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Databricks Exam Databricks Certified Data Engineer Professional Topic 6 Question 33 Discussion

Actual exam question for Databricks's Databricks Certified Data Engineer Professional exam
Question #: 33
Topic #: 6
[All Databricks Certified Data Engineer Professional Questions]

A new data engineer notices that a critical field was omitted from an application that writes its Kafka source to Delta Lake. This happened even though the critical field was in the Kafka source. That field was further missing from data written to dependent, long-term storage. The retention threshold on the Kafka service is seven days. The pipeline has been in production for three months.

Which describes how Delta Lake can help to avoid data loss of this nature in the future?

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Suggested Answer: E

This is the correct answer because it describes how Delta Lake can help to avoid data loss of this nature in the future. By ingesting all raw data and metadata from Kafka to a bronze Delta table, Delta Lake creates a permanent, replayable history of the data state that can be used for recovery or reprocessing in case of errors or omissions in downstream applications or pipelines. Delta Lake also supports schema evolution, which allows adding new columns to existing tables without affecting existing queries or pipelines. Therefore, if a critical field was omitted from an application that writes its Kafka source to Delta Lake, it can be easily added later and the data can be reprocessed from the bronze table without losing any information. Verified Reference: [Databricks Certified Data Engineer Professional], under ''Delta Lake'' section; Databricks Documentation, under ''Delta Lake core features'' section.


by Elouise at Apr 08, 2025, 01:22 AM

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Lovetta
2 days ago
I think option E sounds familiar; it emphasizes the importance of capturing all raw data from Kafka, which could help prevent data loss in the future.
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Ettie
8 days ago
I remember reading that Delta Lake has features for handling schema changes, but I'm not sure if it can retroactively fix missing fields.
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Novella
13 days ago
This is a tricky one, but I think the key is in understanding Delta Lake's capabilities. I'm leaning towards option B, since it seems like the best way to retroactively calculate the missing field value. I'll double-check the other options, but that's my initial thought.
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Ceola
19 days ago
Easy peasy! The answer is E - ingesting all the raw data and metadata from Kafka to a bronze Delta table creates a permanent, replayable history of the data state. That way, we can go back and recover the missing field.
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Daron
24 days ago
Okay, I think I've got this. The answer is B - Delta Lake's schema evolution can retroactively calculate the correct value for the missing field, as long as it was present in the original Kafka source. That should help us recover the lost data.
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Rosina
30 days ago
Hmm, I'm a bit confused by this question. I know Delta Lake has some features for data versioning and recovery, but I'm not sure which one is the best fit for this scenario. I'll need to re-read the question and options carefully.
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Ethan
1 month ago
This seems like a tricky question, but I think the key is understanding how Delta Lake can help recover from data loss. I'll need to carefully review the options and think through the capabilities of Delta Lake.
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Henriette
6 months ago
I'm not sure, but option E also sounds like a good way to create a permanent history of the data state.
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Jill
6 months ago
I agree with Dulce. Delta Lake automatically checking for all fields in the source data seems like a reliable solution.
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Johnetta
6 months ago
I'm leaning towards option C. Automatically checking that all source fields are included in the ingestion layer is a great safeguard against this kind of data loss issue.
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Cassi
5 months ago
User 3: Agreed, having that automatic check in place can save a lot of trouble down the line.
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Emogene
5 months ago
User 2: Yeah, that would definitely help prevent missing critical fields in the future.
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Sharee
5 months ago
User 1: I think option C is a good choice. It ensures all fields from the source data are included in the ingestion layer.
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Rodolfo
7 months ago
Option D is hilarious. 'Data can never be permanently dropped or deleted from Delta Lake' - that's like saying my socks can never disappear in the laundry. Good one!
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Carri
5 months ago
Matthew: Yeah, having a permanent record of the data can definitely help in case of any issues.
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Sage
5 months ago
User 3: Option E seems like a good solution to keep a history of the data state for replayability.
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Matthew
6 months ago
User 2: I agree, it's important to have proper checks in place to avoid data loss.
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Thomasena
6 months ago
User 1: Option D is definitely a stretch. Data can definitely be lost if not properly managed.
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Cathrine
7 months ago
I think option B is the best choice here. Being able to retroactively calculate the missing field's value is a game-changer. Delta Lake's schema evolution is a lifesaver in these scenarios.
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Margot
7 months ago
Option E sounds like the way to go. Capturing the raw data and metadata from Kafka to a Delta bronze table is a solid approach. That way, we can always go back and re-ingest the data if needed.
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Clorinda
6 months ago
Yes, having a permanent, replayable history of the data state in a bronze Delta table provides a safety net in case of missing critical fields or data loss in the future.
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Abraham
6 months ago
Option E sounds like the way to go. Capturing the raw data and metadata from Kafka to a Delta bronze table is a solid approach. That way, we can always go back and re-ingest the data if needed.
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Dulce
7 months ago
I think option C is the best choice to avoid data loss in the future.
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