Databricks-Certified-Professional-Data-Engineer Exam Study Solutions | Databricks-Certified-Professional-Data-Engineer Valid Test Topics

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

Section	Weight	Objectives
Data Ingestion	15-20%	- Batch ingestion methods 1. Spark APIs for ingestion 2. DBR autoloader 3. Integration with external systems - Streaming ingestion 1. Kafka integration 2. Structured streaming fundamentals
Data Warehouse and Lakehouse Architecture	15-20%	- Lakehouse architecture principles 1. Bronze, silver, gold data layers 2. Data governance fundamentals 3. Differences between data lake, data warehouse, and lakehouse
Delta Lake	20-25%	- Delta Lake operations 1. Delta Live Tables 2. Merge, update, delete operations 3. Schema evolution and enforcement - Delta Lake fundamentals 1. Optimize and Z-order 2. Time travel and data versioning 3. ACID transactions
Pipeline Development and Orchestration	10-15%	- Databricks workflows 1. Task dependencies and orchestration 2. Monitoring and alerting 3. Jobs and job scheduling
Data Processing with Spark	25-30%	- Python and SQL for data engineering 1. Performance optimization techniques 2. Built-in and user-defined functions 3. Spark APIs in Python - Spark DataFrames and Spark SQL 1. DataFrame operations and transformations 2. Spark SQL queries and functions 3. Window functions

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q146-Q151):

NEW QUESTION # 146
A Structured Streaming job deployed to production has been resulting in higher than expected cloud storage costs. At present, during normal execution, each micro-batch of data is processed in less than 3 seconds; at least 12 times per minute, a micro-batch is processed that contains 0 records. The streaming write was configured using the default trigger settings. The production job is currently scheduled alongside many other Databricks jobs in a workspace with instance pools provisioned to reduce start-up time for jobs with batch execution. Holding all other variables constant and assuming records need to be processed in less than 10 minutes, which adjustment will meet the requirement?

A. Set the trigger interval to 500 milliseconds; setting a small but non-zero trigger interval ensures that the source is not queried too frequently.
B. Set the trigger interval to 3 seconds; the default trigger interval is consuming too many records per batch, resulting in spill to disk that can increase volume costs.
C. Use the trigger once option and configure a Databricks job to execute the query every 10 minutes; this approach minimizes costs for both compute and storage.
D. Set the trigger interval to 10 minutes; each batch calls APIs in the source storage account, so decreasing trigger frequency to the maximum allowable threshold should minimize this cost.

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
* Exact extract: "If no trigger is specified, the default processing-time trigger runs micro-batches as fast as possible."
* Exact extract: "Trigger once processes all available data once and then stops." References: Structured Streaming triggers; Databricks Jobs and job clusters.

NEW QUESTION # 147
A data engineer wants to automate job monitoring and recovery in Databricks using the Jobs API. They need to list all jobs, identify a failed job, and rerun it.
Which sequence of API actions should the data engineer perform?

A. Use the jobs/list endpoint to list jobs, then use the jobs/create endpoint to create a new job, and run the new job using jobs/run-now.
B. Use the jobs/get endpoint to retrieve job details, then use jobs/update to rerun failed jobs.
C. Use the jobs/list endpoint to list jobs, check job run statuses with jobs/runs/list, and rerun a failed job using jobs/run-now.
D. Use the jobs/cancel endpoint to remove failed jobs, then recreate them with jobs/create and run the new ones.

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
The Databricks Jobs REST API provides several endpoints for automation. The correct monitoring and rerun flow uses three specific calls:
GET /api/2.1/jobs/list - Lists all available jobs within the workspace.
GET /api/2.1/jobs/runs/list - Returns all runs for a specific job, including their current state (e.g., TERMINATED: FAILED).
POST /api/2.1/jobs/run-now - Immediately triggers a rerun of the specified job.
This sequence aligns with Databricks' prescribed automation model for job observability and recovery. Using jobs/update modifies metadata but does not rerun jobs, and jobs/create is only used for creating new jobs, not rerunning failed ones. Cancelling and recreating jobs introduces unnecessary duplication. Therefore, option A is the correct automated recovery workflow.

NEW QUESTION # 148
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. Executor's log file
B. Ganglia
C. Cluster Event Log
D. Workspace audit logs
E. Driver's log file

Answer: B

NEW QUESTION # 149
Which statement describes the correct use of pyspark.sql.functions.broadcast?

A. It caches a copy of the indicated table on attached storage volumes for all active clusters within a Databricks workspace.
B. It caches a copy of the indicated table on all nodes in the cluster for use in all future queries during the cluster lifetime.
C. It marks a column as small enough to store in memory on all executors, allowing a broadcast join.
D. It marks a column as having low enough cardinality to properly map distinct values to available partitions, allowing a broadcast join.
E. It marks a DataFrame as small enough to store in memory on all executors, allowing a broadcast join.

Answer: E

Explanation:
Explanation
https://spark.apache.org/docs/3.1.3/api/python/reference/api/pyspark.sql.functions.broadcast.html

NEW QUESTION # 150
A DLT pipeline includes the following streaming tables:
Raw_lot ingest raw device measurement data from a heart rate tracking device.
Bgm_stats incrementally computes user statistics based on BPM measurements from raw_lot.
How can the data engineer configure this pipeline to be able to retain manually deleted or updated records in the raw_iot table while recomputing the downstream table when a pipeline update is run?

A. Set the pipelines, reset, allowed property to false on raw_iot
B. Set the skipChangeCommits flag to true on bpm_stats
C. Set the pipelines, reset, allowed property to false on bpm_stats
D. Set the SkipChangeCommits flag to true raw_lot

Answer: A

Explanation:
In Databricks Lakehouse, to retain manually deleted or updated records in theraw_iottable while recomputing downstream tables when a pipeline update is run, the propertypipelines.reset.allowedshould be set tofalse.
This property prevents the system from resetting the state of the table, which includes the removal of the history of changes, during a pipeline update. By keeping this property as false, any changes to theraw_iot table, including manual deletes or updates, are retained, and recomputation of downstream tables, such as bpm_stats, can occur with the full history of data changes intact.
References:
* Databricks documentation on DLT pipelines: https://docs.databricks.com/data-engineering/delta-live- tables/delta-live-tables-overview.html

NEW QUESTION # 151
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