The Spark Web IJI is the primary tool for monitoring Spark jobs. It provides detailed information about the job execution, stages, tasks, and resource utilization, which is crucial for identifying performance bottlenecks and optimizing Spark jobs.

All options mentioned in D can be valuable for troubleshooting errors in data quality checks: Airflow UI: Task details page might display error messages or logs specific to the check. Airflow Logs: Scheduler and worker logs might provide additional context about the error and its origin. Custom Script Inspection: Examining the custom Python script used for the check can reveal potential errors within the logic itself.

While checkpointing offers benefits, it comes with trade-offs. It adds overhead for storing and recovering data, potentially impacting performance B. Configuring checkpoints effectively is crucial to avoid unnecessary overhead. Option A is incorrect, and while configuration is involved, it's not inherently error-prone C when done carefully.

Iceberg supports record-level DELETE and INSERT operations for targeted updates. Options A and C are very inefficient for large tables. MERGE INTO is available, but less efficient than direct deletes and inserts.

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.