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Databricks is a leading cloud-based data platform that enables organizations to accelerate innovation and achieve their data-driven goals. To showcase their expertise in using the Databricks platform, data professionals can earn the Databricks-Certified-Professional-Data-Engineer (Databricks Certified Professional Data Engineer) certification. Databricks Certified Professional Data Engineer Exam certification is designed to validate the skills and knowledge required to design, build, and maintain data solutions on the Databricks platform.

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q101-Q106):

NEW QUESTION # 101
A team of data engineer are adding tables to a DLT pipeline that contain repetitive expectations for many of the same data quality checks.
One member of the team suggests reusing these data quality rules across all tables defined for this pipeline.
What approach would allow them to do this?

A. Add data quality constraints to tables in this pipeline using an external job with access to pipeline configuration files.
B. Maintain data quality rules in a separate Databricks notebook that each DLT notebook of file.
C. Maintain data quality rules in a Delta table outside of this pipeline's target schema, providing the schema name as a pipeline parameter.
D. Use global Python variables to make expectations visible across DLT notebooks included in the same pipeline.

Answer: C

Explanation:
Maintaining data quality rules in a centralized Delta table allows for the reuse of these rules across multiple DLT (Delta Live Tables) pipelines. By storing these rules outside the pipeline's target schema and referencing the schema name as a pipeline parameter, the team can apply the same set of data quality checks to different tables within the pipeline. This approach ensures consistency in data quality validations and reduces redundancy in code by not having to replicate the same rules in each DLT notebook or file.
References:
* Databricks Documentation on Delta Live Tables: Delta Live Tables Guide

NEW QUESTION # 102
A data engineer has written the following query:
1. SELECT *
2. FROM json.`/path/to/json/file.json`;
The data engineer asks a colleague for help to convert this query for use in a Delta Live Tables (DLT)
pipeline. The query should create the first table in the DLT pipeline.
Which of the following describes the change the colleague needs to make to the query?

A. They need to add a live. prefix prior to json. in the FROM line
B. They need to add the cloud_files(...) wrapper to the JSON file path
C. They need to add a COMMENT line at the beginning of the query
D. They need to add a CREATE DELTA LIVE TABLE table_name AS line at the beginning of the query
E. They need to add a CREATE LIVE TABLE table_name AS line at the beginning of the query

Answer: E

NEW QUESTION # 103
How do you access or use tables in the unity catalog?

A. catalog_name.schema_name.table_name
B. schema_name.catalog_name.table_name
C. catalog_name.table_name
D. schema_name.table_name
E. catalog_name.database_name.schema_name.table_name

Answer: A

Explanation:
Explanation
The answer is catalog_name.schema_name.table_name
Graphical user interface, diagram Description automatically generated

Note: Database and Schema are analogous they are interchangeably used in the Unity catalog.
FYI, A catalog is registered under a metastore, by default every workspace has a default metastore called hive_metastore, with a unity catalog you have the ability to create meatstores and share that across multiple workspaces.

Diagram Description automatically generated

NEW QUESTION # 104
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?

A. Schema inference and evolution on Databricks ensure that inferred types will always accurately match the data types used by downstream systems.
B. Tungsten 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.
C. Because Delta Lake uses Parquet for data storage, Dremel encoding information for nesting can be directly referenced by the Delta transaction log.
D. By default Delta Lake collects statistics on the first 32 columns in a table; these statistics are leveraged for data skipping when executing selective queries.

Answer: D

Explanation:
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.References: Databricks documentation on Delta Lake optimization techniques, including data skipping and statistics collection (https://docs.databricks.com/delta/optimizations/index.html).

NEW QUESTION # 105
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. libraries
C. fs
D. workspace
E. jobs

Answer: B

Explanation:
The libraries command group allows you to install, uninstall, and list libraries on Databricks clusters. You can use the libraries install command to install a custom Python Wheel on a cluster by specifying the --whl option and the path to the wheel file. For example, you can use the following command to install a custom Python Wheel named mylib-0.1-py3-none-any.whl on a cluster with the id 1234-567890-abcde123:
databricks libraries install --cluster-id 1234-567890-abcde123 --whl dbfs:/mnt/mylib/mylib-0.1-py3-none-any.
whl
This will upload the custom Python Wheel to the cluster and make it available for use with a production job.
You can also use the libraries uninstall command to uninstall a library from a cluster, and the libraries list command to list the libraries installed on a cluster.
References:
* Libraries CLI (legacy): https://docs.databricks.com/en/archive/dev-tools/cli/libraries-cli.html
* Library operations: https://docs.databricks.com/en/dev-tools/cli/commands.html#library-operations
* Install or update the Databricks CLI: https://docs.databricks.com/en/dev-tools/cli/install.html

NEW QUESTION # 106
......

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