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Databricks Certified Data Engineer Professional Sample Questions:

1. 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) 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) Install all dependencies manually in the driver node of an interactive cluster, then export the environment and reimport on job clusters using %conda.

2. Which method can be used to determine the total wall-clock time it took to execute a query?

A) In the Spark UI, take the sum of all task durations that ran across all stages for all jobs associated with that query.
B) Open the Query Profiler associated with that query and use the Aggregated task time metric.
C) In the Spark UI, take the job duration of the longest-running job associated with that query.
D) Open the Query Profiler associated with that query and use the Total wall-clock duration metric.

3. A data engineer is designing a Lakeflow Declarative Pipeline to process streaming order data.
The pipeline uses Auto Loader to ingest data and must enforce data quality by ensuring customer_id and amount are greater than zero. Invalid records should be dropped. Which Lakeflow Declarative Pipelines configurations implement this requirement using Python?

A) @dlt.table
@dlt.expect_or_drop("valid_customer", "customer_id IS NOT NULL")
@dlt.expect_or_drop("valid_amount", "amount > 0")
def silver_orders():
return dlt.read_stream("bronze_orders")
B) @dlt.table
def silver_orders():
return (
dlt.read_stream("bronze_orders")
.expect("valid_customer", "customer_id IS NOT NULL")
.expect("valid_amount", "amount > 0")
)
C) @dlt.table
def silver_orders():
return (
dlt.read_stream("bronze_orders")
.expect_or_drop("valid_customer", "customer_id IS NOT NULL")
.expect_or_drop("valid_amount", "amount > 0")
)
D) @dlt.table
@dlt.expect("valid_customer", "customer_id IS NOT NULL")
@dlt.expect("valid_amount", "amount > 0")
def silver_orders():
return dlt.read_stream("bronze_orders")

4. An upstream system has been configured to pass the date for a given batch of data to the Databricks Jobs API as a parameter. The notebook to be scheduled will use this parameter to load data with the following code:
df = spark.read.format("parquet").load(f"/mnt/source/(date)")
Which code block should be used to create the date Python variable used in the above code block?

A) date = spark.conf.get("date")
B) date = dbutils.notebooks.getParam("date")
C) dbutils.widgets.text("date", "null")
date = dbutils.widgets.get("date")
D) input_dict = input()
date= input_dict["date"]
E) import sys
date = sys.argv[1]

5. A junior data engineer has configured a workload that posts the following JSON to the Databricks REST API endpoint 2.0/jobs/create.

Assuming that all configurations and referenced resources are available, which statement describes the result of executing this workload three times?

A) One new job named "Ingest new data" will be defined in the workspace, but it will not be executed.
B) The logic defined in the referenced notebook will be executed three times on the referenced existing all purpose cluster.
C) Three new jobs named "Ingest new data" will be defined in the workspace, but no jobs will be executed.
D) Three new jobs named "Ingest new data" will be defined in the workspace, and they will each run once daily.
E) The logic defined in the referenced notebook will be executed three times on new clusters with the configurations of the provided cluster ID.

Solutions: