Databricks-Certified-Professional-Data-Engineer Ressourcen Prüfung - Databricks-Certified-Professional-Data-Engineer Prüfungsguide & Databricks-Certified-Professional-Data-Engineer Beste Fragen

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Databricks Databricks-Certified-Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Monitoring and Alerting	10%	- Performance and health monitoring - Pipeline observability and logging - Setting up alerts and notifications
Debugging and Deploying	10%	- CI/CD and DevOps practices - Deployment using bundles, CLI, and APIs - Troubleshooting pipelines and errors
Data Transformation, Cleansing, and Quality	10%	- Standardization and normalization - Data validation and quality checks - Handling missing or inconsistent data
Data Governance	7%	- Policy enforcement - Unity Catalog management - Data lineage and metadata tracking
Data Modelling	6%	- Delta Lake table design - Medallion Architecture implementation - Schema design and management
Data Sharing and Federation	5%	- Cross-workspace and cross-cloud access - Unity Catalog data sharing
Developing Code for Data Processing using Python and SQL	22%	- Batch and incremental processing logic - Data transformation and aggregation - Integration with Databricks APIs and tools
Data Ingestion & Acquisition	7%	- Connecting to diverse data sources - Schema inference and evolution - Auto Loader and streaming ingestion
Ensuring Data Security and Compliance	10%	- Compliance standards implementation - Data encryption and masking - Access control and permissions
Cost & Performance Optimisation	13%	- Query optimization and caching - Storage optimization (partitioning, Z-order, indexing) - Cluster configuration and scaling

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Databricks Certified Professional Data Engineer Exam Databricks-Certified-Professional-Data-Engineer Prüfungsfragen mit Lösungen (Q215-Q220):

215. Frage
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 average temperature recordings for at least one sensor exceeded 120 on three consecutive executions of the query
B. The total average temperature across all sensors exceeded 120 on three consecutive executions of the query
C. The source query failed to update properly for three consecutive minutes and then restarted
D. The maximum temperature recording 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

Antwort: A

Begründung:
This is the correct answer because the query is using a GROUP BY clause on the sensor_id column, which means it will calculate the mean temperature for each sensor separately. The alert will trigger when the mean temperature for any sensor is greater than 120, which means at least one sensor had an average temperature above 120 for three consecutive minutes. The alert will stop when the mean temperature for all sensors drops below 120. Verified References: [Databricks Certified Data Engineer Professional], under "SQL Analytics" section; Databricks Documentation, under "Alerts" section.

216. Frage
What steps need to be taken to set up a DELTA LIVE PIPELINE as a job using the workspace UI?

A. Use Pipeline creation UI, select a new pipeline and job cluster
B. DELTA LIVE TABLES do not support job cluster
C. Select Workflows UI and Delta live tables tab, under task type select Delta live tables pipeline and select the notebook
D. Select Workflows UI and Delta live tables tab, under task type select Delta live tables pipeline and select the pipeline JSON file

Antwort: C

Begründung:
Explanation
The answer is,
Select Workflows UI and Delta live tables tab, under task type select Delta live tables pipeline and select the notebook.
Create a pipeline
To create a new pipeline using the Delta Live Tables notebook:
1.Click Workflows in the sidebar, click the Delta Live Tables tab, and click Create Pipeline.
2.Give the pipeline a name and click to select a notebook.
3.Optionally enter a storage location for output data from the pipeline. The system uses a de-fault location if you leave Storage Location empty.
4.Select Triggered for Pipeline Mode.
5.Click Create.
The system displays the Pipeline Details page after you click Create. You can also access your pipeline by clicking the pipeline name in the Delta Live Tables tab.

217. Frage
The data governance team is reviewing code used for deleting records for compliance with GDPR. They note the following logic is used to delete records from the Delta Lake table named users .

Assuming that user_id is a unique identifying key and that delete_requests contains all users that have requested deletion, which statement describes whether successfully executing the above logic guarantees that the records to be deleted are no longer accessible and why?

A. No; files containing deleted records may still be accessible with time travel until a vacuum command is used to remove invalidated data files.
B. Yes; Delta Lake ACID guarantees provide assurance that the delete command succeeded fully and permanently purged these records.
C. Yes; the Delta cache immediately updates to reflect the latest data files recorded to disk.
D. No; the Delta cache may return records from previous versions of the table until the cluster is restarted.
E. No; the Delta Lake delete command only provides ACID guarantees when combined with the merge into command.

Antwort: A

Begründung:
The code uses the DELETE FROM command to delete records from the users table that match a condition based on a join with another table called delete_requests, which contains all users that have requested deletion. The DELETE FROM command deletes records from a Delta Lake table by creating a new version of the table that does not contain the deleted records. However, this does not guarantee that the records to be deleted are no longer accessible, because Delta Lake supports time travel, which allows querying previous versions of the table using a timestamp or version number. Therefore, files containing deleted records may still be accessible with time travel until a vacuum command is used to remove invalidated data files from physical storage. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Delete from a table" section; Databricks Documentation, under
"Remove files no longer referenced by a Delta table" section.

218. Frage
What is the underlying technology that makes the Auto Loader work?

A. Loader
B. DataFrames
C. Structured Streaming
D. Live DataFames
E. Delta Live Tables

Antwort: C

219. Frage
A data architect has designed a system in which two Structured Streaming jobs will concurrently write to a single bronze Delta table. Each job is subscribing to a different topic from an Apache Kafka source, but they will write data with the same schema. To keep the directory structure simple, a data engineer has decided to nest a checkpoint directory to be shared by both streams.
The proposed directory structure is displayed below:

Which statement describes whether this checkpoint directory structure is valid for the given scenario and why?

A. No; only one stream can write to a Delta Lake table.
B. Yes; both of the streams can share a single checkpoint directory.
C. No; Delta Lake manages streaming checkpoints in the transaction log.
D. No; each of the streams needs to have its own checkpoint directory.
E. Yes; Delta Lake supports infinite concurrent writers.

Antwort: D

Begründung:
This is the correct answer because checkpointing is a critical feature of Structured Streaming that provides fault tolerance and recovery in case of failures. Checkpointing stores the current state and progress of a streaming query in a reliable storage system, such as DBFS or S3. Each streaming query must have its own checkpoint directory that is unique and exclusive to that query. If two streaming queries share the same checkpoint directory, they will interfere with each other and cause unexpected errors or data loss. Verified References: [Databricks Certified Data Engineer Professional], under "Structured Streaming" section; Databricks Documentation , under "Checkpointing" section.

220. Frage
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