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

Option B preserves the historical evidence needed to diagnose the regression. The Lakeflow Jobs UI provides run history, duration information, task details, links to the compute resource, and task metrics. Comparing several slow and normal runs shows whether the increase is consistent or intermittent. Cluster metrics such as CPU utilization, memory pressure, disk I/O, and network activity can then reveal resource spikes or bottlenecks that correlate with the longer runs. Automatically increasing cluster size before diagnosis could hide the cause and increase cost. Archiving old runs or resetting a baseline removes useful comparison data. Rerunning the job once does not make seven days of historical information irrelevant and cannot establish a trustworthy new baseline. Therefore, comparing run-history and cluster metrics, as stated in option B, is the appropriate troubleshooting action.

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Option A is correct because Lakeflow Connect provides a built-in, fully managed Salesforce connector. It ingests Salesforce data into Databricks, supports incremental processing, and publishes governed destination tables in Unity Catalog. Managed SaaS connectors handle source-specific authentication, incremental reads, schema evolution, and automated retries, reducing the custom engineering required for API pagination, state tracking, and failure recovery. Calling the Salesforce REST API from a notebook would require the team to develop and maintain those capabilities itself. Auto Loader is designed to discover and incrementally process files arriving in cloud object storage; it does not connect directly to Salesforce. Using an exported Amazon S3 path would also require a separate export system outside the requested managed Databricks ingestion experience. Therefore, option A is the low-code capability that directly satisfies the Salesforce ingestion requirement.

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