Databricks-Certified-Data-Engineer-Associate Exam Dumps - Databricks Certification Questions and Answers

In Databricks Asset Bundles , the databricks.yml file located at the repository root serves as the single authoritative configuration file for the entire project. This file defines key elements such as the bundle name, deployment targets (for example, dev, test, prod), workspace configurations, permissions, and resource definitions including jobs and pipelines. Databricks documentation specifies that there must be one primary databricks.yml file at the root , ensuring consistency and simplifying deployment processes. However, to support modularity and maintainability, this root configuration file can reference additional YAML files using the include mapping. This allows teams to organize configurations across directories while still maintaining a single source of truth. Option A is incorrect because multiple root-level configuration files are not supported as independent authorities. Option C is invalid since configuration files are not restricted to hidden directories. Option D introduces ambiguity and contradicts the requirement for a single authoritative configuration. Therefore, using one root-level configuration file with optional includes is the correct and recommended approach.

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.

Databricks Asset Bundles provide a modern, declarative approach to defining, testing, and deploying Databricks workflows as version-controlled code artifacts . Using YAML configuration files, engineers can describe jobs, pipelines, dependencies, and environments in a structured and reproducible way. Asset Bundles integrate well with CI/CD pipelines and external schedulers, enabling teams to package workflows, run integration tests, and deploy consistently across environments such as development, staging, and production. This aligns directly with the requirement for version control and external orchestration. The Databricks CLI (A) is primarily used as a deployment interface but does not provide the structured packaging and workflow definition capabilities of Asset Bundles. Databricks Connect (C) is intended for local development and testing of Spark code, not workflow deployment. The Databricks SDK (D) provides programmatic access to APIs but lacks the declarative, packaged deployment model required here. Databricks documentation positions Asset Bundles as the recommended solution for production-grade workflow deployment and testing within modern data engineering practices.

In Python, the == operator is used to compare the values of two variables, while the = operator is used to assign a value to a variable. Therefore, option A and E are incorrect, as they use the = operator for comparison. Option B and C are also incorrect, as they compare the review_period variable to a string value  " True " , which is different from the boolean value True. Option D is the correct answer, as it uses the == operator to compare the day_of_week variable to the integer value 1, and the and operator to check if both conditions are true. If both conditions are true, then the final block of the Python program will be executed. References: [Python Operators], [Python If … Else]

The best practice is to use " Complete " as output mode instead of " append " when working with aggregated tables. Since gold layer is work final aggregated tables, the only option with output mode as complete is option E.