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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q172-Q177):

NEW QUESTION # 172
What is the impact of setting the Spark configuration spark.sql.autoBroadcastJoinThreshold to -1?

A. It disables the broadcast join feature, forcing all joins to be shuffled joins.
B. It sets an unlimited threshold for broadcasting tables, which may cause out-of-memory errors.
C. It increases the threshold for choosing which table to broadcast in a join, potentially improving join performance.
D. It automatically selects the optimal threshold for broadcasting based on the cluster's current workload.

Answer: A

Explanation:
The spark.sql.autoBroadcastJoinThreshold configuration parameter in Spark specifies the maximum size (in bytes) of a table that can be broadcast to all worker nodes for a join. Setting this value to -1 effectively disables the broadcast join optimization, meaning that Spark will not attempt to broadcast any table regardless of its size. As a result, all joins will use the shuffle join mechanism, which can be less efficient for joining small tables. Options B, C, and D misinterpret the effect of setting the parameter to 1 ; it does not set an unlimited threshold, automatically adjust the threshold, or increase it to improve performance, but rather disables the broadcast join feature altogether.

NEW QUESTION # 173
You need to migrate a large Hive-managed table into an Iceberg table within CDP while minimizing downtime.
Which strategy would you consider?

A. Create an external Iceberg table pointing to the existing Hive data and then use Iceberg's INSERT OVERWRITE to copy the data.
B. Use the ALTER TABLE statement in Hive to convert the table format to Iceberg.
C. Utilize Iceberg's migration feature to automatically handle the conversion.
D. Export the data from Hive and reload directly into a new Iceberg table.

Answer: A

Explanation:
B). This is the most controlled approach in CDP. It minimizes disruption and lets you verify the Iceberg table before switching over. A. Export/reload is disruptive and less efficient for large tables. C. Hive's ALTER TABLE may not fully support Iceberg conversions depending on the version. D. Iceberg has a migration tool, but it may have limitations for complex tables.

NEW QUESTION # 174
You are writing a PySpark application where you need to collect the final results from various Executors and present them to the user. Which aspect of the Spark Driver's role is primarily involved in this process?

A. Collecting final results from Executors and providing the output.
B. Optimizing resource utilization within the Kubernetes cluster.
C. Managing Kubernetes API interactions for Executor lifecycle.
D. Constructing the logical plan from the user application.

Answer: A

Explanation:
The Spark Driver's role includes collecting the final results of the tasks executed by the Executors. In this scenario, where the PySpark application requires gathering and presenting results, the Driver compiles data from various Executors and provides the final output, fulfilling its responsibility in result collection and output generation.

NEW QUESTION # 175
What are the potential challenges associated with schema inference in data processing pipelines?

A. Inaccuracies in inferred schemas leading to data processing errors
B. Performance overhead due to schema discovery
C. The need for manual schema updates
D. Handling complex nested structures and arrays
E. Increased storage costs for schema metadata

Answer: A,B,D

Explanation:
Schema inference can introduce performance overhead as the system needs to analyze the data to determine its structure. Inaccuracies in the inferred schema may occur, especially with complex data types or when the data does not follow a consistent format, leading to potential errors in data processing. Handling complex nested structures and arrays can also present challenges, as the inference mechanism must correctly identify these elements within the data.

NEW QUESTION # 176
You want to select specific columns from a Spark DataFrame and rename them. How can you achieve this in Spark SQL?

A. Modify the original DataFrame schema directly
B. Use the select() method with column names and aliases within parentheses
C. Use Spark SQL's ALTER TABLE statement to modify the table schema
D. Implement custom logic to iterate through the DataFrame and create a new one

Answer: B

Explanation:
Modifying the original schema B is not recommended as it can affect subsequent operations. Custom logic C is inefficient. Spark SQL's select() method A allows you to specify the columns and assign aliases for renaming, providing a concise and efficient way to achieve the desired outcome.

NEW QUESTION # 177
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CDP-3002 Exam Preview - Customized CDP-3002 Lab Simulation

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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q172-Q177):

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