Question 187 of 846

Develop data processing →hardMultiple ChoiceObjective-mapped

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 a data engineer for a global e-commerce company. You need to design a data processing solution using Azure Databricks that processes real-time clickstream data from Azure Event Hubs. The solution must join the streaming data with a slowly changing dimension (SCD) table that stores product details. The SCD table is stored in Azure Data Lake Storage Gen2 as Delta format and is updated every few hours. The joined results must be written to a Delta table for near-real-time dashboards. The key requirement is to ensure that the join always uses the latest version of the SCD data without reprocessing the entire stream. The solution must minimize latency and cost. Which approach should you recommend?

Clue words in this question

Noticing these words before you look at the options changes how you read each choice.

Clue: "always"
Why it matters: Absolute qualifier. An answer using 'always' is only correct if there are genuinely no exceptions — absolute statements are often wrong in networking.
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.

Question 1hardmultiple choice

Full question →

A
Use Spark Structured Streaming with foreachBatch and read the SCD table inside the foreachBatch function.
Why wrong: Similar to B, inefficient due to repeated reads.
B
Use Spark Structured Streaming with a static DataFrame for the SCD table and refresh it periodically using a trigger that reads the latest snapshot.
Periodic refresh of a static DataFrame minimizes overhead and ensures latest data.
C
Use Spark Structured Streaming with a batch read of the SCD table in each micro-batch using spark.read.
Why wrong: Reading per micro-batch increases overhead and latency.
D
Use Spark Structured Streaming with a streaming join on the SCD table by converting it to a stream using readStream.
Why wrong: Streaming join on a batch source is inefficient and may cause reprocessing.

Full breakdown with real-world context →

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

✓

Use Spark Structured Streaming with a static DataFrame for the SCD table and refresh it periodically using a trigger that reads the latest snapshot.

Option B is correct because it uses a static DataFrame for the SCD table and refreshes it periodically using a trigger (e.g., a time-based or file-based trigger). This approach ensures that the join always uses the latest version of the SCD data without reprocessing the entire stream, as the static DataFrame is re-read only when the SCD is updated. It minimizes latency and cost by avoiding the overhead of reading the SCD in every micro-batch (as in Option A) or using a streaming join (as in Option D), which is not suitable for batch-updated SCD tables.

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 foreachBatch and read the SCD table inside the foreachBatch function.
Why it's wrong here
Similar to B, inefficient due to repeated reads.
✓
Use Spark Structured Streaming with a static DataFrame for the SCD table and refresh it periodically using a trigger that reads the latest snapshot.
Why this is correct
Periodic refresh of a static DataFrame minimizes overhead and ensures latest data.
Clue confirmation
The clue words "always", "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use Spark Structured Streaming with a batch read of the SCD table in each micro-batch using spark.read.
Why it's wrong here
Reading per micro-batch increases overhead and latency.
✗
Use Spark Structured Streaming with a streaming join on the SCD table by converting it to a stream using readStream.
Why it's wrong here
Streaming join on a batch source is inefficient and may cause reprocessing.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates often assume that reading the SCD table in every micro-batch (Option A or C) is the simplest way to get the latest data, but they overlook the significant performance and cost penalties of full table scans in each micro-batch, especially for large SCD tables.

Trap categories for this question

Similar concept trap
Similar to B, inefficient due to repeated reads.

Detailed technical explanation

How to think about this question

Under the hood, Spark Structured Streaming with a static DataFrame for the SCD table works by caching the SCD data in memory after the initial read, and then using a trigger (e.g., a time-based trigger or a Delta table change detection) to refresh the cache only when the SCD is updated. This avoids the overhead of reading the entire SCD table in every micro-batch, which is critical for minimizing latency and cost in near-real-time scenarios. In real-world deployments, this pattern is often combined with Delta Lake's time travel or change data feed to efficiently detect and apply only the changes, further reducing I/O.

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.

What to study next

Got this wrong? Here's your next step.

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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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 — 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 static DataFrame for the SCD table and refresh it periodically using a trigger that reads the latest snapshot. — Option B is correct because it uses a static DataFrame for the SCD table and refreshes it periodically using a trigger (e.g., a time-based or file-based trigger). This approach ensures that the join always uses the latest version of the SCD data without reprocessing the entire stream, as the static DataFrame is re-read only when the SCD is updated. It minimizes latency and cost by avoiding the overhead of reading the SCD in every micro-batch (as in Option A) or using a streaming join (as in Option D), which is not suitable for batch-updated SCD tables.

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: "always", "minimum / minimize". Absolute qualifier. An answer using 'always' is only correct if there are genuinely no exceptions — absolute statements are often wrong in networking.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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