Databricks-DE-Assoc Practice Test 21 — 15 Questions

Question 1

A data engineer is creating a Lakeflow Job that must run a notebook every weekday at 06:00 in the company's local time zone, which is America/New_York. The engineer configures a schedule trigger but the job runs at the wrong time. Which setting should the engineer verify first?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

The job's time zone setting, which controls how the cron expression is interpreted.. The schedule time zone determines how the cron expression is interpreted. If the job is configured with a cron for 06:00 but the time zone is UTC, the run occurs at 06:00 UTC, which is a different local time. Setting the schedule time zone to America/New_York aligns the trigger with the team's expectation and avoids manual conversion mistakes.

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The notebook's default language, because Python and SQL notebooks evaluate cron expressions differently.

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The job's maximum concurrent runs setting, because concurrency delays the start time.

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The cluster's Spark configuration, because executor time zones affect cron evaluation.

Question 2

A team is preparing to optimize their Databricks data transformation pipeline. Which THREE of the following actions are considered best practices for optimizing Delta Lake performance?

Accepted Answer

Run the OPTIMIZE command to compact small files into larger files.. Optimizing Delta Lake performance requires a holistic approach involving file management, data layout, and efficient querying. Using OPTIMIZE for compaction, Z-Ordering for query locality, and avoiding excessive partitioning are standard practices. These techniques reduce the metadata overhead and I/O costs, leading to faster query execution and lower costs, which are essential for maintaining scalable and performant data pipelines in large enterprise environments.

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Partition the data by high-cardinality columns like 'user_id' or 'transaction_id'.

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Perform a 'VACUUM' operation with a retention period of zero to save costs.

Question 3

A team is using Databricks Asset Bundles (DABs) to manage their CI/CD pipeline. They want to run unit tests on their Python code before deploying the bundle. Where should the tests be executed in the pipeline?

Accepted Answer

In the CI/CD runner (e.g., GitHub Actions) as a separate job step before the bundle deploy command.. Unit tests should be executed in the CI/CD runner as a step before deployment. This ensures that only code that passes tests is deployed. It also leverages the runner's environment and standard testing tools, keeping the pipeline efficient and reliable. Deploying first would risk introducing bugs into production.

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Using the Databricks CLI to run a test command that executes tests on a cluster.

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Inside a Databricks notebook that is executed manually by a developer before merging.

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As a Databricks job within the bundle that is triggered after deployment.

Question 4

A data engineer wants to pass a file path from Task A to Task B in a Lakeflow Job. Task A is a notebook that computes the path, and Task B is a notebook that reads from that path. Which mechanism should the engineer use to share the value between tasks?

Accepted Answer

Use task values: Task A sets a task value with dbutils.jobs.taskValues.set, and Task B retrieves it with dbutils.jobs.taskValues.get.. Task values are the intended feature for passing small, dynamically computed values between tasks in a Lakeflow Job. Task A sets a value using dbutils.jobs.taskValues.set, and Task B retrieves it with dbutils.jobs.taskValues.get, referencing the task that set it. Job parameters are read-only, global variables are not shared across tasks, and using a Delta table is unnecessarily complex for this use case.

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Use job parameters: Task A writes the path to a job parameter, and Task B reads it.

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Use the job's run ID: Task A writes the path to a Delta table keyed by run ID, and Task B queries it.

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Use a global variable in the notebook: Task A sets a global variable, and Task B reads it.

Question 5

Which Databricks feature should be used to securely share data with external organizations without duplicating the data?

Accepted Answer

Delta Sharing. Delta Sharing is a secure, open-protocol solution that allows organizations to share data with third parties without moving or copying the underlying files. By using Delta Sharing, the provider maintains a single source of truth while the consumer gains direct access to the data. This minimizes storage costs, ensures data consistency, and simplifies compliance management for multi-party data sharing scenarios.

Answer

Unity Catalog

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Databricks Workflows

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MLflow Model Registry

Question 6

Which object type in Databricks Unity Catalog acts as the top-level container for organizing schemas and tables, providing a unified namespace for data assets?

Accepted Answer

Catalog. Understanding the Unity Catalog hierarchy is fundamental for data governance. The Catalog is the primary organizational unit that contains schemas, which in turn contain tables, views, and functions. This structure allows engineers to manage access control and discovery at different levels of granularity, which is essential for managing enterprise-scale data environments securely and efficiently.

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Workspace

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Schema

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Table

Question 7

A data engineer has a Lakeflow Job with a linear dependency chain: Task A, then Task B, then Task C. Task B sometimes fails due to transient errors. The engineer wants Task C to run only if Task B succeeds, but also wants Task B to be retried automatically before considering the job failed. Which configuration should they use?

Accepted Answer

Set Task B's Retries to 2 and configure Task C to depend on Task B with the default 'All succeeded' condition.. To automatically retry Task B on transient failures, set its Retries to a positive number. To ensure Task C runs only if Task B succeeds, use the default dependency condition 'All succeeded' for Task C. This combination provides retries for Task B and conditional execution for Task C. Other conditions like 'All done' or 'At least one failed' would cause Task C to run in undesired situations.

Answer

Set Task B's Retries to 2 and configure Task C to depend on Task B with the 'At least one failed' condition.

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Set Task B's Retries to 0 and configure Task C to depend on Task B with the 'At least one succeeded' condition.

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Set Task B's Retries to 2 and configure Task C to depend on Task B with the 'All done' condition.

Question 8

A data engineer needs to grant a group `data_consumers` the ability to query a view `prod.reporting.sales_summary` that is defined on top of tables in the same catalog. The group currently has no privileges on the underlying tables. What is the minimum set of privileges the engineer must grant to `data_consumers` so they can query the view successfully?

