Data and Database Fundamentals

The correct answer is to create a covering index that includes claim_date, status, policy_type, region_code, and claim_amount columns.

This is the most effective solution because a covering index contains all the columns needed to satisfy the query, eliminating the need to access the base table at all. The query requires:
- claim_date for the WHERE clause range filter
- status for the WHERE clause equality filter
- policy_type and region_code for GROUP BY and SELECT
- claim_amount for the AVG() calculation

By including all these columns in a single index (with claim_date and status as leading columns for filtering), the database engine can retrieve all necessary data from the index structure alone. This eliminates the table scan mentioned in the problem and provides the fastest possible query execution.

The option to add status to the existing composite index as the fourth column would not be optimal because status would be positioned after policy_type and region_code, making it less efficient for filtering. Additionally, this approach would still require accessing the base table to retrieve claim_amount for the AVG() calculation.

Creating a separate index on just the status column and relying on index intersection is inefficient. Index intersections require additional overhead to merge results from multiple indexes and are generally less performant than a properly designed single index.

Reordering the index to lead with status assumes equality predicates should always come first, but this overlooks that claim_date with a range condition on 6 months of data provides substantial filtering. More importantly, neither this approach nor any approach that doesn't include claim_amount would eliminate the need to access the base table for the aggregation calculation.

The correct approach is to create an index on the shipment_date column. This solution addresses the root cause of the performance problem identified in the execution plan: the full table scan on 50 million records.

When the WHERE clause filters on shipment_date for the last 30 days, an index on this column allows the database engine to quickly locate only the relevant records (likely a small subset of the 50 million rows) rather than scanning every single row in the table. This dramatically reduces the number of records that need to be processed for the subsequent join and aggregation operations.

The other options are incorrect for the following reasons:

Moving the date filter to a HAVING clause instead of WHERE would actually make performance worse, not better. The HAVING clause is processed after GROUP BY operations, meaning the database would first aggregate all 50 million records and then filter the results. The WHERE clause correctly filters rows before aggregation, which is more efficient.

Replacing AVG with a subquery that pre-calculates values per shipment_id adds unnecessary complexity and computational overhead. The AVG function is already optimized by database engines, and creating a derived table does not solve the fundamental problem of needing to scan 50 million records.

Restructuring the query to perform GROUP BY before JOIN operations is generally not a valid optimization strategy in this context. The aggregation depends on data from the joined tables, and arbitrarily reordering these operations would likely produce incorrect results or not be possible given the query's requirements.

The backup frequency interval setting is the correct answer because it determines how often backup snapshots are created and stored. In this scenario, the company had incremental backups running throughout the day, but their recovery points were only maintained on a daily basis. This means the backup frequency for creating actual recoverable restore points was set to once per day (every 24 hours), rather than more granular intervals.

The scenario clearly illustrates this problem: the IT team wanted to restore to a point 72 hours ago, but the closest available restore point was 96 hours ago. This 24-hour gap exists because the system was configured to create recovery points only once daily. If the backup frequency had been set to create restore points every few hours (such as every 4 or 6 hours), the team would have had a recovery point much closer to their desired 72-hour target.

The backup deduplication ratio setting is incorrect because deduplication is a storage optimization technique that eliminates redundant data segments to save space. It has no effect on when or how often recovery points are created.

The backup retention policy duration is incorrect because retention controls how long backups are kept before deletion. While important for long-term recovery, the problem in this scenario was not about backups being deleted too soon - the 96-hour-old backup still existed. The issue was the lack of granular restore points between the daily backups.

The backup replication factor configuration is incorrect because replication determines how many copies of backup data exist across different locations for redundancy and disaster recovery purposes. It does not affect the frequency or granularity of when recovery points are created.