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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?

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Suggested Answer: B

A unit test is designed to verify the correctness of a small, isolated piece of code, typically a single function. Testing a mathematical function that calculates the area under a curve is an example of a unit test because it is testing a specific, individual function to ensure it operates as expected.


Software Testing Fundamentals: Unit Testing

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

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Janessa
4 months ago
C - I thought default values were optional, though?
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Shakira
4 months ago
B - Updating the schema messes with the transaction log, right?
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Joseph
4 months ago
Wait, can’t they just backfill the old records?
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Malinda
4 months ago
Totally agree, that's a big limitation!
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Lynelle
4 months ago
A - New fields won't apply to old records.
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Sean
5 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
5 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
5 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
5 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
5 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
5 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
5 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
5 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
10 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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Colton
8 months ago
Yeah, they might need to be careful with how they make changes to the pipeline.
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Miss
9 months ago
It's possible that updating the table schema could cause some unexpected problems.
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Melissa
9 months ago
I wonder if adding those new fields will really help with the latency issues.
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Marsha
9 months ago
I know right, seems like they're trying to implement all the latest technologies.
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Miesha
10 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
10 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
9 months ago
True, it's a trade-off for maintaining the integrity of the data pipeline.
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Gerald
9 months ago
I agree, but at least it ensures the schema update doesn't fail completely.
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Ronny
10 months ago
Yeah, it can be a hassle to provide default values for each file added.
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Frederica
11 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
9 months ago
True, but I still think option B is the most risky choice here.
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Nicholle
10 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
10 months ago
But what about option A? Would that also cause problems with historic records?
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Rosamond
10 months ago
I agree, messing with the transaction log metadata could cause some serious issues.
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Elbert
11 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
11 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
11 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
10 months ago
B) Updating the table schema will invalidate the Delta transaction log metadata.
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Emile
10 months ago
A) New fields not be computed for historic records.
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Soledad
12 months ago
I think the limitation will be that new fields cannot be computed for historic records.
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