Continuous mode is designed for low-latency processing. Since the business requirement is to query the final table ‘as quickly as possible’ with the latest user info, Continuous mode minimizes the latency between a source data change and its availability in the target table. While it uses more compute, it best meets the stated performance requirement.

Delta Lake uses optimistic concurrency control. The `spark.databricks.delta.merge.maxCommits` setting allows the operation to retry automatically by checking for a limited number of intervening commits, resolving the conflict transparently. This is the built-in, robust method for handling concurrent writes in Delta Lake, superior to manual retry loops or external locks.

The `optimizeWrite` (or `autoOptimize.optimizeWrite`) feature automatically coalesces small files created by writes, including those from Structured Streaming, into larger, more efficient files as part of the write operation. This directly addresses the problem of small files without changing the streaming trigger logic or other unrelated configurations.

Z-Ordering co-locates related data within files based on one or more columns. Since the query filters on `event_date` (already a partition, which prunes files effectively) and then groups by `country`, adding Z-Order on `country` will ensure that rows for the same country are packed together within each date-partitioned file. This dramatically improves the efficiency of the subsequent `GROUP BY country` operation by reducing the amount of data read from each file.

In DLT, `@dlt.expect_all()` defines ‘soft’ constraints or data quality expectations. Records that violate these expectations are recorded in the pipeline’s event log and metrics, but they are still included in the target table. This allows the pipeline to continue processing (maintaining data flow) while providing visibility into quality issues for triage, which is often the desired behavior in a silver layer.

`foreachBatch` provides at-least-once write semantics. To achieve atomicity (all-or-nothing) across the Delta write and an external system update, the entire operation must be idempotent. This means that if a batch is replayed due to a failure, the repeated writes will not create duplicate data or side effects. Managing offsets externally (e.g., in a Delta table) is a common pattern to enable this idempotency by tracking what has already been processed.

The `APPLY CHANGES INTO` syntax for SCD Type 2 requires: `keys` (to uniquely identify the row/entity), `sequence_by` (to define the order of incoming changes, crucial for determining the latest state), and `track_history` set to `true` (which is the directive to preserve old versions of rows as updates occur, creating the Type 2 history). The other options include invalid or incorrect parameters for this specific operation.

Delta Lake does not have a built-in secondary index feature like traditional RDBMS. If a custom secondary index solution is implemented (e.g., using a separate lookup table), it typically requires synchronous maintenance on every write (INSERT/UPDATE/DELETE), which adds significant overhead. The Databricks-recommended approach for accelerating flexible queries is to use data skipping with Z-Ordering, which does not incur this per-write maintenance penalty.

Delta Sharing supports sharing tables, views, and importantly, **Dynamic Views**. A Dynamic View is defined by a SQL query (e.g., `SELECT * FROM main.table WHERE region=’EMEA’` excluding the `salary` column) stored in the Unity Catalog. When shared, the recipient executes queries against this definition, which always reflects the current data in the underlying source table, meeting the ‘live, read-only view’ requirement without creating physical copies or materialized views.

Continuous mode is designed for low-latency processing. Since the business requirement is to query the final table ‘as quickly as possible’ with the latest user info, Continuous mode minimizes the latency between a source data change and its availability in the target table. While it uses more compute, it best meets the stated performance requirement.0