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CDP-3002 Test Dumps.zip & CDP-3002 Valid Exam Cost
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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q82-Q87):
NEW QUESTION # 82
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. Bucketing considerations are irrelevant in the context of join queries, with partitioning being the sole factor impacting performance.
- B. The join should exclusively rely on the partitioned columns, ignoring the bucketed columns for optimal performance.
- C. Both the partitioning and bucketing columns should align with the join columns where possible to maximize the efficiency of data retrieval.
- D. Ensuring the join columns are neither partitioned nor bucketed as it may lead to increased complexity.
Answer: C
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 # 83
What benefit does bucketing provide for join operations in Hive?
- A. It allows for the join condition to be omitted in queries.
- B. It enables cross-database joins by default.
- C. It can reduce the cost of joins by enabling map-side joins.
- D. It ensures data privacy by encrypting join keys.
Answer: C
Explanation:
Bucketing provides the benefit of reducing the cost of join operations in Hive by enabling map-side joins. If two tables are bucketed on the join column(s), Hive can perform the join operation on the map side, without requiring a reduce phase. This is because the data is already organized in a way that ensures all the join keys are located in the corresponding buckets across the tables, significantly reducing the data shuffling and processing required to complete the join, thereby improving performance.
NEW QUESTION # 84
In the context of caching data for reuse in Spark, how does the Tungsten project contribute to enhancing memory management and execution efficiency?
- A. By providing a graphical interface for memory management
- B. By optimizing Spark's internal memory management and data processing operations
- C. By enforcing strict data typing to reduce runtime type checking overhead
- D. By rewriting Spark operations in a lower-level language for direct memory access
Answer: B
Explanation:
The Tungsten project contributes to enhancing memory management and execution efficiency in Spark by optimizing Spark's internal memory management and data processing operations. Tungsten introduces binary in-memory data representation and explicit control over memory allocation, reducing the overhead associated with garbage collection and improving cache efficiency. This optimization supports more efficient execution of operations and better utilization of system resources, particularly for tasks involving large datasets and complex computations.
NEW QUESTION # 85
In Spark, when is it most beneficial to use the repartitionByRange method?
- A. When minimizing network traffic during shuffle operations is the primary concern.
- B. When decreasing the number of partitions to reduce task scheduling overhead.
- C. When sorting data within each partition by a specified column or set of columns is required.
- D. When the data is highly skewed and a uniform distribution of data across partitions is required.
Answer: C
Explanation:
The repartitionByRange method is used to repartition the data based on a range of values, which effectively sorts the data within each partition according to the specified columns. This can be beneficial for performance optimization in scenarios where sorted data can improve the efficiency of subsequent operations, such as range-based queries or when preparing data for window functions. While Option A mentions data skew, repartitionByRange is specifically designed for sorting rather than uniformly distributing skewed data. Option B and D address different concerns related to shuffle operations and task scheduling, which are not the primary purposes of repartitionByRange.
NEW QUESTION # 86
How does partition pruning contribute to query performance in the Cloudera Data Platform?
- A. By increasing the number of partitions
- B. By encrypting data at rest
- C. By removing unnecessary data replication
- D. By selecting only relevant partitions for query execution
Answer: D
Explanation:
Partition pruning enhances query performance by allowing the engine to skip over partitions of the data that are not relevant to the query, thereby reducing the amount of data scanned and processed.
NEW QUESTION # 87
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