Question 831 of 846

Develop data processing →mediumMultiple ChoiceObjective-mapped

Quick Answer

The correct approach is to use the serverless SQL pool to create external tables on Cosmos DB and ADLS Gen2, then use CREATE EXTERNAL TABLE AS SELECT (CETAS) to write the combined data to a table in the dedicated SQL pool. This works because the serverless SQL pool can query Cosmos DB via its analytical store and ADLS Gen2 via OPENROWSET, allowing you to join the video metadata and user interaction data using T-SQL, then persist the results directly into the dedicated SQL pool with CETAS. On the DP-203 exam, this scenario tests your understanding of when to use serverless versus dedicated pools for data transformation, with a common trap being to choose PolyBase (which cannot query Cosmos DB directly) or Azure Data Factory (which would require extra orchestration). Remember the key distinction: serverless SQL pool handles the query and transformation, while CETAS handles the write to the dedicated pool. Memory tip: CETAS stands for "Create External Table As Select"—think of it as a one-step "query and land" operation that bridges serverless querying with dedicated storage.

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 designing a data processing solution for a media company that uses Azure Synapse Analytics. The solution must process video metadata stored in Azure Cosmos DB and combine it with user interaction data from Azure Data Lake Storage Gen2. The combined data must be stored in a dedicated SQL pool for reporting. The data volume is moderate, and the processing should be done using T-SQL. Which approach should you use?

Question 1mediummultiple choice

Full question →

A
Use Azure Data Factory with Copy activities to load data from Cosmos DB and ADLS Gen2 into the dedicated SQL pool, then use a stored procedure to merge.
Why wrong: Adds extra data movement.
B
Use Azure Databricks to read from Cosmos DB and ADLS Gen2, transform with Spark SQL, and write to the dedicated SQL pool using JDBC.
Why wrong: Not T-SQL based; uses Spark.
C
Use the serverless SQL pool to create external tables on Cosmos DB and ADLS Gen2, then use CREATE EXTERNAL TABLE AS SELECT (CETAS) to write the combined data to a table in the dedicated SQL pool.
Leverages T-SQL and serverless pool for in-place querying.
D
Use PolyBase to create external tables on Cosmos DB and ADLS Gen2, then use INSERT...SELECT to load into dedicated SQL pool.
Why wrong: PolyBase does not support Cosmos DB as an external data source.

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 the serverless SQL pool to create external tables on Cosmos DB and ADLS Gen2, then use CREATE EXTERNAL TABLE AS SELECT (CETAS) to write the combined data to a table in the dedicated SQL pool.

Option A is correct because Azure Synapse Analytics serverless SQL pool can query Cosmos DB via Azure Cosmos DB analytical store and ADLS Gen2 via OPENROWSET, and then you can use CETAS to write results to the dedicated SQL pool. Option B is wrong because Azure Data Factory would require copy activities and may not be as efficient. Option C is wrong because Azure Databricks uses Spark, not T-SQL. Option D is wrong because PolyBase cannot directly query Cosmos DB.

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 Copy activities to load data from Cosmos DB and ADLS Gen2 into the dedicated SQL pool, then use a stored procedure to merge.
Why it's wrong here
Adds extra data movement.
✗
Use Azure Databricks to read from Cosmos DB and ADLS Gen2, transform with Spark SQL, and write to the dedicated SQL pool using JDBC.
Why it's wrong here
Not T-SQL based; uses Spark.
✓
Use the serverless SQL pool to create external tables on Cosmos DB and ADLS Gen2, then use CREATE EXTERNAL TABLE AS SELECT (CETAS) to write the combined data to a table in the dedicated SQL pool.
Why this is correct
Leverages T-SQL and serverless pool for in-place querying.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use PolyBase to create external tables on Cosmos DB and ADLS Gen2, then use INSERT...SELECT to load into dedicated SQL pool.
Why it's wrong here
PolyBase does not support Cosmos DB as an external data source.

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 the serverless SQL pool to create external tables on Cosmos DB and ADLS Gen2, then use CREATE EXTERNAL TABLE AS SELECT (CETAS) to write the combined data to a table in the dedicated SQL pool. — Option A is correct because Azure Synapse Analytics serverless SQL pool can query Cosmos DB via Azure Cosmos DB analytical store and ADLS Gen2 via OPENROWSET, and then you can use CETAS to write results to the dedicated SQL pool. Option B is wrong because Azure Data Factory would require copy activities and may not be as efficient. Option C is wrong because Azure Databricks uses Spark, not T-SQL. Option D is wrong because PolyBase cannot directly query Cosmos DB.

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

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

About these practice questions

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Same concept, more angles

1 more ways this is tested on DP-203

These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.

Variation 1. 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?

easy

A.Use Azure Data Factory with Mapping Data Flows to read Parquet files, perform aggregations, and write to the dedicated SQL pool.
✓ 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.
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.
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 B: 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.

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