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

Section	Weight	Objectives
Topic 1: Data Processing	28%	- ETL Pipelines - Spark SQL - Data Transformation - Structured Streaming
Topic 2: Data Quality and Governance	12%	- Data Quality - Data Lineage - Governance
Topic 3: Monitoring and Troubleshooting	16%	- Performance Optimization - Monitoring - Troubleshooting
Topic 4: Data Modeling and Storage	20%	- Data Modeling - Storage Optimization - File Formats
Topic 5: Databricks Lakehouse Platform	24%	- Unity Catalog - Lakehouse Architecture - Data Management - Delta Lake

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q211-Q216):

NEW QUESTION # 211
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. libraries
B. jobs
C. workspace
D. fs
E. configure

Answer: D

Explanation:
https://docs.databricks.com/en/archive/dev-tools/cli/dbfs-cli.html

NEW QUESTION # 212
The data science team has requested assistance in accelerating queries on free form text from user reviews. The data is currently stored in Parquet with the below schema:
item_id INT, user_id INT, review_id INT, rating FLOAT, review STRING
The review column contains the full text of the review left by the user. Specifically, the data science team is looking to identify if any of 30 key words exist in this field.
A junior data engineer suggests converting this data to Delta Lake will improve query performance.
Which response to the junior data engineer's suggestion is correct?

A. Delta Lake statistics are not optimized for free text fields with high cardinality.
B. The Delta log creates a term matrix for free text fields to support selective filtering.
C. ZORDER ON review will need to be run to see performance gains.
D. Text data cannot be stored with Delta Lake.
E. Delta Lake statistics are only collected on the first 4 columns in a table.

Answer: A

Explanation:
Converting the data to Delta Lake may not improve query performance on free text fields with high cardinality, such as the review column. This is because Delta Lake collects statistics on the minimum and maximum values of each column, which are not very useful for filtering or skipping data on free text fields. Moreover, Delta Lake collects statistics on the first 32 columns by default, which may not include the review column if the table has more columns. Therefore, the junior data engineer's suggestion is not correct. A better approach would be to use a full-text search engine, such as Elasticsearch, to index and query the review column. Alternatively, you can use natural language processing techniques, such as tokenization, stemming, and lemmatization, to preprocess the review column and create a new column with normalized terms that can be used for filtering or skipping data.

NEW QUESTION # 213
A data engineer is working on a Databricks notebook that requires several third-party Python libraries. Some of these are available on PyPI, while others are custom-developed and stored as local.wheel (.whl) and source (.tar.gz) files in an S3 bucket. The goal is to ensure all dependencies are installed and correctly available across multiple jobs running on any automated cluster in a Unity Catalog-enabled workspace. The engineer needs to install the required dependencies in a way that ensures a consistent environment setup across interactive notebooks and jobs and complies with workspace security policies (no internet access). Which approach should the engineer use to install and manage these dependencies while also ensuring reproducibility and compliance?

A. Use an init script on the cluster to install all dependencies using pip, referencing the local file system.
B. Use %pip install in every notebook and job to install packages directly from PyPl and custom S3 paths.
C. Install all dependencies manually in the driver node of an interactive cluster, then export the environment and reimport on job clusters using %conda.
D. Create a Python wheel file for the entire project, upload it to the Databricks Workspace Files or Volumes, and install it using a Cluster Library or pip install in a requirements.txt declared within a Databricks Asset Bundle.

Answer: D

Explanation:
Packaging dependencies as wheel files and managing them through Databricks-supported artifacts ensures consistent, reproducible environments across interactive and job clusters.
Storing the wheels in Workspace Files or Unity Catalog Volumes complies with no-internet security constraints, while installing them via cluster libraries or Asset Bundles standardizes dependency management across all jobs and pipelines.

NEW QUESTION # 214
Which statement describes Delta Lake optimized writes?

A. Optimized writes logical partitions instead of directory partitions partition boundaries are only represented in metadata fewer small files are written.
B. A shuffle occurs prior to writing to try to group data together resulting in fewer files instead of each executor writing multiple files based on directory partitions.
C. An asynchronous job runs after the write completes to detect if files could be further compacted; yes, an OPTIMIZE job is executed toward a default of 1 GB.
D. Before a job cluster terminates, OPTIMIZE is executed on all tables modified during the most recent job.

Answer: B

Explanation:
Delta Lake optimized writes involve a shuffle operation before writing out data to the Delta table.
The shuffle operation groups data by partition keys, which can lead to a reduction in the number of output files and potentially larger files, instead of multiple smaller files. This approach can significantly reduce the total number of files in the table, improve read performance by reducing the metadata overhead, and optimize the table storage layout, especially for workloads with many small files.

NEW QUESTION # 215
A data engineer needs to provide access to a group named manufacturing-team. The team needs privileges to create tables in the quality schema. Which set of SQL commands will grant a group named manufacturing-team to create tables in a schema named production with the parent catalog named manufacturing with the least privileges?

A. GRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE CATALOG ON CATALOG manufacturing TO manufacturing-team;
B. GRANT USE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE CATALOG ON CATALOG manufacturing TO manufacturing-team;
C. GRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE CATALOG ON CATALOG manufacturing TO manufacturing-team;
D. GRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE CATALOG ON CATALOG manufacturing TO manufacturing-team;

Answer: A

Explanation:
To create a table within a schema, a principal must have CREATE TABLE on the schema, USE SCHEMA on that schema, and USE CATALOG on the parent catalog. This combination ensures the group has just enough privileges to create objects in that schema without excessive permissions like CREATE SCHEMA or CREATE CATALOG.

NEW QUESTION # 216
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