有難いCDP-3002｜正確的なCDP-3002的中率試験｜試験の準備方法CDP Data Engineer - Certification Exam一発合格ClouderaのCDP-3002の認定試験証明書を取りたいなら、Jpexamが貴方達を提供した資料をかったら、お得です。Jpexamはもっぱら認定試験に参加するIT業界の専門の人士になりたい方のために模擬試験の練習問題と解答を提供した評判の高いサイトでございます。 Cloudera CDP Data Engineer - Certification Exam 認定 CDP-3002 試験問題 (Q119-Q124):質問 # 119
You're building an Airflow DAG to automate data quality checks on the output of your ETL pipeline. The checks involve performing various data validation tasks like checking for missing values, ensuring data type consistency, and verifying data integrity based on specific business rules. How can you implement these checks within Airflow?
A. Utilize Python libraries like Pandas or Spark for data manipulation and validation within the PythonOperator.
B. Use the PythonOperator to write custom Python scripts for each individual check and chain them together in the DAG.
C. All of the above
D. Leverage dedicated Airflow operators like BigQueryCheckOperator or S3KeySensor (these operators are specific to certain data sources and not generally applicable for all data quality checks).
正解：C
質問 # 120
In the context of Spark, what is a potential downside of indiscriminate use of data caching, especially with the MEMORY_AND DISK storage level?
A. It enhances data security by storing intermediate results in encrypted form.
B. It may increase execution time due to overheads from frequent disk 1/0 operations.
C. It can decrease network traffic by reducing the need for data shuffling.
D. It can lead to reduced fault tolerance due to reliance on in-memory storage.
正解：B
解説：
Indiscriminate caching, especially with the MEMORY_AND DISK storage level, can lead to increased execution time due to the overheads associated with frequent disk I/O operations. When the memory capacity is exceeded, data is spilled to disk, which can significantly slow down data access compared to in-memory operations. While this approach ensures that the data is not lost if it exceeds memory capacity, it introduces additional latency due to disk access times.
質問 # 121
When creating a partitioned table in Hive, what does the clause PARTITIONED BY specify?
A. The default file format for data storage
B. The replication factor for the HDFS data blocks
C. The compression algorithm used for data storage
D. The column(s) used to divide the table into partitions
正解：D
解説：
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).
質問 # 122
In the context of dynamic partitioning in Hive, what challenge does the use of too many dynamic partitions in a single load operation present?
A. It simplifies the management of Hive metadata, reducing the load on the NameNode.
B. It automatically disables the execution of map-reduce jobs.
C. It can lead to an excessive number of small files, negatively impacting HDFS performance.
D. It enhances data security by segmenting data into finer-grained partitions.
正解：C
解説：
Using too many dynamic partitions in a single load operation can lead to the creation of an excessive number of small files. This situation is problematic because it can significantly degrade HDFS performance due to the overhead associated with managing a large number of files, including increased memory consumption on the NameNode for metadata management and potential slowdowns in data processing tasks that have to open and read many small files.
質問 # 123
You are designing a data pipeline that involves ingesting data from multiple sources, performing data transformations using Spark, and storing the results in a data lake. How would you leverage the Cloudera Data Engineering service to ensure efficient and fault-tolerant execution?
A. Develop a single Spark job containing all transformation logic.
B. Implement custom logic within the YAML configuration file to manage data flow and error handling.
C. Design the pipeline with stages and steps, leveraging Spark operators for transformations and utilizing retries and error handling mechanisms.
D. Utilize separate Spark jobs for each data source and transformation step.