The answer is to broadcast the smaller table with 1 million rows to all worker nodes. This optimization minimizes shuffle by enabling a map-side join, where the small dataset is replicated to every executor, allowing each partition of the large 10-billion-row table to join locally without costly data movement across the network. With Photon runtime, broadcast joins are particularly efficient because they leverage vectorized execution and compressed data transfer, making the 1-million-row table well within the default spark.sql.autoBroadcastJoinThreshold of 10 MB. On the DP-203 exam, this scenario tests your understanding of shuffle minimization techniques in Azure Databricks, often appearing as a performance tuning question where the trap is to suggest repartitioning or bucketing instead. Remember the 10:1 ratio rule of thumb: if one table is at least an order of magnitude smaller, broadcast it. Memory tip: "Small goes everywhere, big stays put."
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 designing a batch processing solution using Azure Databricks. The data source is a large Parquet dataset stored in Azure Data Lake Storage Gen2 (ADLS Gen2). The processing requires joining two datasets: one with 10 billion rows and another with 1 million rows. The cluster uses Photon runtime. Which optimization should you apply to minimize shuffle?
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.
Broadcast the smaller table (1 million rows) to all worker nodes.
Broadcasting the smaller table avoids shuffling the large table, significantly reducing data movement.
B
Increase the cluster size to reduce shuffle overhead.
Why wrong: Increasing cluster size may improve parallelism but does not reduce the amount of data shuffled.
C
Create bucketed tables on the join key for both datasets.
Why wrong: Bucketing can reduce shuffle if both tables are bucketed on the same key, but it requires pre-processing; broadcast join is simpler and more efficient.
D
Use Delta Lake and optimize file layout with OPTIMIZE command.
Why wrong: OPTIMIZE improves file layout but does not directly reduce shuffle during joins.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Broadcast the smaller table (1 million rows) to all worker nodes.
Broadcasting the smaller table (1 million rows) to all worker nodes is the correct optimization because it eliminates the need for a full shuffle during the join. With Photon runtime, broadcast joins are highly efficient as they replicate the small table to each executor, allowing map-side joins that avoid costly data movement across the network. Given the 10:1 row ratio, the 1-million-row table is well within the default broadcast threshold (10 MB compressed, configurable via spark.sql.autoBroadcastJoinThreshold), making this the most effective shuffle-minimization technique.
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.
✓
Broadcast the smaller table (1 million rows) to all worker nodes.
Why this is correct
Broadcasting the smaller table avoids shuffling the large table, significantly reducing data movement.
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.
✗
Increase the cluster size to reduce shuffle overhead.
Why it's wrong here
Increasing cluster size may improve parallelism but does not reduce the amount of data shuffled.
✗
Create bucketed tables on the join key for both datasets.
Why it's wrong here
Bucketing can reduce shuffle if both tables are bucketed on the same key, but it requires pre-processing; broadcast join is simpler and more efficient.
✗
Use Delta Lake and optimize file layout with OPTIMIZE command.
Why it's wrong here
OPTIMIZE improves file layout but does not directly reduce shuffle during joins.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume increasing cluster size (Option B) is a universal performance fix, but the DP-203 exam specifically tests the understanding that shuffle reduction techniques like broadcast joins are more impactful than simply adding more nodes, especially when one dataset is small enough to fit in executor memory.
Detailed technical explanation
How to think about this question
Under the hood, broadcast join in Spark works by collecting the small table to the driver, serializing it, and distributing it to all executors via TorrentBroadcast (which uses BitTorrent-like peer-to-peer replication). With Photon runtime, vectorized execution and native code generation further accelerate the broadcast hash join by processing data in cache-friendly batches. A real-world scenario where this matters is when joining a massive fact table (e.g., 10 billion clickstream events) with a small dimension table (e.g., 1 million user profiles); broadcasting the dimension table avoids a full shuffle of the fact table, which could otherwise overwhelm network bandwidth and cause out-of-disk errors.
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.
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
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-203 question in full detail.
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: Broadcast the smaller table (1 million rows) to all worker nodes. — Broadcasting the smaller table (1 million rows) to all worker nodes is the correct optimization because it eliminates the need for a full shuffle during the join. With Photon runtime, broadcast joins are highly efficient as they replicate the small table to each executor, allowing map-side joins that avoid costly data movement across the network. Given the 10:1 row ratio, the 1-million-row table is well within the default broadcast threshold (10 MB compressed, configurable via spark.sql.autoBroadcastJoinThreshold), making this the most effective shuffle-minimization technique.
What should I do if I get this DP-203 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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