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To prepare for the DCPDE exam, candidates should have a solid understanding of data engineering concepts, such as data modeling, data integration, data transformation, and data quality. They should also have experience working with big data technologies, such as Apache Spark, Apache Kafka, and Apache Hadoop.

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q49-Q54):

NEW QUESTION # 49
A newly joined team member John Smith in the Marketing team who currently does not have any access to the data requires read access to customers table, which of the following statements can be used to grant access.

A. GRANT SELECT, USAGE TO john.smith@marketing.com ON TABLE customers
B. GRANT READ, USAGE TO john.smith@marketing.com ON TABLE customers
C. GRANT SELECT, USAGE ON TABLE customers TO john.smith@marketing.com
D. GRANT READ, USAGE ON customers TO john.smith@marketing.com
E. GRANT READ, USAGE ON TABLE customers TO john.smith@marketing.com

Answer: C

Explanation:
Explanation
The answer is GRANT SELECT, USAGE ON TABLE customers TO john.smith@marketing.com Data object privileges - Azure Databricks | Microsoft Docs

NEW QUESTION # 50
A data architect has determined that a table of the following format is necessary:
Which of the following code blocks uses SQL DDL commands to create an empty Delta table in the above
format regardless of whether a table already exists with this name?

A. 1. CREATE TABLE IF NOT EXISTS table_name ( id STRING, birthDate DATE, avgRating FLOAT )
B. 1. CREATE OR REPLACE TABLE table_name
2. WITH COLUMNS ( id STRING, birthDate DATE, avgRating FLOAT ) USING DELTA
C. 1. CREATE OR REPLACE TABLE table_name ( id STRING, birthDate DATE, avgRating FLOAT )
D. 1. CREATE OR REPLACE TABLE table_name AS
2. SELECT id STRING, birthDate DATE, avgRating FLOAT USING DELTA
E. 1. CREATE TABLE table_name AS
2. SELECT id STRING, birthDate DATE, avgRating FLOAT

Answer: C

NEW QUESTION # 51
The DevOps team has configured a production workload as a collection of notebooks scheduled to run daily using the Jobs UI. A new data engineering hire is onboarding to the team and has requested access to one of these notebooks to review the production logic.
What are the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data?

A. Can Read
B. Can Manage
C. No permissions
D. Can Run
E. Can Edit

Answer: A

Explanation:
Explanation
This is the correct answer because it is the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data. Notebook permissions are used to control access to notebooks in Databricks workspaces. There are four types of notebook permissions: Can Manage, Can Edit, Can Run, and Can Read. Can Manage allows full control over the notebook, including editing, running, deleting, exporting, and changing permissions. Can Edit allows modifying and running the notebook, but not changing permissions or deleting it. Can Run allows executing commands in an existing cluster attached to the notebook, but not modifying or exporting it. Can Read allows viewing the notebook content, but not running or modifying it. In this case, granting Can Read permission to the user will allow them to review the production logic in the notebook without allowing them to makeany changes to it or run any commands that may affect production data. Verified References: [Databricks Certified Data Engineer Professional], under "Databricks Workspace" section; Databricks Documentation, under "Notebook permissions" section.

NEW QUESTION # 52
Which statement characterizes the general programming model used by Spark Structured Streaming?

A. Structured Streaming models new data arriving in a data stream as new rows appended to an unbounded table.
B. Structured Streaming relies on a distributed network of nodes that hold incremental state values for cached stages.
C. Structured Streaming uses specialized hardware and I/O streams to achieve sub-second latency for data transfer.
D. Structured Streaming is implemented as a messaging bus and is derived from Apache Kafka.
E. Structured Streaming leverages the parallel processing of GPUs to achieve highly parallel data throughput.

Answer: A

Explanation:
Explanation
This is the correct answer because it characterizes the general programming model used by Spark Structured Streaming, which is to treat a live data stream as a table that is being continuously appended. This leads to a new stream processing model that is very similar to a batch processing model, where users can express their streaming computation using the same Dataset/DataFrame API as they would use for static data. The Spark SQL engine will take care of running the streaming query incrementally and continuously and updating the final result as streaming data continues to arrive. Verified References: [Databricks Certified Data Engineer Professional], under "Structured Streaming" section; Databricks Documentation, under "Overview" section.

NEW QUESTION # 53
A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then
perform a streaming write into a new table. The code block used by the data engineer is below:
1. (spark.table("sales")
2. .withColumn("avg_price", col("sales") / col("units"))
3. .writeStream
4. .option("checkpointLocation", checkpointPath)
5. .outputMode("complete")
6. ._____
7. .table("new_sales")
8.)
If the data engineer only wants the query to execute a single micro-batch to process all of the available data,
which of the following lines of code should the data engineer use to fill in the blank?

A. .trigger(continuous="once")
B. .trigger(processingTime="once")
C. .processingTime(1)
D. .processingTime("once")
E. .trigger(once=True)

Answer: E

NEW QUESTION # 54
......

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