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The exam is intended for data engineers with experience in designing and implementing data solutions using Databricks. Candidates for this certification should have a good understanding of data engineering concepts, data processing frameworks, and programming languages such as Python and SQL. They should also be familiar with cloud platforms such as AWS, Azure, and Google Cloud Platform.

The Databricks Certified Professional Data Engineer exam consists of multiple-choice questions and is conducted online. The exam is intended to measure the candidate's proficiency in various areas, such as Spark architecture, Spark programming, data processing, data analysis, and data modeling. The exam also tests the candidate's ability to optimize Spark performance and troubleshoot Spark applications. It is recommended that individuals who plan to take this exam have at least two years of hands-on experience in big data technologies and Apache Spark.

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q63-Q68):

NEW QUESTION # 63
Which of the following is true, when building a Databricks SQL dashboard?

A. A dashboard can only have one refresh schedule
B. A dashboard can only connect to one schema/Database
C. Only one visualization can be developed with one query result
D. A dashboard can only use results from one query
E. More than one visualization can be developed using a single query result

Answer: E

Explanation:
Explanation
the answer is, More than one visualization can be developed using a single query result.
In the query editor pane + Add visualization tab can be used for many visualizations for a single query result.
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NEW QUESTION # 64
Which of the following Structured Streaming queries successfully performs a hop from a Silver to Gold table?

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

Answer: B

Explanation:
Explanation
The answer is
1.(spark.table("sales")
2..groupBy("store")
3..agg(sum("sales"))
4..writeStream
5..option("checkpointLocation", checkpointPath)
6..outputMode("complete")
7..table("aggregatedSales") )
The gold layer is normally used to store aggregated data
Review the below link for more info,
Medallion Architecture - Databricks
Gold Layer:
1. Powers Ml applications, reporting, dashboards, ad hoc analytics
2. Refined views of data, typically with aggregations
3. Reduces strain on production systems
4. Optimizes query performance for business-critical data
Exam focus: Please review the below image and understand the role of each layer(bronze, silver, gold) in medallion architecture, you will see varying questions targeting each layer and its purpose.
Sorry I had to add the watermark some people in Udemy are copying my content.
A diagram of a house Description automatically generated with low confidence

NEW QUESTION # 65
In order to use Unity catalog features, which of the following steps needs to be taken on man-aged/external tables in the Databricks workspace?

A. Copy data from workspace to unity catalog
B. Upgrade workspace to Unity catalog
C. Migrate/upgrade objects in workspace managed/external tables/view to unity catalog
D. Upgrade to DBR version 15.0
E. Enable unity catalog feature in workspace settings

Answer: C

Explanation:
Explanation
Upgrade tables and views to Unity Catalog - Azure Databricks | Microsoft Docs Managed table: Upgrade a managed to Unity Catalog External table: Upgrade an external table to Unity Catalog

NEW QUESTION # 66
A data engineer has created a Delta table as part of a data pipeline. Downstream data analysts now need
SELECT permission on the Delta table.
Assuming the data engineer is the Delta table owner, which part of the Databricks Lakehouse Plat-form can
the data engineer use to grant the data analysts the appropriate access?

A. Jobs
B Dashboards
B. Data Explorer
C. Databricks Filesystem
D. Repos

Answer: D

NEW QUESTION # 67
You have written a notebook to generate a summary data set for reporting, Notebook was scheduled using the job cluster, but you realized it takes 8 minutes to start the cluster, what feature can be used to start the cluster in a timely fashion so your job can run immediatley?

A. Disable auto termination so the cluster is always running
B. Setup an additional job to run ahead of the actual job so the cluster is running second job starts
C. Use Databricks Premium edition instead of Databricks standard edition
D. Pin the cluster in the cluster UI page so it is always available to the jobs
E. Use the Databricks cluster pools feature to reduce the startup time

Answer: E

Explanation:
Explanation
Cluster pools allow us to reserve VM's ahead of time, when a new job cluster is created VM are grabbed from the pool. Note: when the VM's are waiting to be used by the cluster only cost incurred is Azure. Databricks run time cost is only billed once VM is allocated to a cluster.
Here is a demo of how to setup a pool and follow some best practices,
Graphical user interface, text Description automatically generated

NEW QUESTION # 68
......

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