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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Data Ingestion & Acquisition | - Design and implement data ingestion pipelines
|
| Data Governance | - Govern enterprise data
|
| Cost & Performance Optimization | - Optimize cost and performance
|
| Developing Code for Data Processing using Python and SQL | - Building and Testing an ETL Pipeline with Lakeflow Declarative Pipelines, SQL, and Apache Spark
|
| Data Transformation, Cleansing, and Quality | - Transform and validate data
|
| Data Sharing and Federation | - Share and federate data
|
| Debugging and Deploying | - Debugging and Troubleshooting
|
| Data Modeling | - Design and optimize data models
|
| Ensuring Data Security and Compliance | - Applying Data Security Mechanisms
|
| Monitoring and Alerting | - Monitoring
|
Databricks Certified Data Engineer Professional Sample Questions:
Question #1
Spill occurs as a result of executing various wide transformations. However, diagnosing spill requires one to proactively look for key indicators.
Where in the Spark UI are two of the primary indicators that a partition is spilling to disk?
A. Stage's detail screen and Query's detail screen
B. Driver's and Executor's log files
C. Executor's detail screen and Executor's log files
D. Stage's detail screen and Executor's log files
E. Query's detail screen and Job's detail screen
Question #2
An upstream system is emitting change data capture (CDC) logs that are being written to a cloud object storage directory. Each record in the log indicates the change type (insert, update, or delete) and the values for each field after the change. The source table has a primary key identified by the field pk_id.
For analytical purposes, only the most recent value for each record needs to be recorded in the target Delta Lake table in the Lakehouse. The Databricks job to ingest these records occurs once per hour, but each individual record may have changed multiple times over the course of an hour.
Which solution meets these requirements?
A. Deduplicate records in each batch by pk_id and overwrite the target table.
B. Use Delta Lake's change data feed to automatically process CDC data from an external system, propagating all changes to all dependent tables in the Lakehouse.
C. Iterate through an ordered set of changes to the table, applying each in turn to create the current state of the table, (insert, update, delete), timestamp of change, and the values.
D. Use MERGE INTO to insert, update, or delete the most recent entry for each pk_id into a table, then propagate all changes throughout the system.
Question #3
A data engineer wants to refactor the following DLT code, which includes multiple table definitions with very similar code.
In an attempt to programmatically create these tables using a parameterized table definition, the data engineer writes the following code.
The pipeline runs an update with this refactored code, but generates a different DAG showing incorrect configuration values for these tables.
How can the data engineer fix this?
A. Convert the list of configuration values to a dictionary of table settings, using different input the for loop.
B. Load the configuration values for these tables from a separate file, located at a path provided by a pipeline parameter.
C. Convert the list of configuration values to a dictionary of table settings, using table names as keys.
D. Wrap the loop inside another table definition, using generalized names and properties to replace with those from the inner table
Question #4
A data engineer needs to productionize a new Spark application written by teammate. This application has numerous external dependencies, including libraries, and requires custom environment variables and Spark configuration parameters to be set. Which two methods will help the data engineer accomplish the task? (Choose two.)
A. Use secrets in init scripts to store configuration data
B. Create init scripts on DBFS.
C. Use compute policies to set system properties, environment variables, and Spark configuration parameters.
D. Add libraries to compute policies
E. Install libraries on DBFS
Question #5
The data engineering team has configured a Databricks SQL query and alert to monitor the values in a Delta Lake table. The recent_sensor_recordings table contains an identifying sensor_id alongside the timestamp and temperature for the most recent 5 minutes of recordings.
The below query is used to create the alert:
The query is set to refresh each minute and always completes in less than 10 seconds. The alert is set to trigger when mean (temperature) > 120. Notifications are triggered to be sent at most every 1 minute.
If this alert raises notifications for 3 consecutive minutes and then stops, which statement must be true?
A. The source query failed to update properly for three consecutive minutes and then restarted
B. The maximum temperature recording for at least one sensor exceeded 120 on three consecutive executions of the query
C. The total average temperature across all sensors exceeded 120 on three consecutive executions of the query
D. The average temperature recordings for at least one sensor exceeded 120 on three consecutive executions of the query
E. The recent_sensor_recordingstable was unresponsive for three consecutive runs of the query
Solutions:
| Question #1 Correct Answer: D | Question #2 Correct Answer: B | Question #3 Correct Answer: C | Question #4 Correct Answer: B,C | Question #5 Correct Answer: D |
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