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Databricks Certified Data Engineer Professional Exam - Topic 3 Question 52 Discussion

A data engineer is configuring Delta Sharing for a Databricks-to-Databricks scenario to optimize read performance. The recipient needs to perform time travel queries and streaming reads on shared sales data.Which configuration will provide the optimal performance while enabling these capabilities?
A) Share tables WITH HISTORY, ensure tables don't have partitioning enabled, and enable CDF before sharing.
B) Share tables WITHOUT HISTORY and enable partitioning for better query performance.
C) Share the entire schema WITHOUT HISTORY and rely on recipient-side caching for performance.
D) Use the open sharing protocol instead of Databricks-to-Databricks sharing for better performance.

Databricks Certified Data Engineer Professional Exam - Topic 3 Question 52 Discussion

Actual exam question for Databricks's Databricks Certified Data Engineer Professional exam
Question #: 52
Topic #: 3
[All Databricks Certified Data Engineer Professional Questions]

A data engineer is configuring Delta Sharing for a Databricks-to-Databricks scenario to optimize read performance. The recipient needs to perform time travel queries and streaming reads on shared sales data.

Which configuration will provide the optimal performance while enabling these capabilities?

Show Suggested Answer Hide Answer
Suggested Answer: A

The official Delta Sharing guidance specifies that in order for recipients to use time travel queries and streaming reads, providers must share Delta tables WITH HISTORY. Sharing history ensures the Delta log is included, which enables efficient access to table snapshots and incremental data streams. Additionally, Change Data Feed (CDF) must be enabled prior to sharing if downstream consumers require streaming CDC queries. Without history, recipients cannot perform time travel or streaming queries. Open sharing supports static Delta tables but lacks streaming support. Therefore, sharing tables WITH HISTORY and enabling CDF is the required configuration for both performance and functionality.


by Georgiana at Jun 30, 2026, 02:13 PM

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Eun
2 days ago
I think option A is the best choice. Time travel needs history.
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Trina
7 days ago
Definitely A! History is key for accurate queries.
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Pauline
13 days ago
C is risky. Relying on caching might not be reliable.
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Kanisha
18 days ago
A is optimal for performance and capabilities.
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Harris
23 days ago
D sounds interesting, but I trust Databricks for this scenario.
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Wava
28 days ago
I prefer A. Partitioning can complicate things.
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Mohammad
1 month ago
Option B could work too, but no history limits time travel.
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Reita
1 month ago
I agree, A seems solid. CDF is crucial for streaming reads.
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Keshia
1 month ago
I think option A is the best choice. Time travel needs history.
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Svetlana
2 months ago
Sharing without history seems risky, I’d stick with A.
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Ty
2 months ago
Definitely A, CDF is a game changer for streaming reads.
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Claribel
2 months ago
Surprised that option D is even mentioned, isn't it less efficient?
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Stanford
2 months ago
I disagree, partitioning can really boost performance.
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Deeanna
2 months ago
Option A is the way to go for time travel!
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Noel
2 months ago
I recall that using the open sharing protocol can sometimes enhance performance, but I wonder if it really applies here with Databricks-to-Databricks.
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Peter
3 months ago
I practiced a similar question where caching was emphasized, but I feel like sharing the entire schema without history might not be optimal.
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Celestine
3 months ago
I'm not entirely sure, but I remember something about partitioning being beneficial for performance, so maybe option B could be a contender?
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Santos
3 months ago
I think option A sounds familiar since it mentions sharing with history, which is important for time travel queries.
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