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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q118-Q123):
NEW QUESTION # 118
What is the role of a Spark driver in a distributed processing job?
- A. Stores and processes intermediate data
- B. Coordinates tasks across the cluster
- C. Performs computations on individual data partitions
- D. Manages communication between executors and workers
Answer: B
Explanation:
The driver program acts as the central entity, submitting jobs, scheduling tasks on executors, and managing dependencies between stages
NEW QUESTION # 119
What are the potential trade-offs to consider when using checkpointing in Spark applications?
- A. Checkpointing requires manual configuration and can be error-prone
- B. All of the above
- C. Checkpointing always improves performance and has no drawbacks
- D. Checkpointing introduces overhead for storing and recovering data, impacting performance
Answer: B,D
Explanation:
While checkpointing offers benefits, it comes with trade-offs. It adds overhead for storing and recovering data, potentially impacting performance B. Configuring checkpoints effectively is crucial to avoid unnecessary overhead. Option A is incorrect, and while configuration is involved, it's not inherently error-prone C when done carefully.
NEW QUESTION # 120
Which tool or API is primarily used for monitoring and inspecting the performance of Spark applications in real-time?
A Spark History Server
- A. Apache Ambari
- B. Spark Web UI
- C. Hadoop YARN ResourceManager UI
Answer: C
Explanation:
The Spark Web UI is a monitoring tool that provides information about the execution of a Spark application. It gives insights into the scheduler stages and tasks, executor usage, storage usage, and environmental settings. It is accessible while the application is running and is the primary tool for real-time performance monitoring. The Spark History Server helps in inspecting the application performance after it has completed. Hadoop YARN ResourceManager UI and Apache Ambari are more generally used for cluster management and monitoring, not specifically for real-time Spark application performance.
NEW QUESTION # 121
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 manually requires the developer to specify the join strategy for each operation.
- C. It dynamically selects the most appropriate join strategy based on the query execution plan.
- D. It exclusively uses broadcast join for all operations to minimize execution time.
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 # 122
What is the key benefit of using the Cloudera Data Engineering service compared to building and managing data pipelines manually?
- A. Improved data security through proprietary tools.
- B. Increased scalability, reliability, and manageability.
- C. Cost savings due to open-source technology.
- D. Faster development time for simple data pipelines.
Answer: B
Explanation:
The Cloudera Data Engineering service offers a single platform for managing complex data pipelines. It provides benefits like:Scalability: Easily handle growing data volumes and processing demands. Reliability: Built-in features like retry logic and error handling improve pipeline reliability. Manageability: Provides a central interface for monitoring, scheduling, and troubleshooting pipelines. These features significantly enhance managing and scaling data pipelines compared to manual approaches.
NEW QUESTION # 123
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