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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:

Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineer us ingesting JSON files from cloud object storage using Databricks Auto Loader.
The source folder may occasionally receive large files of data, which risks overwhelming the stream. To ensure predictable micro-batch sizes, the team wants to throttle ingestion based on the volume of data scanned at 1 GB, regardless of the number of files. Which Auto Loader configuration should the data engineer used to achieve this?

A) Configure cloudFiles.maxBytesPerTrigger with 1 GB to place a limit.
B) Configure cloudFiles.maxSizePerTrigger with 1 GB to place a limit.
C) Configure cloudFiles.maxFilesPerTrigger and estimate the average file size to approximate a size-based throttle of 1 GB.
D) Configure cloudFiles.maxPartitionBytes with 1GB to limit data in each partition.

2. A Data engineer wants to run unit's tests using common Python testing frameworks on python functions defined across several Databricks notebooks currently used in production. How can the data engineer run unit tests against function that work with data in production?

A) Define units test and functions within the same notebook
B) Run unit tests against non-production data that closely mirrors production
C) Define and import unit test functions from a separate Databricks notebook
D) Define and unit test functions using Files in Repos

3. The marketing team is looking to share data in an aggregate table with the sales organization, but the field names used by the teams do not match, and a number of marketing specific fields have not been approval for the sales org.
Which of the following solutions addresses the situation while emphasizing simplicity?

A) Use a CTAS statement to create a derivative table from the marketing table configure a production jon to propagation changes.
B) Instruct the marketing team to download results as a CSV and email them to the sales organization.
C) Add a parallel table write to the current production pipeline, updating a new sales table that varies as required from marketing table.
D) Create a view on the marketing table selecting only these fields approved for the sales team alias the names of any fields that should be standardized to the sales naming conventions.
E) Create a new table with the required schema and use Delta Lake's DEEP CLONE functionality to sync up changes committed to one table to the corresponding table.

4. A data engineer created a daily batch ingestion pipeline using a cluster with the latest DBR version to store banking transaction data, and persisted it in a MANAGED DELTA table called prod.gold.all_banking_transactions_daily. The data engineer is constantly receiving complaints from business users who query this table ad hoc through a SQL Serverless Warehouse about poor query performance. Upon analysis, the data engineer identified that these users frequently use high- cardinality columns as filters. The engineer now seeks to implement a data layout optimization technique that is incremental, easy to maintain, and can evolve over time. Which command should the data engineer implement?

A) Alter the table to use Z-ORDER and implement a periodic OPTIMIZE command.
B) Alter the table to use Hive-Style Partitions and implement a periodic OPTIMIZE command.
C) Alter the table to use Hive-Style Partitions + Z-ORDER and implement a periodic OPTIMIZE command.
D) Alter the table to use Liquid Clustering and implement a periodic OPTIMIZE command.

5. Assuming that the Databricks CLI has been installed and configured correctly, which Databricks CLI command can be used to upload a custom Python Wheel to object storage mounted with the DBFS for use with a production job?

A) configure
B) workspace
C) libraries
D) jobs
E) fs

Solutions: