Databricks Certified Data Engineer Professional Exam - 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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Software Testing Fundamentals: Unit Testing

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

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Janessa

6 months ago

C - I thought default values were optional, though?

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Shakira

6 months ago

B - Updating the schema messes with the transaction log, right?

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Joseph

6 months ago

Wait, can’t they just backfill the old records?

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Malinda

7 months ago

Totally agree, that's a big limitation!

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Lynelle

7 months ago

A - New fields won't apply to old records.

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Sean

7 months ago

I recall that Spark can capture metadata from Kafka, including topic and partition info, so I doubt option D is correct.

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Lynda

7 months ago

I thought that when you add fields to a Delta table, you don't necessarily need to provide default values for existing records. So, I'm leaning away from option C.

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Scot

7 months ago

I'm not entirely sure, but I think updating the schema might affect the transaction log. I feel like I've seen a question about that in practice exams.

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Gail

8 months ago

I remember reading that when you add new fields to a Delta table, the existing records won't have those fields populated. So, I think option A could be a limitation.

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Francoise

8 months ago

Ah, I see. That makes sense. So we'll need to be careful in our analysis to account for the fact that we won't have a complete picture of the historical data.

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German

8 months ago

Okay, I think I've got a handle on this. The key limitation is that the new fields won't be computed for historic records, so we'll only have the new metadata for data ingested after the schema change.

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Meghan

8 months ago

Hmm, I'm a bit confused about the limitations of adding new fields to an existing Delta table. I'll need to review the Delta Lake documentation to make sure I understand this properly.

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Sheron

8 months ago

This seems like a tricky one. I'll need to think carefully about the implications of updating the Delta table schema.

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Vallie

1 year 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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Colton

11 months ago

Yeah, they might need to be careful with how they make changes to the pipeline.

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Miss

11 months ago

It's possible that updating the table schema could cause some unexpected problems.

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Melissa

11 months ago

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

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Marsha

11 months ago

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

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Miesha

1 year 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

1 year 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

11 months ago

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

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Gerald

11 months ago

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

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Ronny

12 months ago

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

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Frederica

1 year 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

12 months ago

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

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Nicholle

12 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 year ago

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

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Rosamond

1 year ago

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

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Elbert

1 year ago

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

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Theron

1 year 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

1 year 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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