Pass the Databricks Databricks Certification Databricks-Certified-Data-Engineer-Associate Questions and answers with CertsForce

 Delta Live Tables expectations are optional clauses that apply data quality checks on each record passing through a query. An expectation consists of a description, a boolean statement, and an action to take when a record fails the expectation. The ON VIOLATION clause specifies the action to take, which can be one of the following: warn, drop, or fail. The drop action means that invalid records are dropped from the target dataset before data is written to the target. The failure is reported as a metric for the dataset, which can be viewed by querying the Delta Live Tables event log. The event log contains information such as the number of records that violate an expectation, the number of records dropped, and the number of records written to the target dataset. References:

Manage data quality with Delta Live Tables

Monitor Delta Live Tables pipelines

Delta Live Tables SQL language reference

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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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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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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spark.sql.autoBroadcastJoinThreshold controls the estimated table-size limit under which Spark can automatically select a broadcast join. Increasing it to a value above the lookup table’s estimated size allows Spark to copy that small relation to each executor. The large fact table can then be processed locally without being fully shuffled across the cluster, often reducing network transfer and join duration. The threshold should remain within executor-memory capacity because broadcasting an excessively large relation can cause memory pressure. spark.default.parallelism controls default task parallelism, while spark.sql.shuffle.partitions controls the number of partitions used by shuffle operations. spark.executor.memory changes executor capacity but does not instruct the optimizer to select a broadcast join. Therefore, option A is the relevant configuration.

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Databricks documents that Delta Sharing does not require data replication , but cloud providers can still charge data egress fees when data is shared across clouds or across geographic regions . Databricks specifically states that sharing within the same region incurs no egress cost , while cross-cloud or cross-region transfers can create additional charges from the underlying cloud provider. That means option A is correct. Options B and C describe same-cloud or same-region use cases that do not trigger the cross-region/cross-cloud egress pattern Databricks calls out. Option D is unrelated to the main documented billing driver for Delta Sharing costs. The important principle is that Delta Sharing itself is designed to avoid replication overhead, but the physical movement of data between cloud boundaries or regions can still result in vendor networking charges. Therefore, when sharing data with external partners in other regions or clouds, engineers should plan for possible egress costs and monitor them accordingly.

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Delta Sharing is an open protocol developed by Databricks that enables secure, real-time sharing of data across organizations without requiring recipients to have a Databricks account. It is specifically designed for scenarios where external partners need read-only access to data stored in Delta format. With Delta Sharing, data providers create a share and grant access to recipients via a secure mechanism (such as a profile file or URL-based access), ensuring that the data cannot be modified by the consumer. This approach eliminates the need for data duplication or manual file transfers, maintaining data freshness and governance. Option B is incorrect because Unity Catalog governs internal access within an organization, not external sharing without accounts. Option C is inefficient and breaks data lineage and freshness. Option D violates security best practices by granting excessive permissions. Delta Sharing ensures fine- grained access control, auditability, and secure external collaboration , making it the correct solution for this requirement.

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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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