Databricks-Certified-Data-Engineer-Professional Valid Exam Notes | Free Databricks-Certified-Data-Engineer-Professional Study Material

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

Section	Weight	Objectives
Topic 1: Data Processing	28%	- ETL Pipelines - Spark SQL - Data Transformation - Structured Streaming
Topic 2: Data Quality and Governance	12%	- Data Lineage - Data Quality - Governance
Topic 3: Databricks Lakehouse Platform	24%	- Data Management - Unity Catalog - Lakehouse Architecture - Delta Lake
Topic 4: Data Modeling and Storage	20%	- Data Modeling - Storage Optimization - File Formats
Topic 5: Monitoring and Troubleshooting	16%	- Performance Optimization - Troubleshooting - Monitoring

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q30-Q35):

NEW QUESTION # 30
A distributed team of data analysts share computing resources on an interactive cluster with autoscaling configured. In order to better manage costs and query throughput, the workspace administrator is hoping to evaluate whether cluster upscaling is caused by many concurrent users or resource-intensive queries.
In which location can one review the timeline for cluster resizing events?

A. Workspace audit logs
B. Executor's log file
C. Driver's log file
D. Ganglia
E. Cluster Event Log

Answer: E

Explanation:
The Cluster Event Log in Databricks will show the timeline for cluster resizing events, including details about when and why a cluster was resized (scaled up or down). This log would help the workspace administrator determine the causes of cluster scaling, whether due to many concurrent users submitting jobs or a few users running resource-intensive queries.

NEW QUESTION # 31
In order to facilitate near real-time workloads, a data engineer is creating a helper function to leverage the schema detection and evolution functionality of Databricks Auto Loader. The desired function will automatically detect the schema of the source directly, incrementally process JSON files as they arrive in a source directory, and automatically evolve the schema of the table when new fields are detected.
The function is displayed below with a blank:

Which response correctly fills in the blank to meet the specified requirements?

Answer: D

Explanation:
https://docs.databricks.com/en/ingestion/auto-loader/schema.html

NEW QUESTION # 32
A new data engineer notices that a critical field was omitted from an application that writes its Kafka source to Delta Lake. This happened even though the critical field was in the Kafka source.
That field was further missing from data written to dependent, long-term storage. The retention threshold on the Kafka service is seven days. The pipeline has been in production for three months.
Which describes how Delta Lake can help to avoid data loss of this nature in the future?

A. The Delta log and Structured Streaming checkpoints record the full history of the Kafka producer.
B. Delta Lake schema evolution can retroactively calculate the correct value for newly added fields, as long as the data was in the original source.
C. Ingestine all raw data and metadata from Kafka to a bronze Delta table creates a permanent, replayable history of the data state.Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from
D. Delta Lake automatically checks that all fields present in the source data are included in the ingestion layer.
E. Data can never be permanently dropped or deleted from Delta Lake, so data loss is not possible under any circumstance.

Answer: C

Explanation:
This is the correct answer because it describes how Delta Lake can help to avoid data loss of this nature in the future. By ingesting all raw data and metadata from Kafka to a bronze Delta table, Delta Lake creates a permanent, replayable history of the data state that can be used for recovery or reprocessing in case of errors or omissions in downstream applications or pipelines.
Delta Lake also supports schema evolution, which allows adding new columns to existing tables without affecting existing queries or pipelines. Therefore, if a critical field was omitted from an application that writes its Kafka source to Delta Lake, it can be easily added later and the data can be reprocessed from the bronze table without losing any information.

NEW QUESTION # 33
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 recent_sensor_recordingstable was unresponsive for three consecutive runs of the query
E. The maximum temperature recording for at least one sensor exceeded 120 on three consecutive executions of the query

Answer: A

Explanation:
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.

NEW QUESTION # 34
Which statement describes Delta Lake optimized writes?

A. An asynchronous job runs after the write completes to detect if files could be further compacted; yes, an OPTIMIZE job is executed toward a default of 1 GB.
B. A shuffle occurs prior to writing to try to group data together resulting in fewer files instead of each executor writing multiple files based on directory partitions.
C. Optimized writes logical partitions instead of directory partitions partition boundaries are only Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from represented in metadata fewer small files are written.
D. Before a job cluster terminates, OPTIMIZE is executed on all tables modified during the most recent job.

Answer: B

Explanation:
Delta Lake optimized writes involve a shuffle operation before writing out data to the Delta table.
The shuffle operation groups data by partition keys, which can lead to a reduction in the number of output files and potentially larger files, instead of multiple smaller files. This approach can significantly reduce the total number of files in the table, improve read performance by reducing the metadata overhead, and optimize the table storage layout, especially for workloads with many small files.

NEW QUESTION # 35
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