Databricks Databricks-Certified-Data-Engineer-Associate - Databricks Certified Data Engineer Associate Exam

 To append a new record to an existing Delta table, you can use the INSERT INTO statement with the VALUES clause. This statement will insert one or more rows into the table with the specified values. Option A is the only code block that follows this syntax correctly. Option B is incorrect, as it uses the UNION operator, which will return a new table that is the union of two tables, not append to an existing table. Option C is incorrect, as it uses the INSERT VALUES statement, which is not a valid SQL syntax. Option D is incorrect, as it uses the UPDATE statement, which will modify existing rows in the table, not append new rows. Option E is incorrect, as it uses the UPDATE VALUES statement, which is also not a valid SQL syntax. References: Insert data into a table using SQL | Databricks on AWS, Insert data into a table using SQL - Azure Databricks, Delta Lake Quickstart - Azure Databricks

The engineer should use Repair run . A repair reruns unsuccessful tasks as part of the original job run while preserving the successful status of tasks that do not need to be executed again. In this scenario, Tasks 1 and 2 remain successful, Task 3 is rerun after its problem is corrected, and Task 4 can execute after its dependency succeeds. Run now starts a separate job run and normally executes the workflow from its beginning. Cloning a task creates another task definition and does not resume the failed run. Reset job is not the appropriate Lakeflow Jobs recovery operation. Databricks also notes that repaired tasks should use idempotent write logic where possible because a failed task might have produced partial output before failing. Therefore, option C correctly resumes the workflow without unnecessarily repeating the successful upstream tasks.

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Serverless jobs compute is the best fit because this is an automated, scheduled workload and no custom cluster configuration is stated. Databricks recommends serverless compute for most automated jobs because it provides fewer settings to manage, faster startup, automatic scaling, and lower cost. Databricks manages the infrastructure, so the team avoids maintaining classic clusters and reduces idle-capacity costs between overnight runs. Classic all-purpose compute is intended primarily for interactive work and is generally not recommended for automated jobs. Classic jobs compute is appropriate when custom cluster settings or capabilities unavailable on serverless are required. SQL warehouses are optimized for SQL analytics tasks rather than general multi-job batch execution. Therefore, option D best satisfies operational simplicity, consistent execution, scaling, and cost control. The existing Word question bank used for comparison is cited here.

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Databricks recommends managed tables when you want the platform to control both the table metadata and the underlying storage lifecycle. For Unity Catalog managed tables, Databricks manages storage layout and applies platform-managed optimization features over time. Databricks also documents that when you drop a managed table , the underlying cloud data is deleted automatically after the managed retention period, which simplifies cleanup and reduces storage administration. That behavior matches the requirement exactly. External tables, by contrast, are used when you want to retain direct control over the storage path and lifecycle. Databricks explicitly states that dropping an external table removes only the metadata and does not delete the underlying files . Foreign tables are for federation scenarios against external systems, not for Bronze ingestion storage managed by Databricks. Temporary views also do not provide the desired governed lifecycle. Because the team wants Databricks-managed storage, managed retention, and simplified lifecycle handling, managed tables are the correct choice for these Bronze ingestion tables.

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 The keyword that can be used to compact the small files associated with a Delta table is OPTIMIZE. The OPTIMIZE command performs file compaction on a Delta table by rewriting a set of small files into a set of larger files1. This can improve the performance of queries that scan the table by reducing the number of files that need to be read and the amount of metadata that needs to be processed1. The OPTIMIZE command can also optionally sort the data within each file by a given set of columns, which can further improve the query performance by enabling data skipping and predicate pushdown1. The OPTIMIZE command can be applied to the whole table or to a specific partition of the table1.

The other keywords are not suitable for compacting the small files associated with a Delta table. REDUCE is a keyword used in the SQL syntax for aggregating data using a user-defined function2. COMPACTION is not a valid keyword in SQL or Python. REPARTITION is a keyword used in the Python syntax for changing the number of partitions of a DataFrame or an RDD3. VACUUM is a keyword used to remove files that are no longer referenced by a Delta table and are older than a retention threshold4.

1: OPTIMIZE | Databricks on AWS

2: REDUCE | Databricks on AWS

3: repartition | Databricks on AWS

4: VACUUM | Databricks on AWS

In Production mode, the pipeline runs continuously and updates the output tables whenever new data is available in the input sources. The compute resources are allocated on demand and released when the pipeline is stopped. This mode is suitable for production workloads that require high availability and reliability. References: Configure pipeline settings for Delta Live Tables, Tutorial: Run your first Delta Live Tables pipeline, Building Reliable Data Pipelines Using DataBricks’ Delta Live Tables

In Unity Catalog, an external table is defined by referencing data that already exists in cloud storage, and Databricks documentation states that this is done by using a LOCATION clause that points to the storage path. Databricks also notes that the external table’s path should be contained within an existing external location that has been configured with the appropriate storage credential and access permissions. This makes option A the correct choice. Option B is incorrect because simply calling something “unmanaged” without specifying a location does not create a proper Unity Catalog external table. Option C is also incorrect because Databricks documentation explicitly requires a storage location for Unity Catalog external tables. Option D is wrong because managed tables are intended for Databricks-managed storage and lifecycle handling, not for referencing existing external files. So, for ADLS-based data that must remain in place while still being queryable through Databricks, the right pattern is to configure the external location and then create the table with a CREATE TABLE ... LOCATION statement.

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Unity Catalog attribute-based access control policies provide centralized, tag-driven governance. Administrators define policies at a catalog or schema scope and use governed tags to identify the tables or columns to which the policies apply. When matching tags are assigned to current or future objects, the applicable policy is evaluated automatically, avoiding repetitive manual attachment of filters and masks to individual tables. This approach improves scalability and consistency when many objects require the same protection rules. It is important that the policy applies to objects matching its tag conditions, rather than indiscriminately to every object. The other choices incorrectly describe supported object types, multiple-filter behavior, or implementation languages. Databricks specifically positions attribute-based access control as the centralized approach for consistently applying row filtering and column masking across many governed tables. Therefore, option A is correct.

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The command databricks bundle deploy deploys the bundle’s local files, artifacts, and resource definitions to the configured remote Databricks workspace. Databricks documents this command as the deployment operation for Declarative Automation Bundles, and a specific target can be selected with the -t or --target option. The validate command checks whether the bundle configuration is valid but does not create or update workspace resources. The run command executes a resource that has already been deployed; it is not the deployment step. The init command creates a new bundle project from a template, while bundle deployment is not a valid command combination. Because the question asks for the single command that sends local bundle assets to the target workspace, option C is correct.

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