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The Databricks Databricks-Certified-Professional-Data-Engineer exam consists of multiple-choice questions and hands-on tasks that test the candidate's practical knowledge of Databricks. Databricks-Certified-Professional-Data-Engineer exam covers a wide range of topics such as data engineering, data processing, ETL, data modeling, data warehousing, data governance, and data security. Databricks-Certified-Professional-Data-Engineer exam is designed to evaluate the candidate's ability to design and implement scalable data pipelines using Databricks.

Databricks Certified Professional Data Engineer exam covers a wide range of topics, such as data ingestion, transformation, storage, and processing. Databricks-Certified-Professional-Data-Engineer Exam Tests the candidates' ability to use Databricks tools and technologies to solve real-world problems and challenges. Candidates who pass Databricks-Certified-Professional-Data-Engineer exam demonstrate their proficiency in designing, building, and managing data pipelines with Databricks, which is a leading cloud-based platform for big data processing and analytics.

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q211-Q216):

NEW QUESTION # 211
A particular job seems to be performing slower and slower over time, the team thinks this started to happen when a recent production change was implemented, you were asked to take look at the job history and see if we can identify trends and root cause, where in the workspace UI can you perform this analysis?

A. Under jobs UI select the job you are interested, under runs we can see current active runs and last 60 days historical run
B. Under jobs UI select the job cluster, under spark UI select the application job logs, then you can access last 60 day historical runs
C. Historical job runs can only be accessed by REST API
D. Under Compute UI, select Job cluster and select the job cluster to see last 60 day his-torical runs
E. Under Workspace logs, select job logs and select the job you want to monitor to view the last 60 day historical runs

Answer: A

Explanation:
Explanation
The answer is,
Under jobs UI select the job you are interested, under runs we can see current active runs and last 60 days historical run

NEW QUESTION # 212
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. Driver's and Executor's log files
B. Stage's detail screen and Query's detail screen
C. Stage's detail screen and Executor's files
D. Executor's detail screen and Executor's log files

Answer: B

Explanation:
In Apache Spark's UI, indicators of data spilling to disk during the execution of wide transformations can be found in the Stage's detail screen and the Query's detail screen. These screens provide detailed metrics about each stage of a Spark job, including information about memory usage and spill data. If a task is spilling data to disk, it indicates that the data being processed exceeds the available memory, causing Spark to spill data to disk to free up memory. This is an important performance metric as excessive spill can significantly slow down the processing.
Reference:
Apache Spark Monitoring and Instrumentation: Spark Monitoring Guide
Spark UI Explained: Spark UI Documentation

NEW QUESTION # 213
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. streamingDF.join(userLookup, ["userid"], how="inner")
B. streamingDF.join(userLookup, ["user_id"], how="left")
C. streamingDF.join(userLookup, ["user_id"], how="outer")
D. userLookup.join(streamingDF, ["userid"], how="inner")
E. userLookup.join(streamingDF, ["user_id"], how="right")

Answer: E

Explanation:
In Spark Structured Streaming, certain types of joins between a static DataFrame and a streaming DataFrame are not supported. Specifically, a right outer join where the static DataFrame is on the left side and the streaming DataFrame is on the right side is not valid. This is because Spark Structured Streaming cannot handle scenarios where it has to wait for new rows to arrive in the streaming DataFrame to match rows in the static DataFrame. The other join types listed (inner, left, and full outer joins) are supported in streaming-static DataFrame joins.
:
Structured Streaming Programming Guide: Join Operations
Databricks Documentation on Stream-Static Joins: Databricks Stream-Static Joins

NEW QUESTION # 214
Which of the following Structured Streaming queries is performing a hop from a bronze table to a Silver table?

A. 1.(spark.readStream.load(rawSalesLocation)
2..writeStream
3..option("checkpointLocation", checkpointPath)
4..outputMode("append")
5..table("uncleanedSales") )
B. 1.(spark.read.load(rawSalesLocation)
2..writeStream
3..option("checkpointLocation", checkpointPath)
4..outputMode("append")
5..table("uncleanedSales") )
C. 1.(spark.table("sales").agg(sum("sales"),sum("units"))
2..writeStream
3..option("checkpointLocation",checkpointPath)
4..outputMode("complete")
5..table("aggregatedSales"))
D. 1.(spark.table("sales")
2..withColumn("avgPrice", col("sales") / col("units"))
3..writeStream
4..option("checkpointLocation", checkpointPath)
5..outputMode("append")
6..table("cleanedSales"))
E. 1.(spark.table("sales").groupBy("store")
2..agg(sum("sales")).writeStream
3..option("checkpointLocation",checkpointPath)
4..outputMode("complete")
5..table("aggregatedSales"))

Answer: D

Explanation:
Explanation
A diagram of a house Description automatically generated with low confidence

NEW QUESTION # 215
A data engineering team has a time-consuming data ingestion job with three data sources. Each notebook takes about one hour to load new data. One day, the job fails because a notebook update introduced a new required configuration parameter. The team must quickly fix the issue and load the latest data from the failing source.
Which action should the team take?

A. Repair the run with the new parameter, and update the task by adding the missing task parameter.
B. Update the task by adding the missing task parameter, and manually run the job.
C. Repair the run with the new parameter.
D. Share the analysis with the failing notebook owner so that they can fix it quickly.

Answer: A

Explanation:
The repair run capability in Databricks Jobs allows re-execution of failed tasks without re-running successful ones. When a parameterized job fails due to missing or incorrect task configuration, engineers can perform a repair run to fix inputs or parameters and resume from the failed state.
This approach saves time, reduces cost, and ensures workflow continuity by avoiding unnecessary recomputation. Additionally, updating the task definition with the missing parameter prevents future runs from failing.
Running the job manually (B) loses run context; (C) alone does not prevent recurrence; (D) delays resolution.
Thus, A follows the correct operational and recovery practice.

NEW QUESTION # 216
......

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