Databricks Databricks-Certified-Professional-Data-Engineer Exam With Confidence Using Practice Dumps

Databricks Asset Bundles (DABs) enable declarative configuration and deployment of Databricks resources such as jobs, pipelines, and dashboards across multiple environments.

Once deployed, jobs can be executed in a specific target context using the databricks bundle run command, which ensures all environment-specific configurations from the bundle definition (such as parameters, cluster settings, and workspace URLs) are respected.

The -t flag specifies the target environment (e.g., dev, staging, or prod). This ensures that the execution runs with the correct configuration defined under that target in databricks.yml.

Other options (A, B, and C) are invalid because they reference deprecated or incorrect command syntax that doesn’t integrate with bundle targets. Therefore, D is the correct and verified answer.

The query joins billions of rows (network_events) with millions of rows (ip_ranges) using a range predicate (BETWEEN). Unlike equality joins (=), range joins are not efficiently handled by broadcast or sort-merge joins because:

Broadcast Join (D): Effective for small tables but only for equality joins. Since this query uses a range condition , broadcast will not reduce the complexity of scanning billions of records across non-equality conditions.

Sort-Merge Join (C): Works for ordered joins but is inefficient on range conditions. Sorting billions of records adds excessive overhead and will not resolve the bottleneck.

Increasing Shuffle Partitions (A): Only spreads out shuffle work but does not address the fundamental inefficiency of range-based lookups at scale.

Range Joins in Spark (RANGE_JOIN hint):

Databricks provides range join optimizations specifically for conditions such as BETWEEN. By applying a RANGE_JOIN hint , Spark can build optimized data structures (such as interval indexes or partition pruning strategies) that map billions of input rows to ranges much faster. This avoids brute-force scans and unnecessary shuffle costs.

Thus, Option B is the correct solution because:

It leverages range-join optimization , which is purpose-built for queries joining massive event logs to smaller lookup tables with IP ranges.

This ensures Spark can evaluate billions of rows against millions of ranges with optimized matching logic, drastically improving query performance while preserving correctness.

Exact extract: “If no trigger is specified, the default processing-time trigger runs micro-batches as fast as possible.”

Exact extract: “Trigger once processes all available data once and then stops.”