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Databricks Certified Data Engineer Professional Sample Questions:

1. A distributed team of data analysts share computing resources on an interactive cluster with autoscaling configured. In order to better manage costs and query throughput, the workspace administrator is hoping to evaluate whether cluster upscaling is caused by many concurrent users or resource-intensive queries.
In which location can one review the timeline for cluster resizing events?

A) Cluster Event Log
B) Executor's log file
C) Ganglia
D) Workspace audit logs
E) Driver's log file

2. In order to facilitate near real-time workloads, a data engineer is creating a helper function to leverage the schema detection and evolution functionality of Databricks Auto Loader. The desired function will automatically detect the schema of the source directly, incrementally process JSON files as they arrive in a source directory, and automatically evolve the schema of the table when new fields are detected.
The function is displayed below with a blank:

Which response correctly fills in the blank to meet the specified requirements?

A)

B)

C)

D)

E)

3. A view is registered with the following code:

Both users and orders are Delta Lake tables.
Which statement describes the results of querying recent_orders?

A) All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
B) All logic will execute when the table is defined and store the result of joining tables to the DBFS; this stored data will be returned when the table is queried.
C) The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.
D) All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.

4. A healthcare analytics team is implementing a dimensional model in Delta Lake for patient care analysis. They have a date dimension table and are evaluating design options to ensure it supports a wide range of time-based analyses. Which design approach for the date dimension will support efficient time-based querying and aggregation?

A) Create separate dimension tables for different calendar systems (fiscal, academic, etc.)
B) Pre-calculate attributes like fiscal_period, quarter, month_name, day_of_week, and holiday.
C) Store only the date value and calculate all time attributes in queries.
D) Store the date as string in ISO format (YYYY-MM-DD) for readability.

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

Solutions: