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You need to design your Airflow DAG for data quality checks to be scalable and manageable as the number of datasets and checks grows. How can you achieve this?
A. Leverage external configuration files (e.g., YAML or JSON) to define data quality checks and associated parameters.
B. Hardcode all data quality checks and data sources directly within the DAG code.
C. Implement a modular design using sub-DAGs, where each sub-DAG encapsulates the data quality checks for a specific dataset.
D. Utilize Airflow variables to store configuration details like data source paths and check thresholds.
Answer: A,C
Explanation:
While option C might be relevant for handling transient errors, option B and D combined offer a robust approach:'BranchPythonOperator': Allows you to evaluate the outcome of the data quality checks (success or failurE. within the 'PythonOperator'. Conditional Branching: Based on the check results, the DAG can either proceed with downstream tasks (success) or trigger error handling routines if the checks fail. Automatic Retries (optional): Configuring retries for the data quality checks can be added as a safeguard in case of temporary issues (option D.
NEW QUESTION # 243
What advanced technique can be used in Hive to optimize queries on bucketed tables by skipping unnecessary data?
A. Data encryption at the bucket level
B. Bucket pruning based on query predicates
C. Increasing the replication factor of bucketed data
D. Manually specifying the buckets to scan during query execution
Answer: B
Explanation:
Bucket pruning is an advanced technique that can be used in Hive to optimize queries on bucketed tables by skipping unnecessary data. Similar to partition pruning, bucket pruning allows Hive to skip over buckets that do not match the query predicates, thereby reducing the amount of data scanned and improving query performance. This technique relies on the metadata about the buckets and the distribution of data within them to determine which buckets are relevant to the query, enabling more efficient data access patterns.
NEW QUESTION # 244
For iterative machine learning algorithms in Spark, which caching level minimizes the trade-off between computation time and storage efficiency?
A. DISK ONLY
B. MEMORY ONLY
C. MEMORY ONLY SER
D. MEMORY AND DISK SER
Answer: C
Explanation:
MEMORY_ONLY_SER (serialize data in memory) minimizes the trade-off between computation time and storage efficiency for iterative machine learning algorithms. It stores the data in a serialized form in memory, reducing the space it occupies compared to the deserialized form (MEMORY ONLY), while still avoiding the disk I/O overhead associated with DISK ONLY or MEMORY AND DISK SER. This level strikes a balance by using less memory than MEMORY_ONLY and being faster than options that involve disk storage.
NEW QUESTION # 245
You have a DataFrame containing sales data with columns "product_id", "customer id", and "amount". How can you efficiently calculate the total sales per customer?
A. Use a loop to iterate through the DataFrame and accumulate the sales for each customer
B. Leverage Spark's machine learning library (MLIiB. for aggregation
C. Implement a custom function to group and sum the sales
D. Utilize spark SQL's GROUP BY and SUM functions
Answer: D
Explanation:
Option B provides the most efficient and concise way to achieve this.
NEW QUESTION # 246
If a Spark Driver pod in Kubernetes is reaching its CPU limit and experiencing performance issues, what is the most appropriate first action?
A. Increase the CPU requests in the pod's YAML configuration.
B. Decrease the memory limits in the pod's YAML configuration.
C. Restart the Kubernetes cluster.
D. Increase the CPU limits in the pod's YAML configuration.
Answer: D
Explanation:
If a pod is experiencing performance issues due to CPU limitations, increasing the CPU limits in the pod's YAML configuration is the most direct way to address the issue.
NEW QUESTION # 247
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