- A
Increase the batch size in the lookup transformation settings.
Why wrong: Batch size is not a setting in lookup transformation.
- B
Partition the dimension table on the lookup key before reading.
Why wrong: Partitioning is not done in the data flow; it's a table design consideration.
- C
Enable the 'Broadcast' option on the lookup source transformation if the dimension table is less than 100 MB.
Broadcasting avoids shuffle for small dimension tables.
- D
Select only the necessary columns in the lookup source transformation.
Narrowing columns reduces the amount of data processed.
- E
Ensure the dimension table has an index on the columns used for the lookup.
Indexes speed up lookup queries.
Question 574 of 851
DP-203 Broadcast join Practice Question
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. A key principle to apply: broadcast join. 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 building a data processing pipeline in Azure Synapse Analytics that uses a mapping data flow to perform a lookup transformation. The lookup source is a dimension table with 10 million rows. You need to optimize the lookup performance. Which THREE actions should you take?
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
✓
Enable the 'Broadcast' option on the lookup source transformation if the dimension table is less than 100 MB.
To optimize lookup performance in a mapping data flow, the recommended actions are: C. Enable the 'Broadcast' option on the lookup source if the dimension table is less than 100 MB. This avoids shuffling the large fact table across nodes. D. Select only the necessary columns in the lookup source transformation to reduce data transfer and memory usage. E. Ensure the dimension table has an index on the columns used for the lookup to speed up the join operation. Options A and B are not optimal: Increasing batch size (A) does not improve lookup performance; it affects sink writes. Partitioning the dimension table on the lookup key before reading (B) can be helpful, but in a data flow, partitioning is typically applied to the source or within the data flow itself, and it is not one of the top three recommended actions for lookup optimization in Synapse mapping data flows.
Key principle: Broadcast join
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the batch size in the lookup transformation settings.
Why it's wrong here
Batch size is not a setting in lookup transformation.
- ✗
Partition the dimension table on the lookup key before reading.
Why it's wrong here
Partitioning is not done in the data flow; it's a table design consideration.
- ✓
Enable the 'Broadcast' option on the lookup source transformation if the dimension table is less than 100 MB.
Why this is correct
Broadcasting avoids shuffle for small dimension tables.
Related concept
Broadcast join
- ✓
Select only the necessary columns in the lookup source transformation.
Why this is correct
Narrowing columns reduces the amount of data processed.
Related concept
Broadcast join
- ✓
Ensure the dimension table has an index on the columns used for the lookup.
Why this is correct
Indexes speed up lookup queries.
Related concept
Broadcast join
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
Treat this as a scenario question. Identify the problem, the constraint, and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Broadcast join
- Column pruning
- Indexing
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
Broadcast join
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Broadcast join Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Review broadcast join, then practise related DP-203 questions on the same topic to reinforce the concept.
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Develop data processing — study guide chapter
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Develop data processing practice questions
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Microsoft Azure Data Engineer Associate DP-203 study guide
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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 — Broadcast join.
What is the correct answer to this question?
The correct answer is: Enable the 'Broadcast' option on the lookup source transformation if the dimension table is less than 100 MB. — To optimize lookup performance in a mapping data flow, the recommended actions are: C. Enable the 'Broadcast' option on the lookup source if the dimension table is less than 100 MB. This avoids shuffling the large fact table across nodes. D. Select only the necessary columns in the lookup source transformation to reduce data transfer and memory usage. E. Ensure the dimension table has an index on the columns used for the lookup to speed up the join operation. Options A and B are not optimal: Increasing batch size (A) does not improve lookup performance; it affects sink writes. Partitioning the dimension table on the lookup key before reading (B) can be helpful, but in a data flow, partitioning is typically applied to the source or within the data flow itself, and it is not one of the top three recommended actions for lookup optimization in Synapse mapping data flows.
What should I do if I get this DP-203 question wrong?
Review broadcast join, then practise related DP-203 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Broadcast join
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Last reviewed: Jun 21, 2026
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