Databricks Exam Databricks Certified Data Engineer Professional Topic 6 Question 27 Discussion

Actual exam question for Databricks's Databricks Certified Data Engineer Professional exam

Question #: 27
Topic #: 6

[All Databricks Certified Data Engineer Professional Questions]

A data pipeline uses Structured Streaming to ingest data from kafka to Delta Lake. Data is being stored in a bronze table, and includes the Kafka_generated timesamp, key, and value. Three months after the pipeline is deployed the data engineering team has noticed some latency issued during certain times of the day.

A senior data engineer updates the Delta Table's schema and ingestion logic to include the current timestamp (as recoded by Apache Spark) as well the Kafka topic and partition. The team plans to use the additional metadata fields to diagnose the transient processing delays:

Which limitation will the team face while diagnosing this problem?

ANew fields not be computed for historic records.

BUpdating the table schema will invalidate the Delta transaction log metadata.

CUpdating the table schema requires a default value provided for each file added.

DSpark cannot capture the topic partition fields from the kafka source.

Show Suggested Answer

Suggested Answer: B

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by Arlie at Dec 07, 2024, 05:41 AM

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Vallie

1 months ago

Wait, they're using Structured Streaming with Kafka and Delta Lake? Someone's been watching too many Big Data tutorials on YouTube.

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Melissa

11 days ago

I wonder if adding those new fields will really help with the latency issues.

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Marsha

14 days ago

I know right, seems like they're trying to implement all the latest technologies.

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Miesha

2 months ago

Spark can't capture the topic partition fields from Kafka? That's wild. I guess the team's gonna have to get creative with their diagnostics.

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Winifred

2 months ago

Option C, huh? Providing default values for each file added? That's a pain, but I suppose it's better than having the schema update fail entirely.

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Marcelle

6 days ago

True, it's a trade-off for maintaining the integrity of the data pipeline.

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Gerald

8 days ago

I agree, but at least it ensures the schema update doesn't fail completely.

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Ronny

1 months ago

Yeah, it can be a hassle to provide default values for each file added.

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Frederica

2 months ago

Hmm, my money's on option B. Messing with the Delta transaction log metadata? That sounds like a recipe for disaster.

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Vernell

26 days ago

True, but I still think option B is the most risky choice here.

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Nicholle

1 months ago

I think option C might also be a limitation, having to provide a default value for each file added sounds like a hassle.

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Willis

1 months ago

But what about option A? Would that also cause problems with historic records?

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Rosamond

2 months ago

I agree, messing with the transaction log metadata could cause some serious issues.

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Elbert

3 months ago

I think the limitation will be that Spark cannot capture the topic partition fields from the kafka source.

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Theron

3 months ago

I disagree, I believe the limitation will be that updating the table schema will invalidate the Delta transaction log metadata.

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Kenneth

3 months ago

Ah, the joys of schema evolution! I guess the team is in for a fun time with those 'transient processing delays'. At least they're trying to get to the bottom of it.

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Glory

2 months ago

B) Updating the table schema will invalidate the Delta transaction log metadata.

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Emile

2 months ago

A) New fields not be computed for historic records.

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Soledad

3 months ago

I think the limitation will be that new fields cannot be computed for historic records.

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