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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Data Sharing and Federation | - Delta Sharing
|
| Monitoring and Alerting | - Alerting
|
| Developing Code for Data Processing using Python and SQL | - Using Python and Tools for Development
|
| Data Transformation, Cleansing, and Quality | - Data Quality
|
| Debugging and Deploying | - Deploying CI/CD
|
| Data Ingestion & Acquisition | - Design and implement data ingestion pipelines
|
| Data Governance | - Unity Catalog Permissions
|
| Data Modelling | - Scalable Data Models
|
| Ensuring Data Security and Compliance | - Data Security
|
| Cost & Performance Optimisation | - Delta Optimization
|
Databricks Certified Data Engineer Professional Sample Questions:
A data engineer is working in an interactive notebook with many transformations before outputting the result from display(df.collect() ). The notebook includes wide transformations and a cross join.
The data engineer is getting the following error: "The spark driver has stopped unexpectedly and is restarting. Your notebook will be automatically reattached." Which action should the data engineer take?
- A. Look at the compute metrics UI to see if the executors have higher than 90% memory utilization.
- B. Rewrite their code to avoid putting memory pressure on the driver node.
- C. Check into the Spark UI to see how many jobs are assigned to each stage as they are employing fewer executors.
- D. Run the notebook on a single node cluster to keep driver from falling.
Correct Answer: B 🗳️
Explanation: Only visible for Prep4pass members. You can sign-up / login (it's free).
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 an hour on a dedicated interactive cluster.
- B. Configure a job that executes every time new data lands in a given directory.
- C. Schedule a job to execute the pipeline once an hour on a new job cluster.
- D. Schedule a Structured Streaming job with a trigger interval of 60 minutes.
Correct Answer: C 🗳️
Explanation: Only visible for Prep4pass members. You can sign-up / login (it's free).
The data governance team has instituted a requirement that the "user" table containing Personal Identifiable Information (PII) must have the appropriate masking on the SSN column. This means that anyone outside of the HRAdminGroup should see masked social security numbers as ***-**-
****.
The team created a masking function:
What does the data governance team need to do next to achieve this goal?
- A. CREATE TABLE users
(name STRING);
ALTER TABLE users CREATE COLUMN ssn CREATE MASK ssn_mask; - B. CREATE TABLE users
(name STRING, ssn STRING);
ALTER TABLE users ALTER COLUMN ssn SET MASK ssn_mask; - C. CREATE TABLE users
(name STRING, ssn INT MASKED ssn_mask); - D. CREATE TABLE users
(name STRING, int STRING);
ALTER TABLE users ALTER COLUMN ssn CREATE MASK if is_member('HRAdminGroup');
Correct Answer: B 🗳️
Explanation: Only visible for Prep4pass members. You can sign-up / login (it's free).
Where in the Spark UI can one diagnose a performance problem induced by not leveraging predicate push-down?
- A. In the Executor's log file, by gripping for "predicate push-down"
- B. In the Delta Lake transaction log. by noting the column statistics
- C. In the Query Detail screen, by interpreting the Physical Plan
- D. In the Stage's Detail screen, in the Completed Stages table, by noting the size of data read from the Input column
- E. In the Storage Detail screen, by noting which RDDs are not stored on disk
Correct Answer: C 🗳️
Explanation: Only visible for Prep4pass members. You can sign-up / login (it's free).
Which statement describes the correct use of pyspark.sql.functions.broadcast?
- A. It caches a copy of the indicated table on all nodes in the cluster for use in all future queries during the cluster lifetime.
- B. It marks a column as small enough to store in memory on all executors, allowing a broadcast join.
- C. It caches a copy of the indicated table on attached storage volumes for all active clusters within a Databricks workspace.
- D. It marks a DataFrame as small enough to store in memory on all executors, allowing a broadcast join.
- E. It marks a column as having low enough cardinality to properly map distinct values to available partitions, allowing a broadcast join.
Correct Answer: D 🗳️
Explanation: Only visible for Prep4pass members. You can sign-up / login (it's free).
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