The correct answer is to use Spark Structured Streaming with a streaming join and cache the reference table as a static DataFrame. This approach minimizes latency because the static reference data is read once from Azure Data Lake Storage Gen2 and cached in memory, allowing the streaming data from Event Hubs to join against it without repeated I/O overhead, while ensuring data consistency by using a single snapshot of the reference table for the entire streaming query. On the DP-203 exam, this scenario tests your understanding of how to optimize streaming joins with slowly changing reference data—a common pattern for real-time pipelines. The key trap is assuming you must reload the reference data per micro-batch (which adds latency) or use batch processing (which breaks streaming continuity). Remember the memory tip: "Cache the static, stream the fast"—once your reference DataFrame is cached, it acts like a read-only lookup table that doesn't slow down the stream.
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 solution using Azure Databricks. The solution must process streaming data from Azure Event Hubs, join it with a static reference table stored in Azure Data Lake Storage Gen2 (Parquet format), and write the output to Azure Synapse Analytics. The reference table is updated daily. Which approach minimizes latency and ensures data consistency?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use Spark Structured Streaming with a streaming join and cache the reference table as a static DataFrame.
Option D is correct because using Spark Structured Streaming with a streaming join and a static DataFrame for the reference table minimizes latency and ensures consistency by reading the reference table once. Option A is wrong because batch processing every hour adds latency. Option B is wrong because loading the reference table per batch increases overhead. Option C is wrong because foreachBatch is useful for batch writes but does not improve join performance.
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 Spark Structured Streaming with a streaming join and cache the reference table as a static DataFrame.
Why this is correct
Caching the reference table as static minimizes latency and ensures consistency.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use Spark Structured Streaming with foreachBatch to write to Synapse.
Why it's wrong here
foreachBatch does not address the join latency.
✗
Use Spark Structured Streaming with a streaming join and load the reference table in each micro-batch.
Why it's wrong here
Loading per micro-batch increases overhead.
✗
Use a batch job that runs every hour to process the data.
Why it's wrong here
Batch processing adds latency and is not suitable for streaming.
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-203 question in full detail.
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.
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 Spark Structured Streaming with a streaming join and cache the reference table as a static DataFrame. — Option D is correct because using Spark Structured Streaming with a streaming join and a static DataFrame for the reference table minimizes latency and ensures consistency by reading the reference table once. Option A is wrong because batch processing every hour adds latency. Option B is wrong because loading the reference table per batch increases overhead. Option C is wrong because foreachBatch is useful for batch writes but does not improve join performance.
What should I do if I get this DP-203 question wrong?
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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