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Databricks Certified Data Engineer Professional Sample Questions:

1. 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 requires a default value provided for each file added.
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B) New fields not be computed for historic records.
C) Updating the table schema will invalidate the Delta transaction log metadata.
D) Spark cannot capture the topic partition fields from the kafka source.
E) New fields cannot be added to a production Delta table.

2. A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on Task A.
If task A fails during a scheduled run, which statement describes the results of this run?

A) Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until all tasks have successfully been completed.
B) Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task A failed, all commits will be rolled back automatically.
C) Tasks B and C will be skipped; some logic expressed in task A may have been committed before task failure.
D) Tasks B and C will attempt to run as configured; any changes made in task A will be rolled back due to task failure.
E) Tasks B and C will be skipped; task A will not commit any changes because of stage failure.

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

4. The business reporting tem requires that data for their dashboards be updated every hour. The total processing time for the pipeline that extracts transforms and load the data for their pipeline runs in 10 minutes.
Assuming normal operating conditions, which configuration will meet their service-level agreement requirements with the lowest cost?

A) Schedule a job to execute the pipeline once hour on a new job cluster.
B) Schedule a Structured Streaming job with a trigger interval of 60 minutes.
C) Schedule a jo to execute the pipeline once and hour on a dedicated interactive cluster.
D) Configure a job that executes every time new data lands in a given directory.

5. A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Incremental state information should be maintained for 10 minutes for late-arriving data.
Streaming DataFrame df has the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:

Choose the response that correctly fills in the blank within the code block to complete this task.

A) awaitArrival("event_time", "10 minutes")
B) withWatermark("event_time", "10 minutes")
C) await("event_time + `10 minutes'")
D) delayWrite("event_time", "10 minutes")
E) slidingWindow("event_time", "10 minutes")

Solutions: