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Databricks Certified Data Engineer Professional Sample Questions:

1. 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) 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.
B) Because Delta Lake uses Parquet for data storage, Dremel encoding information for nesting can be directly referenced by the Delta transaction log.
C) Schema inference and evolution on Databricks ensure that inferred types will always accurately match the data types used by downstream systems.
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.

2. 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) 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.
B) Install all dependencies manually in the driver node of an interactive cluster, then export the environment and reimport on job clusters using %conda.
C) Use an init script on the cluster to install all dependencies using pip, referencing the local file system.
D) Use %pip install in every notebook and job to install packages directly from PyPl and custom S3 paths.

3. When scheduling Structured Streaming jobs for production, which configuration automatically recovers from query failures and keeps costs low?

A) Cluster: New Job Cluster;
Retries: None;
Maximum Concurrent Runs: 1
B) Cluster: Existing All-Purpose Cluster;
Retries: None;
Maximum Concurrent Runs: 1
C) Cluster: New Job Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: Unlimited
D) Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1
E) Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1

4. Why are Pandas UDFs often preferred over traditional PySpark UDFs in performance-critical applications involving large datasets?

A) They allow row-level execution of functions in Python with native Spark optimization, removing the need for columnar execution.
B) They leverage Apache Arrow to enable vectorized operations between the JVM and Python runtimes, reducing serialization costs and improving computational efficiency.
C) They eliminate the JVM-Python boundary by bypassing serialization entirely, thereby avoiding data conversion overhead.
D) They minimize memory usage by streaming each row individually through a lightweight Python wrapper, avoiding batch processing overhead.

5. When monitoring a complex workload, being able to see the query plan is critical to understanding what the workload is doing. Where can the visualization of the query plan be found?

A) In the Query Profiler, under Query Source
B) In the Spart UI, under the Jobs tab
C) In the Query Profiler, under the Stages tab
D) In the Spark UI, under the SQL/DataFrame tab

Solutions: