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

Section	Objectives
Databricks Lakehouse Platform Architecture	- Medallion architecture (Bronze, Silver, Gold) - Data governance concepts (Unity Catalog basics) - Workspace and cluster architecture
Data Modeling and Transformation	- Dimensional modeling concepts - Performance optimization techniques - Spark SQL transformations
Production Pipelines and Orchestration	- Error handling and recovery strategies - Databricks Workflows - Job scheduling and monitoring
Delta Lake and Data Management	- Delta Lake transactions and ACID properties - Time travel and versioning - Schema evolution and enforcement
Data Ingestion and Processing	- Batch and streaming ingestion with Auto Loader - ETL pipeline design patterns - Structured Streaming fundamentals

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q144-Q149):

NEW QUESTION # 144
A data engineer is performing a join operating to combine values from a static userlookup table with a streaming DataFrame streamingDF.
Which code block attempts to perform an invalid stream-static join?

A. userLookup.join(streamingDF, ["user_id"], how="right")
B. userLookup.join(streamingDF, ["userid"], how="inner")
C. streamingDF.join(userLookup, ["user_id"], how="outer")
D. streamingDF.join(userLookup, ["userid"], how="inner")
E. streamingDF.join(userLookup, ["user_id"], how="left")

Answer: C

Explanation:
https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#support- matrix-for-joins-in-streaming-queries

NEW QUESTION # 145
A data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs. A DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens.
Which statement describes the contents of the workspace audit logs concerning these events?

A. Because User A created the jobs, their identity will be associated with both the job creation Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from events and the job run events.
B. Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events.
C. Because User B last configured the jobs, their identity will be associated with both the job creation events and the job run events.
D. Because the REST API was used for job creation and triggering runs, user identity will not be captured in the audit logs.
E. Because the REST API was used for job creation and triggering runs, a Service Principal will be automatically used to identity these events.

Answer: B

Explanation:
The events are that a data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs, and a DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens. The workspace audit logs are logs that record user activities in a Databricks workspace, such as creating, updating, or deleting objects like clusters, jobs, notebooks, or tables. The workspace audit logs also capture the identity of the user who performed each activity, as well as the time and details of the activity.
Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events in the workspace audit logs.

NEW QUESTION # 146
The Databricks workspace administrator has configured interactive clusters for each of the data engineering groups. To control costs, clusters are set to terminate after 30 minutes of inactivity.
Each user should be able to execute workloads against their assigned clusters at any time of the day.
Assuming users have been added to a workspace but not granted any permissions, which of the following describes the minimal permissions a user would need to start and attach to an already configured cluster.

A. Cluster creation allowed. "Can Restart" privileges on the required cluster
B. "Can Restart" privileges on the required cluster
C. "Can Manage" privileges on the required cluster
D. Cluster creation allowed. "Can Attach To" privileges on the required cluster
E. Workspace Admin privileges, cluster creation allowed. "Can Attach To" privileges on the required cluster

Answer: B

Explanation:
https://learn.microsoft.com/en-us/azure/databricks/security/auth-authz/access-control/cluster-acl
https://docs.databricks.com/en/security/auth-authz/access-control/cluster-acl.html Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

NEW QUESTION # 147
Each configuration below is identical to the extent that each cluster has 400 GB total of RAM, 160 total cores and only one Executor per VM.
Given a job with at least one wide transformation, which of the following cluster configurations will result in maximum performance?

A. Total VMs: 2
200 GB per Executor
80 Cores / Executor
B. Total VMs: 8
50 GB per Executor
20 Cores / Executor
C. Total VMs: 1
400 GB per Executor
160 Cores / Executor
D. Total VMs: 4
100 GB per Executor
40 Cores/Executor

Answer: C

Explanation:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from
https://docs.databricks.com/en/clusters/cluster-config-best-practices.html

NEW QUESTION # 148
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. Run the notebook on a single node cluster to keep driver from falling.
D. Check into the Spark UI to see how many jobs are assigned to each stage as they are employing fewer executors.

Answer: B

Explanation:
Calling df.collect() on a large DataFrame forces all data to be loaded into the driver's memory.
With wide transformations and a cross join, this can easily exceed the driver's capacity, causing it to crash. The data engineer should rewrite the code to avoid collecting large datasets on the driver, using operations like display(df) or writing to storage instead.

NEW QUESTION # 149
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