Databricks Exam Databricks-Certified-Professional-Data-Engineer Topic 1 Question 28 Discussion

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

Question #: 28
Topic #: 1

[All Databricks-Certified-Professional-Data-Engineer Questions]

A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings. The source data contains 100 unique fields in a highly nested JSON structure.

The silver_device_recordings table will be used downstream for highly selective joins on a number of fields, and will also be leveraged by the machine learning team to filter on a handful of relevant fields, in total, 15 fields have been identified that will often be used for filter and join logic.

The data engineer is trying to determine the best approach for dealing with these nested fields before declaring the table schema.

Which of the following accurately presents information about Delta Lake and Databricks that may Impact their decision-making process?

ABecause Delta Lake uses Parquet for data storage, Dremel encoding information for nesting can be directly referenced by the Delta transaction log.

BTungsten encoding used by Databricks is optimized for storing string data: newly-added native support for querying JSON strings means that string types are always most efficient.

CSchema inference and evolution on Databricks ensure that inferred types will always accurately match the data types used by downstream systems.

DBy default Delta Lake collects statistics on the first 32 columns in a table; these statistics are leveraged for data skipping when executing selective queries.

Show Suggested Answer

Suggested Answer: D

Delta Lake, built on top of Parquet, enhances query performance through data skipping, which is based on the statistics collected for each file in a table. For tables with a large number of columns, Delta Lake by default collects and stores statistics only for the first 32 columns. These statistics include min/max values and null counts, which are used to optimize query execution by skipping irrelevant data files. When dealing with highly nested JSON structures, understanding this behavior is crucial for schema design, especially when determining which fields should be flattened or prioritized in the table structure to leverage data skipping efficiently for performance optimization. Reference: Databricks documentation on Delta Lake optimization techniques, including data skipping and statistics collection (https://docs.databricks.com/delta/optimizations/index.html).

by Gracie at Jan 14, 2025, 07:50 PM

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Ayesha

3 months ago

Hold up, are we talking about Dremel, Tungsten, and Databricks all in one question? This exam is starting to sound like a tech startup pitch competition!

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Golda

3 months ago

Ah, so Delta Lake collects statistics on the first 32 columns by default. That could be handy for optimizing our queries. I wonder if we can configure that to suit our needs.

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Rodolfo

3 months ago

Schema inference and evolution on Databricks? That could save us a lot of headaches down the line. I wonder how reliable it is though.

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Alberta

2 months ago

Yeah, schema evolution on Databricks is a game-changer for sure.

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Dustin

3 months ago

It's pretty reliable, Databricks does a good job matching inferred types with downstream systems.

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4 months ago

I think option D is important because collecting statistics on the first 32 columns can help with data skipping.

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