The answer is to enable checkpointing in the streaming query to store progress. This configuration prevents duplicates because checkpointing in Spark Structured Streaming saves the offset of the last successfully processed event from Event Hubs; when the query restarts, it resumes from that exact offset, guaranteeing exactly-once processing and eliminating duplicate writes to Delta tables. On the Microsoft Azure Data Fundamentals DP-900 exam, this concept tests your understanding of how streaming reliability works in Azure Databricks, often appearing as a scenario where you must choose between checkpointing, watermarking, or output modes—a common trap is confusing checkpointing with watermarking, which handles late data rather than recovery. Remember the memory tip: “Checkpoint = Checkpoint offset, not check for late data.”
DP-900 Describe an analytics workload on Azure Practice Question
This DP-900 practice question tests your understanding of describe an analytics workload on azure. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 company uses Azure Databricks to process streaming data from Event Hubs. The data is transformed and written to Azure Data Lake Storage Gen2 as Delta tables. You notice that some records are duplicated in the Delta tables. Which configuration change should you make to prevent duplicates?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Enable checkpointing in the streaming query to store progress.
Checkpointing in Spark Structured Streaming stores the offset of the last processed event from Event Hubs. When the query restarts, it reads from the checkpointed offset, ensuring each event is processed exactly once and preventing duplicates in the Delta table.
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.
✗
Add a separate job to deduplicate the Delta table.
Why it's wrong here
This adds latency and complexity.
✓
Enable checkpointing in the streaming query to store progress.
Why this is correct
Checkpointing ensures exactly-once processing.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use Delta Lake's idempotent write support.
Why it's wrong here
Idempotent writes help but are not a substitute for checkpointing.
✗
Increase the batch interval in the streaming query.
Why it's wrong here
Longer intervals may reduce duplicates but do not eliminate them.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse idempotent writes (which prevent duplicate writes within a single transaction) with checkpointing (which prevents duplicate reads across query restarts), leading them to choose Option C instead of B.
Detailed technical explanation
How to think about this question
Under the hood, checkpointing writes the current offset range (e.g., partition:offset) to a durable location (e.g., Azure Data Lake Storage) in JSON format. On restart, Spark Structured Streaming reads the checkpoint directory and resumes from the last committed offset, guaranteeing at-least-once semantics. A subtle behavior is that if the checkpoint directory is deleted or corrupted, the query will start from the earliest available offset, causing massive duplication.
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 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-900 question in full detail.
Describe an analytics workload on Azure — This question tests Describe an analytics workload on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Enable checkpointing in the streaming query to store progress. — Checkpointing in Spark Structured Streaming stores the offset of the last processed event from Event Hubs. When the query restarts, it reads from the checkpointed offset, ensuring each event is processed exactly once and preventing duplicates in the Delta table.
What should I do if I get this DP-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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