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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q154-Q159):

NEW QUESTION # 154
Consider the following code snippet:# Sample DataFrame (assuming it exists) df = spark.createDataFrame(...)
# Attempt to add a new column with a case-when expression (fix the error) df = df.withColumn("category", F.when(df["price"] ] 100, "Expensive").otherwise("Cheap")) df.show() What is the error in this code, and how can it be fixed?

A. The error is using the wrong syntax for case-when expressions. Fix: Use SQL-like syntax with CASE WHEN and END.
B. The error is attempting to modify the original DataFrame in-place. Fix: Use df.withColumn to create a new DataFrame with the added column.
C. There is no error in the code snippet.
D. The error is missing parentheses around the conditions in the when function. Fix: F.when((df["price"] ] 100), "Expensive").otherwise("Cheap")

Answer: B

Explanation:
The error lies in attempting to modify the original DataFrame in-place. The correct approach is to use withColumn to create a new DataFrame with the added column, as shown in the fixed code:df = df.withColumn("category", ] 100, "Expensive").otherwise("Cheap"))

NEW QUESTION # 155
You're integrating data quality checks into a complex ETL pipeline with numerous tasks and dependencies. How can you ensure the checks are executed in the correct order and don't interfere with other pipeline tasks?

A. Utilize Airflow upstream/downstream dependencies to define the execution order between check tasks and other pipeline tasks.
B. Implement a custom script to manage the execution of the data quality checks independently.
C. Run all tasks (ETL and checks) concurrently, assuming they are independent.
D. Schedule the data quality checks as a separate DAG and trigger it after the ETL pipeline completes.

Answer: A

Explanation:
While option A might be a workaround, relying on separate DAGs can lead to management complexity. Option B highlights the importance of using Airflow's dependency features:

NEW QUESTION # 156
What is a primary consideration when deciding to cache data in a distributed computing environment like Apache Spark?

A. The trade-off between memory usage and computational efficiency
B. Ensuring data is encrypted before caching
C. Using disk storage for all cached data to improve fault tolerance
D. Caching every dataset regardless of its access frequency

Answer: A

Explanation:
When deciding to cache data in a distributed computing environment like Apache Spark, a primary consideration is the trade-off between memory usage and computational efficiency. Caching can significantly speed up data access for frequently accessed datasets, but it also consumes precious memory resources. Balancing the benefits of reduced computation with the costs of increased memory usage is crucial for optimizing application performance.

NEW QUESTION # 157
In optimizing join operations, what role does the Catalyst optimizer in Spark play, specifically regarding join strategies?

A. It disables all optimizations by default to provide consistent performance across different datasets.
B. It exclusively uses broadcast join for all operations to minimize execution time.
C. It dynamically selects the most appropriate join strategy based on the query execution plan.
D. It manually requires the developer to specify the join strategy for each operation.

Answer: C

Explanation:
The Catalyst optimizer in Spark dynamically selects the most appropriate join strategy based on the query execution plan and the characteristics of the data involved. It considers factors such as the size of the datasets, their distribution, and the available resources to choose between available join strategies (e.g., broadcast join, shuffle hash join, sort merge join) to optimize the query's performance.

NEW QUESTION # 158
When creating a partitioned table in Hive, what does the clause PARTITIONED BY specify?

A. The compression algorithm used for data storage
B. The default file format for data storage
C. The replication factor for the HDFS data blocks
D. The column(s) used to divide the table into partitions

Answer: D

Explanation:
The PARTITIONED BY clause in Hive specifies the column(s) by which the table is to be divided into partitions. Each partition corresponds to a specific value or range of values of the partitioning column(s) and is stored in its own directory, enabling more efficient data access patterns based on those column(s).

NEW QUESTION # 159
......

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