Helping Hand Questions for Databricks-Certified-Data-Engineer-Associate

A data lakehouse is a data management architecture that combines the flexibility, cost-efficiency, and scale of data lakes with the data management and ACID transactions of data warehouses, enabling business intelligence (BI) and machine learning (ML) on all data12. One of the key features of a data lakehouse is that it supports ACID-compliant transactions, which means that it ensures data integrity, consistency, and isolation across concurrent read and write operations3. This feature results in improved data quality over a traditional data lake, which does not support transactions and may suffer from data corruption, duplication, or inconsistency due to concurrent or streaming data ingestion and processing . References: 1: What is a Data Lakehouse? - Databricks 2: What is a Data Lakehouse? Definition, features & benefits. - Qlik 3: ACID Transactions - Databricks : [Data Lake vs Data Warehouse: Key Differences] : [Data Lakehouse: The Future of Data Engineering]

The COPY INTO statement is an idempotent operation, which means that it will skip any files that have already been loaded into the target table1. This ensures that the data is not duplicated or corrupted by multiple attempts to load the same file. Therefore, if the data engineer runs the same command every day without specifying the names of the files to be copied with the FILES keyword or a glob pattern with the PATTERN keyword, the statement will only copy the first file that matches the source location and ignore the rest. To avoid this problem, the data engineer should either use the FILES or PATTERN keywords to filter the files to be copied based on the date or some other criteria, or delete the files from the source location after they are copied into the table2. References: 1: COPY INTO | Databricks on AWS 2: Get started using COPY INTO to load data | Databricks on AWS

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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