Accepted Answer

Grant `SELECT` on the view, plus `USE CATALOG` on `prod` and `USE SCHEMA` on `reporting`.. In Unity Catalog, views execute with the view owner's privileges for referenced objects, so consumers do not need direct privileges on underlying tables. The minimum required set is `SELECT` on the view, `USE CATALOG` on the containing catalog, and `USE SCHEMA` on the containing schema. This allows querying while adhering to least privilege.

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Grant `ALL PRIVILEGES` on the view and `USE SCHEMA` on `reporting`.

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Grant `SELECT` on the view and `SELECT` on the underlying tables.

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Grant `SELECT` on the view and `BROWSE` on the underlying tables.

Question 9

Which tool in Databricks provides real-time monitoring of cluster resource usage, including CPU, memory, and network throughput, for an active job?

Accepted Answer

The Ganglia UI.. The Metrics tab in the Databricks cluster UI offers a visual dashboard showing resource utilization. Monitoring these metrics helps engineers identify if a job is CPU-bound, memory-bound, or network-bound. This visibility is crucial for rightsizing clusters. By observing these trends, engineers can optimize costs by reducing cluster size for underutilized jobs or improve performance by scaling up resources for jobs that encounter significant hardware contention.

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The Spark UI.

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The Query Profile.

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The Databricks Job Run History.

Question 10

Which of the following is the primary benefit of using 'Auto Loader' (cloudFiles) for data ingestion in Databricks compared to standard batch processing?

Accepted Answer

It eliminates the need to perform manual directory listing by tracking file state.. Auto Loader is designed for scalable, incremental ingestion from cloud object storage. It maintains its own state, allowing it to pick up new files without performing expensive directory listings. This functionality is essential for data engineers managing high-frequency data streams where traditional batch processing would become increasingly inefficient and costly as the number of files in storage grows over time.

Answer

It automatically cleans and normalizes all incoming data formats using AI.

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It is the only method that supports reading data from Delta tables.

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It requires less compute resources than a simple 'spark.read' command.

Question 11

A data engineering team is implementing a CI/CD pipeline for Databricks notebooks and jobs using Databricks Asset Bundles. They want to ensure deployments are reproducible and that production changes are traceable. Which TWO of the following practices should they follow? (Choose two.)

Accepted Answer

Use a separate target in databricks.yml for each environment, and run databricks bundle deploy with the appropriate target in the pipeline.. Reproducible and traceable deployments require that the bundle and notebooks are versioned together and deployed from a specific commit. Using environment-specific targets in databricks.yml ensures consistent deployment across workspaces. Manual edits, embedded credentials, and local deployments all break reproducibility and traceability, so they must be avoided.

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Deploy the bundle from a developer's local machine to production to avoid the complexity of a CI/CD runner.

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Manually edit the deployed job in the production workspace after deployment to apply urgent fixes, and sync those changes back to Git later.

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Embed production credentials directly in the databricks.yml file so the pipeline can authenticate without additional configuration.

Question 12

Refer to the exhibit. A data engineer is using this command to reload data into a Delta table after a schema correction. What is the primary effect of setting the 'force' option to 'true' in this context?

Accepted Answer

The command will re-ingest all files, even if they were loaded before.. The COPY_OPTIONS parameter includes a force setting that determines whether the command should re-process files that have been loaded previously. By default, COPY INTO is idempotent and skips files that are already present in the transaction log. Setting force to true overrides this behavior, allowing the engineer to perform a full reload of the existing data.

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The command will automatically vacuum the target table before loading.

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The command will bypass schema validation for the incoming JSON files.

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The command will delete the source files from S3 after a successful load.

Question 13

A data engineer is setting up a Lakeflow Job that runs a notebook task. The engineer needs the task to always execute even if the upstream task in the workflow fails. Which configuration should be applied to the dependent task's condition?

Accepted Answer

Set the task's "Run if" condition to "All done".. In Lakeflow Jobs, task dependencies use "Run if" conditions to control execution based on upstream task states. "All done" ensures the task runs after all dependencies finish, regardless of success or failure. This is the only condition that satisfies the requirement to always execute, making it suitable for tasks that must run unconditionally, such as cleanup or alerting steps.

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Set the task's "Run if" condition to "All succeeded".

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Set the task's "Run if" condition to "At least one failed".

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Set the task's "Run if" condition to "At least one succeeded".

Question 14

What is the primary goal of implementing 'environment parity' in a Databricks CI/CD pipeline?

Accepted Answer

To minimize failures caused by differences between testing and production.. Environment parity ensures that the development, staging, and production environments are identical in configuration, libraries, and settings. This eliminates 'environment drift,' where code fails in production due to subtle discrepancies. Achieving parity through Infrastructure-as-Code (IaC) is the cornerstone of reliable CI/CD, as it guarantees that testing in staging provides a true representation of how the code will behave in production, significantly reducing the frequency of deployment-related failures.

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To ensure every developer has full admin access to production.

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To reduce the cost of compute resources in development workspaces.

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To allow for manual code deployments in all environments.

Question 15

A data engineer wants to monitor the health and performance of Databricks Jobs over time. Which feature should they use to visualize trends, such as job success rates and average execution times, across multiple runs?

Accepted Answer

The Jobs Run History dashboard.. The Databricks Jobs UI provides a comprehensive view of job history, which is essential for identifying long-term performance trends and reliability issues. By analyzing these metrics, engineers can detect regressions, troubleshoot intermittent failures, and optimize costs by adjusting schedules or resources. This historic view is a cornerstone of operational maintenance in Databricks, ensuring that pipelines remain stable and performant as data volumes evolve.

Answer

The Spark SQL UI.

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The Delta Lake Time Travel feature.

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Cluster Ganglia Metrics.