有難いCloudera CDP-3002 | 効果的なCDP-3002関連資格試験対応試験 | 試験の準備方法CDP Data Engineer - Certification Exam復習教材CDP-3002試験問題のさまざまなバージョンを知りたい場合があります。まず、PDFバージョンは読みやすく、印刷しやすいです。次に、ソフトウェアバージョンは実際のCDP-3002実際のテストガイドをシミュレートしますが、Windowsオペレーティングシステムでのみ実行できます。第三に、オンライン版はあらゆる電子機器をサポートし、オフライン使用もサポートします。初めて、オンライン環境でCDP-3002試験問題を開く必要があり、それをオフラインで使用できます。全体として、受験者が試験に合格するのを支援することが常に求められています。 CDP-3002実際のテストガイドが最適です。 Cloudera CDP Data Engineer - Certification Exam 認定 CDP-3002 試験問題 (Q194-Q199):質問 # 194
Your team is using PySpark and wants to ensure task re-execution in case of a node failure. What mechanism in Spark ensures that tasks are retried on other nodes upon failure?
A. Master Node Redundancy
B. Task Re-execution
C. Data Replication
D. Checkpointing
正解：B
解説：
Task re-execution is the mechanism in Spark that ensures tasks are retried on other nodes in the event of a node failure. This is a key feature of Spark's fault tolerance capability, allowing it to handle worker node failures without data loss.
質問 # 195
Your Airflow DAG involves sending notifications upon successful completion of the entire pipeline. How can you achieve this functionality?
A. Implement a custom notification script within the final task of the DAG.
B. Use the Email Operator to send an email notification upon successful DAG run completion.
C. Configure the Airflow web UI to send alerts based on DAG run status.
D. Utilize Airflow variables to store notification details and access them within the final task.
正解：B
解説：
The EmailOperator in Airflow provides a convenient way to send email notifications based on DAG run completion status. While other options might be used in specific scenarios, option B is the most straightforward approach for sending completion notifications.
質問 # 196
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. Ensuring the join columns are neither partitioned nor bucketed as it may lead to increased complexity.
D. Bucketing considerations are irrelevant in the context of join queries, with partitioning being the sole factor impacting performance.
正解：B
解説：
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.
質問 # 197
How can you dynamically generate tasks in Apache Airflow?
A. By using the Variable feature to store dynamic configurations
B. By defining a Python function inside the DAG file
C. By using the Jinja templating engine
D. By using Python loops to create tasks during DAG parsing
正解：D
解説：
You can dynamically generate tasks in Apache Airflow by using Python loops within the DAG definition file. This allows for the programmatic creation of tasks based on dynamic inputs or configurations. While Jinja templating, Python functions, and Variables can influence task behavior and configurations, the direct method for dynamically generating tasks is through Python loops during DAG parsing.
質問 # 198
Which of the following is a benefit of using broadcast variables in Spark for caching static lookup tables?
A. They increase the amount of data shuffle during joins.
B. They are automatically cleaned up after each task.
C. They reduce the reliability of Spark applications.
D. They reduce network I/O by making data available locally on each node.
正解：D
解説：
Broadcast variables are used in Spark to cache static lookup tables on each node, rather than sending this data with every task. This approach reduces network I/O by making the data available locally, which is particularly beneficial for tasks that need to access the same data repeatedly, such as when performing map-side joins or lookup operations.