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In Apache Airflow, how can you ensure that a data quality check task is only executed if a previous data ingestion task succeeded and the ingested data volume exceeds a certain threshold?
A. Configure the trigger_rule parameter to all_success and use a PythonOperator to verify the data volume.
B. Use a BranchPythonOperator with a condition that checks the success of the ingestion task and the data volume.
C. Utilize the ShortCircuitOperator with a custom Python function to check both conditions.
D. Implement a PythonOperator with a pre_execute hook for the condition check.
Answer: C
Explanation:
The ShortCircuitOperator is designed for situations where subsequent tasks should only run based on the outcome of a condition. By using this operator with a custom Python function that verifies both the success of the previous task and whether the data volume exceeds a specific threshold, you can effectively control the execution flow of your data quality checks.
NEW QUESTION # 126
Consider the following code snippet:# Sample DataFrame (assuming it exists) df = spark.createDataFrame(...)
# Attempt to explode a nested array column (fix the error)
df_exploded = df.withColumn("items", F.explode(df["items"]))
df_exploded.show()
What is the error in this code, and how can it be fixed?
A. There is no error in the code snippet.
B. The error is attempting to modify the original DataFrame in-place. Fix: Use a separate variable to store the exploded DataFrame.
C. The error is using withColumn instead of a dedicated method for exploding arrays. Fix: Use df.withColumnExploded. (This function doesn't exist in Spark)
D. The error is missing parentheses around the column name in the explode function. Fix: F.explode(df("items"))
Answer: B
NEW QUESTION # 127
You need to migrate a large Hive-managed table into an Iceberg table within CDP while minimizing downtime.
Which strategy would you consider?
A. Utilize Iceberg's migration feature to automatically handle the conversion.
B. Use the ALTER TABLE statement in Hive to convert the table format to Iceberg.
C. Create an external Iceberg table pointing to the existing Hive data and then use Iceberg's INSERT OVERWRITE to copy the data.
D. Export the data from Hive and reload directly into a new Iceberg table.
Answer: C
Explanation:
B). This is the most controlled approach in CDP. It minimizes disruption and lets you verify the Iceberg table before switching over. A. Export/reload is disruptive and less efficient for large tables. C. Hive's ALTER TABLE may not fully support Iceberg conversions depending on the version. D. Iceberg has a migration tool, but it may have limitations for complex tables.
NEW QUESTION # 128
You're building an Airflow DAG that consists of multiple interdependent ETL pipelines. How can you ensure they execute in the correct order and avoid conflicts?
A. Implement a custom script to manage the execution order of the pipelines.
B. Schedule each pipeline separately with appropriate scheduling intervals.
C. Run all pipelines simultaneously, assuming they are independent.
D. Utilize Airflow sub-DAGs to group related tasks and define dependencies between them.
Answer: D
Explanation:
Airflow sub-DAGs provide a structured way to organize and manage complex workflows. Option B allows you to group related ETL pipelines into sub-DAGs and define dependencies between them, ensuring they execute in the desired order while preventing potential conflicts.
NEW QUESTION # 129
When performing a bucketed join between two tables, what must be true for the join to be executed as a map-side join, thereby maximizing performance?
A. Both tables must be bucketed on the join columns with a different number of buckets.
B. Both tables must be partitioned, not bucketed, on the join columns.
C. Both tables must be bucketed on the join columns with the same number of buckets and the same hash function.
D. Only one table needs to be bucketed, regardless of the bucket count.
Answer: C
Explanation:
For a join to be executed as a map-side join when using bucketed tables, both tables must be bucketed on the join columns with the same number of buckets and use the same hash function. This ensures that the data is distributed in a way that corresponding buckets from each table contain joinable data, allowing Hive to perform the join directly on the map side without shuffling data, which significantly improves performance by reducing network I/O and processing time.
NEW QUESTION # 130
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