The answer is to use a broadcast join with the static DataFrame loaded from Delta Lake. This is correct because Spark Structured Streaming broadcast join with a static table avoids expensive shuffles by copying the small reference dataset to every executor node, enabling efficient stream-static joins without network bottlenecks. On the Microsoft Azure Data Engineer Associate DP-203 exam, this scenario tests your understanding of optimizing streaming workloads in Azure Databricks, often appearing as a trap where candidates mistakenly choose a stream-stream join or a complex foreachBatch approach. The key insight is that the reference table is static and small enough to fit in memory, making broadcast the natural choice for minimal latency. Remember the memory tip: "Static and small? Broadcast it all."
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.
Your team is developing a data processing solution that uses Azure Databricks to transform streaming data from Azure Event Hubs. The transformation includes joining the stream with a static reference table stored in Azure Data Lake Storage Gen2. You need to implement the join efficiently. Which approach should you use?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use a broadcast join with the static DataFrame loaded from Delta Lake
Option A is correct because streaming-static joins in Spark Structured Streaming are optimized when the static data is broadcast to all nodes, avoiding shuffles. Option B is wrong because the reference table is static; streaming-static join is appropriate. Option C is wrong because joining two streams is not needed. Option D is wrong because foreachBatch with a batch join adds complexity.
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.
✗
Use a watermark on both sides and perform a stream-stream join
Why it's wrong here
Only one side is a stream.
✓
Use a broadcast join with the static DataFrame loaded from Delta Lake
Why this is correct
Broadcast join avoids shuffling and is efficient for streaming-static joins.
Use foreachBatch to micro-batch the stream and perform a batch join
Why it's wrong here
Adds complexity without benefit over broadcast join.
✗
Use a stream-stream join by converting the static table to a stream
Why it's wrong here
Unnecessary and inefficient.
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 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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-203 question in full detail.
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.
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: Use a broadcast join with the static DataFrame loaded from Delta Lake — Option A is correct because streaming-static joins in Spark Structured Streaming are optimized when the static data is broadcast to all nodes, avoiding shuffles. Option B is wrong because the reference table is static; streaming-static join is appropriate. Option C is wrong because joining two streams is not needed. Option D is wrong because foreachBatch with a batch join adds complexity.
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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Question Discussion
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