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Databricks Certified Data Engineer Professional Sample Questions:

1. A table named user_ltv is being used to create a view that will be used by data analysts on various teams. Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
The user_ltv table has the following schema:
email STRING, age INT, ltv INT
The following view definition is executed:

An analyst who is not a member of the auditing group executes the following query:
SELECT * FROM user_ltv_no_minors
Which statement describes the results returned by this query?

A) All records from all columns will be displayed with the values in user_ltv.
B) All age values less than 18 will be returned as null values all other columns will be returned with the values in user_ltv.
C) All values for the age column will be returned as null values, all other columns will be returned with the values in user_ltv.
D) All columns will be displayed normally for those records that have an age greater than 18; records not meeting this condition will be omitted.
E) All columns will be displayed normally for those records that have an age greater than 17; records not meeting this condition will be omitted.

2. The marketing team is looking to share data in an aggregate table with the sales organization, but the field names used by the teams do not match, and a number of marketing specific fields have not been approval for the sales org.
Which of the following solutions addresses the situation while emphasizing simplicity?

A) Use a CTAS statement to create a derivative table from the marketing table configure a production jon to propagation changes.
B) Create a view on the marketing table selecting only these fields approved for the sales team alias the names of any fields that should be standardized to the sales naming conventions.
C) Add a parallel table write to the current production pipeline, updating a new sales table that varies Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from as required from marketing table.
D) Create a new table with the required schema and use Delta Lake's DEEP CLONE functionality to sync up changes committed to one table to the corresponding table.
E) Instruct the marketing team to download results as a CSV and email them to the sales organization.

3. The following code has been migrated to a Databricks notebook from a legacy workload:

The code executes successfully and provides the logically correct results, however, it takes over
20 minutes to extract and load around 1 GB of data.
Which statement is a possible explanation for this behavior?

A) %sh does not distribute file moving operations; the final line of code should be updated to use %fs instead.
B) %sh executes shell code on the driver node. The code does not take advantage of the worker nodes or Databricks optimized Spark.
C) %sh triggers a cluster restart to collect and install Git. Most of the latency is related to cluster startup time.
D) Instead of cloning, the code should use %sh pip install so that the Python code can get executed in parallel across all nodes in a cluster.
E) Python will always execute slower than Scala on Databricks. The run.py script should be refactored to Scala.

4. A data pipeline uses Structured Streaming to ingest data from kafka to Delta Lake. Data is being stored in a bronze table, and includes the Kafka_generated timesamp, key, and value. Three months after the pipeline is deployed the data engineering team has noticed some latency issued during certain times of the day.
A senior data engineer updates the Delta Table's schema and ingestion logic to include the current timestamp (as recoded by Apache Spark) as well the Kafka topic and partition. The team plans to use the additional metadata fields to diagnose the transient processing delays.
Which limitation will the team face while diagnosing this problem?

A) Updating the table schema will invalidate the Delta transaction log metadata.
B) Spark cannot capture the topic partition fields from the kafka source.
C) New fields not be computed for historic records.
D) New fields cannot be added to a production Delta table.
E) Updating the table schema requires a default value provided for each file added.
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

5. The DevOps team has configured a production workload as a collection of notebooks scheduled to run daily using the Jobs UI. A new data engineering hire is onboarding to the team and has requested access to one of these notebooks to review the production logic.
What are the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data?

A) Can Manage
B) No permissions
C) Can Run
D) Can Edit
E) Can Read

Solutions: