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What is the recommended way to handle dependencies between data quality checks in Apache Airflow to ensure that checks are performed in a specific sequence?
A. Implement each data quality check as a separate DAG.
B. Explicitly set task dependencies using the set_upstream or set_downstream methods.
C. Use the SequentialExecutor for the Airflow environment.
D. Use the depends_on_past parameter in each data quality check task.
Answer: B
Explanation:
Explicitly setting task dependencies using set_upstream, set_downstream, or the bitshift operators (]] and [[) in your DAG definition file is the recommended way to manage the sequence of data quality checks. This approach ensures that tasks are executed in the correct order, based on their dependencies.
NEW QUESTION # 166
How does the Cloudera Data Engineering service integrate with cloud storage solutions like Amazon S3 or Azure Blob Storage?
A. Requires custom scripting for each cloud storage provider.
B. Requires manual configuration for each storage bucket.
C. Utilizes built-in connectors for seamless access.
D. Not directly supported, requires external tools.
Answer: C
Explanation:
The Cloudera Data Engineering service comes with pre-built connectors for various cloud storage solutions like Amazon S3 and Azure Blob Storage. These connectors simplify data movement and integration between the platform and cloud storage solutions.
NEW QUESTION # 167
A team is planning to use PySpark to read data from an Apache Cassandra database. Which of the following options correctly demonstrates how to load data from a Cassandra table named in the keyspace 'sales'?
A.
B.
C.
D.
Answer: D
Explanation:
Option B is correct as it uses the specific format for Cassandra ('org.apache.spark.sql.cassandra') and correctly specifies the keyspace and table options.
NEW QUESTION # 168
You want to debug an issue within your Spark application that interacts with Hive tables. What tools and techniques can you employ for effective debugging?
A. Print debug statements throughout the code to inspect intermediate data
B. Leverage Spark's web UI and Hive logs for general error messages
C. Implement unit tests for individual Spark operations within your application
D. Use Spark's Lineage Viewer to visualize data flow and identify potential errors
Answer: C,D
Explanation:
While printing statements A might provide some insights, relying solely on logs B limits visibility. Spark's Lineage Viewer C offers a visual representation of data flow, helping pinpoint where issues might occur. Additionally, unit testing individual Spark operations D can isolate and identify problems within specific code sections.
NEW QUESTION # 169
How does Spark handle data shuffling during distributed processing?
A. By transferring only required data between executors
B. By broadcasting all data to each executor
C. By storing all data on a single node
D. Spark doesn't perform data shuffling
Answer: A
Explanation:
Shuffling involves moving data between executors when operations require data from different partitions. Spark optimizes this process by transferring only the necessary data, minimizing network traffic.
NEW QUESTION # 170
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