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

Section	Objectives
Topic 1: Production Pipelines and Orchestration	- Build and manage workflows using Databricks Jobs - Automate ETL pipelines and scheduling - Pipeline reliability and fault tolerance
Topic 2: Security, Governance, Monitoring, and Optimization	- Implement Unity Catalog governance and access control - Monitor and optimize Spark workloads - Cost optimization and performance tuning
Topic 3: Data Modeling and Storage	- Design scalable data lakehouse architectures - Schema evolution and data partitioning strategies - Delta Lake table design and optimization
Topic 4: Data Ingestion and Transformation	- Ingest data using Apache Spark and Databricks - Transform and clean datasets using Spark SQL and DataFrame APIs - Handle batch and streaming data pipelines

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q121-Q126):

NEW QUESTION # 121
What is the top-level object in unity catalog?

A. Catalog
B. Workspace
C. Table
D. Metastore
E. Database

Answer: D

Explanation:
Explanation
Key concepts - Azure Databricks | Microsoft Docs

Diagram Description automatically generated

NEW QUESTION # 122
A data engineer needs to capture pipeline settings from an existing in the workspace, and use them to create and version a JSON file to create a new pipeline.
Which command should the data engineer enter in a web terminal configured with the Databricks CLI?

A. Stop the existing pipeline; use the returned settings in a reset command
B. Use the alone command to create a copy of an existing pipeline; use the get JSON command to get the pipeline definition; save this to git
C. Use the get command to capture the settings for the existing pipeline; remove the pipeline_id and rename the pipeline; use this in a create command
D. Use list pipelines to get the specs for all pipelines; get the pipeline spec from the return results parse and use this to create a pipeline

Answer: C

Explanation:
The Databricks CLI provides a way to automate interactions with Databricks services. When dealing with pipelines, you can use the databricks pipelines get --pipeline-id command to capture the settings of an existing pipeline in JSON format. This JSON can then be modified by removing the pipeline_id to prevent conflicts and renaming the pipeline to create a new pipeline. The modified JSON file can then be used with the databricks pipelines create command to create a new pipeline with those settings.
:
Databricks Documentation on CLI for Pipelines: Databricks CLI - Pipelines

NEW QUESTION # 123
Which of the following Structured Streaming queries is performing a hop from a bronze table to a Silver 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").agg(sum("sales"),sum("units"))
2..writeStream
3..option("checkpointLocation",checkpointPath)
4..outputMode("complete")
5..table("aggregatedSales"))
C. 1.(spark.read.load(rawSalesLocation)
2..writeStream
3..option("checkpointLocation", checkpointPath)
4..outputMode("append")
5..table("uncleanedSales") )
D. 1.(spark.table("sales").groupBy("store")
2..agg(sum("sales")).writeStream
3..option("checkpointLocation",checkpointPath)
4..outputMode("complete")
5..table("aggregatedSales"))
E. 1.(spark.readStream.load(rawSalesLocation)
2..writeStream
3..option("checkpointLocation", checkpointPath)
4..outputMode("append")
5..table("uncleanedSales") )

Answer: A

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

NEW QUESTION # 124
You noticed that a team member started using an all-purpose cluster to develop a notebook and used the same all-purpose cluster to set up a job that can run every 30 mins so they can update un-derlying tables which are used in a dashboard. What would you recommend for reducing the overall cost of this approach?

A. Change the cluster mode from all-purpose to single-mode
B. Reduce the size of the cluster
C. Reduce the number of nodes and enable auto scale
D. Change the cluster all-purpose to job cluster when scheduling the job
E. Enable auto termination after 30 mins

Answer: D

Explanation:
Explanation
While using an all-purpose cluster is ok during development but anytime you don't need to interact with a notebook, especially for a scheduled job it is less expensive to use a job cluster. Using an all-purpose cluster can be twice as expensive as a job cluster.
Please note: The compute cost you pay the cloud provider for the same cluster type and size be-tween an all-purpose cluster and job cluster is the same the only difference is the DBU cost.
The total cost of cluster = Total cost of VM compute(Azure or AWS or GCP) + Cost per DBU The per DBU cost varies between all-purpose and Job Cluster Here is the recent cost estimate from AWS between Jobs Cluster and all-purpose Cluster, for jobs compute its
$0.15 cents per DBU v$0.55 cents per DBU for all-purpose
Graphical user interface Description automatically generated

How do I check how much the DBU cost for my cluster?
When you click on an exister cluster or when you look at the cluster details you will see this in the top right corner Graphical user interface, text, application, email Description automatically generated

NEW QUESTION # 125
You were asked to create a table that can store the below data, orderTime is a timestamp but the finance team when they query this data normally prefer the orderTime in date format, you would like to create a calculated column that can convert the orderTime column timestamp datatype to date and store it, fill in the blank to complete the DDL.

A. AS DEFAULT (CAST(orderTime as DATE))
B. GENERATED ALWAYS AS (CAST(orderTime as DATE))
Correct)
C. GENERATED DEFAULT AS (CAST(orderTime as DATE))
D. Delta lake does not support calculated columns, value should be inserted into the table as part of the ingestion process
E. AS (CAST(orderTime as DATE))

Answer: B

Explanation:
Explanation
The answer is, GENERATED ALWAYS AS (CAST(orderTime as DATE))
https://docs.microsoft.com/en-us/azure/databricks/delta/delta-batch#--use-generated-columns Delta Lake supports generated columns which are a special type of columns whose values are au-tomatically generated based on a user-specified function over other columns in the Delta table. When you write to a table with generated columns and you do not explicitly provide values for them, Delta Lake automatically computes the values.
Note: Databricks also supports partitioning using generated column

NEW QUESTION # 126
......

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