Mapping Data Flows runs on Spark clusters, incurring more overhead. Option D is wrong because using a notebook in a Spark pool requires provisioning a Spark pool, which adds administrative overhead.

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.

✗
Use Azure Data Factory with Mapping Data Flows to read Parquet files, perform aggregations, and write to the dedicated SQL pool.
Why it's wrong here
Mapping Data Flows run on Spark, requiring a Spark cluster and increasing complexity.
✓
Use a serverless SQL pool to query the Parquet files via OPENROWSET, then use CETAS to write the aggregated results to the dedicated SQL pool using an external table.
Why this is correct
Serverless SQL pool can read Parquet natively, and CETAS allows writing to dedicated SQL pool via external table.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Create an Azure Synapse Spark notebook that reads Parquet files, performs aggregation using PySpark, and writes the results to the dedicated SQL pool using the Spark Synapse connector.
Why it's wrong here
Requires provisioning and managing a Spark pool.
✗
Use PolyBase in a dedicated SQL pool to create external tables over the Parquet files, then use INSERT...SELECT to load aggregated data into the target table.
Why it's wrong here
Dedicated SQL pool cannot read Parquet directly; external tables require external data sources and file formats supported by PolyBase, but Parquet is not directly supported without using serverless.

Common exam traps

Common exam trap: answer the scenario, not the keyword

Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.

Detailed technical explanation

How to think about this question

This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.

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.
Use explanations to understand the rule behind the answer.

TExam Day Tips

Underline the problem statement mentally.
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.

What to study next

Got this wrong? Here's your next step.

Identify which DP-203 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.

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FAQ

Questions learners often ask

What does this DP-203 question test?

Develop data processing — This question tests Develop data processing — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Use a serverless SQL pool to query the Parquet files via OPENROWSET, then use CETAS to write the aggregated results to the dedicated SQL pool using an external table. — Option C is correct because a serverless SQL pool can query Parquet files directly, and the CREATE EXTERNAL TABLE AS SELECT (CETAS) statement can transform and store results in a dedicated SQL pool table using PolyBase. This approach uses serverless resources for transformation and avoids managing Spark pools. Option A is wrong because dedicated SQL pool cannot directly read Parquet without PolyBase or external tables. Option B is wrong because Azure Data Factory with Mapping Data Flows runs on Spark clusters, incurring more overhead. Option D is wrong because using a notebook in a Spark pool requires provisioning a Spark pool, which adds administrative overhead.

What should I do if I get this DP-203 question wrong?

Are there clue words in this question I should notice?

Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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