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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q149-Q154):

NEW QUESTION # 149
For a Hive table that is both partitioned and bucketed, what considerations must be taken into account to optimize a join query involving this table?

A. The join should exclusively rely on the partitioned columns, ignoring the bucketed columns for optimal performance.
B. Both the partitioning and bucketing columns should align with the join columns where possible to maximize the efficiency of data retrieval.
C. Bucketing considerations are irrelevant in the context of join queries, with partitioning being the sole factor impacting performance.
D. Ensuring the join columns are neither partitioned nor bucketed as it may lead to increased complexity.

Answer: B

Explanation:
For a Hive table that is both partitioned and bucketed, optimizing a join query involves aligning both the partitioning and bucketing columns with the join columns where possible. This alignment allows Hive to leverage both partition pruning and bucketing strategies to reduce the amount of data scanned and processed during the join. By ensuring that the join operation can take advantage of both partitioning (to eliminate irrelevant partitions) and bucketing (to facilitate efficient join strategies like map-side joins), query performance can be significantly improved.

NEW QUESTION # 150
You're tasked with optimizing the performance of your ETL pipeline in Airflow. What are some potential strategies to consider?

A. Leverage partitioning and bucketing techniques in the data warehouse to improve query performance.
B. All of the above
C. Utilize efficient data structures and algorithms within your custom Python operators for data transformation.
D. Increase the number of worker processes in Airflow to parallelize task execution.

Answer: B

Explanation:
All options listed in D contribute to optimizing Airflow ETL pipeline performance. Optimizing code within operators, utilizing data warehouse features, and increasing worker processes (considering resource constraints) can all lead to improved efficiency.

NEW QUESTION # 151
Which of the following statements best describes the process of schema inference in the context of big data processing?

A. Assigning random data types to columns in unstructured data.
B. Encrypting data based on its type.
C. Manually defining the schema for each dataset before processing.
D. Automatically determining the data structure of a dataset.

Answer: D

Explanation:
Schema inference refers to the process of automatically identifying the structure (such as data types and column names) of a dataset, especially in semi-structured or unstructured data sources. This process enables data processing systems to understand the format of the data without requiring explicit schema definitions from the user.

NEW QUESTION # 152
You need to create a new Hive table from a Spark DataFrame. What are the different approaches you can consider?

A. Use the DataFrame.write.saveAsTable("table_name") method with appropriate options
B. All of the above
C. Directly write the DataFrame to a directory in HDFS and define a corresponding Hive table schema
D. Convert the DataFrame to a temporary table and then use HiveQL commands to create a permanent table

Answer: B

Explanation:
While each option offers a way to create a Hive table from a Spark DataFrame, they provide different levels of control and convenience. Option A requires manual schema definition, while B offers a concise approach with configuration options. Option C might be useful for specific scenarios, but B is generally preferred.

NEW QUESTION # 153
How does Spark achieve fault tolerance during distributed processing?

A. By replicating data across all nodes in the cluster
B. null
C. By implementing automatic checkpointing of intermediate results
D. By restarting failed tasks on different nodes

Answer: B

Explanation:
Spark employs a combination of data lineage tracking, checkpointing, and task re-execution to ensure job completion even in the event of node failures.

NEW QUESTION # 154
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