- 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.
Quick Answer
The answer is to ensure the dimension table has an index on the columns used for the lookup, enable broadcasting of the lookup table, and narrow the columns selected from the lookup source. These three actions directly optimize lookup transformation mapping data flow performance by reducing data shuffling and transfer overhead. Indexing the lookup columns accelerates row matching within the dimension table, while broadcasting pushes a copy of the table to each worker node, avoiding expensive network shuffles. Narrowing columns minimizes the data volume moved during the lookup operation. On the DP-203 exam, this scenario tests your understanding of how mapping data flows execute distributed joins—a common trap is confusing sink batch size or table partitioning with in-flow optimization. Remember the mnemonic “BIN” for Broadcast, Index, Narrow to recall the three correct actions.
DP-203 Develop data processing 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. 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 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.
Options A, B, and E are correct. Option A is correct because broadcasting the lookup table (if small enough) avoids shuffling. Option B is correct because indexing the lookup columns speeds up the lookup. Option C is wrong because increasing batch size in a lookup does not directly improve performance; it's for sink. Option D is wrong because partitioning the lookup table on the join key can help, but the question is about the data flow, not the table design. Option E is correct because narrowing the columns reduces data transfer.
Key principle: Count usable hosts — not total addresses — and remember that the network and broadcast addresses are not available to hosts in standard IPv4 subnets.
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
CIDR notation defines the prefix length.
- ✓
Select only the necessary columns in the lookup source transformation.
Why this is correct
Narrowing columns reduces the amount of data processed.
Related concept
CIDR notation defines the prefix length.
- ✓
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
CIDR notation defines the prefix length.
Common exam traps
Common exam trap: usable hosts are not the same as total addresses
Subnetting questions often tempt you into counting all addresses. In normal IPv4 subnets, the network and broadcast addresses are not usable host addresses.
Detailed technical explanation
How to think about this question
Subnetting questions test whether you can identify the network, broadcast address, usable range, mask and correct subnet. Slow down enough to calculate the block size correctly.
KKey Concepts to Remember
- CIDR notation defines the prefix length.
- Block size helps identify subnet boundaries.
- Network and broadcast addresses are not usable hosts in normal IPv4 subnets.
- The required host count determines the smallest suitable subnet.
TExam Day Tips
- Write the block size before choosing the subnet.
- Check whether the question asks for hosts, subnets or a specific address range.
- Do not confuse /24, /25, /26 and /27 host counts.
Key takeaway
Count usable hosts — not total addresses — and remember that the network and broadcast addresses are not available to hosts in standard IPv4 subnets.
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. Count usable hosts — not total addresses — and remember that the network and broadcast addresses are not available to hosts in standard IPv4 subnets. 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 block sizes, usable host formulas (2^n − 2), and how to find network and broadcast addresses for /24 through /30. Then practise related DP-203 subnetting questions on CIDR, address ranges, and subnet selection.
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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 — CIDR notation defines the prefix length..
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. — Options A, B, and E are correct. Option A is correct because broadcasting the lookup table (if small enough) avoids shuffling. Option B is correct because indexing the lookup columns speeds up the lookup. Option C is wrong because increasing batch size in a lookup does not directly improve performance; it's for sink. Option D is wrong because partitioning the lookup table on the join key can help, but the question is about the data flow, not the table design. Option E is correct because narrowing the columns reduces data transfer.
What should I do if I get this DP-203 question wrong?
Review block sizes, usable host formulas (2^n − 2), and how to find network and broadcast addresses for /24 through /30. Then practise related DP-203 subnetting questions on CIDR, address ranges, and subnet selection.
What is the key concept behind this question?
CIDR notation defines the prefix length.
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Last reviewed: Jun 21, 2026
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