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

The correct command to grant the necessary permission on the entire database to the new team is to use the GRANT USAGE command. The GRANT USAGE command grants the principal the ability to access the securable object, such as a database, schema, or table. In this case, the securable object is the database customers, and the principal is the group team. By granting usage on the database, the team will be able to see what tables already exist in the database. Option E is the only option that uses the correct syntax and the correct privilege type for this scenario. Option A uses the wrong privilege type (VIEW) and the wrong securable object (CATALOG). Option B uses the wrong privilege type (CREATE), which would allow the team to create new tables in the database, but not necessarily see the existing ones. Option C uses the wrong securable object (CATALOG) and the wrong principal (customers). Option D uses the wrong securable object (team) and the wrong principal (customers). References: GRANT, Privilege types, Securable objects, Principals

Option B changes duplicate detection from all-column comparison to comparison on the two required business keys. With no subset argument, dropDuplicates() considers every column, so records that differ in event_timestamp are not duplicates. Databricks documents that passing a list such as [ " user_id " , " event_type " ] makes those columns the duplicate-comparison subset while returning a DataFrame with the original schema. groupBy(...).count() aggregates the data and returns counts instead of one complete event row. Dropping event_timestamp and then calling dropDuplicates() still compares every remaining column, not only the two specified keys. Calling distinct() before selecting the keys also performs distinctness on complete rows and then discards the non-key columns. Therefore, df.dropDuplicates([ " user_id " , " event_type " ]) is the precise change, making option B correct.

The engineer should inspect the job’s maximum concurrent-runs configuration and its queueing setting. max_concurrent_runs controls how many active runs of the same job can execute simultaneously. When that limit has already been reached, Databricks can skip the newly triggered run if queueing is disabled. When queueing is enabled, the additional run waits until capacity becomes available instead of being skipped. Spot-instance availability might delay or prevent compute startup, but it would not normally produce a job-level concurrency message. SQL warehouse permissions affect SQL task execution after a run begins, while timeout_seconds determines how long a task may run before timing out. Therefore, option C directly addresses the reported run state. This candidate was also compared against the existing Word question bank.

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The processingTime option specifies a time-based trigger interval for fixed interval micro-batches. This means that the query will execute a micro-batch to process data every 5 seconds, regardless of how much data is available. This option is suitable for near-real time processing workloads that require low latency and consistent processing frequency. The other options are either invalid syntax (A, C), default behavior (B), or experimental feature (E). References: Databricks Documentation - Configure Structured Streaming trigger intervals, Databricks Documentation - Trigger.

A materialized view stores the result of its defining query and refreshes that result when required. This makes it appropriate for complex Gold-layer joins and aggregations that would otherwise be recomputed whenever a dashboard is opened. Because the source data changes only every few hours, the materialized view can be refreshed according to that update pattern while BI queries read precomputed results with lower latency and reduced resource consumption. A standard view stores only its query definition and executes the underlying computation when queried. A streaming table is intended primarily for incremental or continuously arriving data rather than periodic precomputation of BI aggregations. An external table describes where data is stored but does not inherently precompute query results. Therefore, option B best satisfies the performance and refresh requirements.

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