Databricks-DE-Pro Multiple Select — 10 Questions

Question 1

A data engineer is investigating a job failure that occurred only in the production environment. Which TWO features in Databricks help in comparing the production environment to the development environment?

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

Accepted Answer

Use the 'View as JSON' feature in the Jobs UI to compare job configurations.. Comparing environment configurations is essential for troubleshooting parity issues. Databricks provides tools like the Jobs UI for export and version control integration for code, which help engineers identify subtle differences between environments. Ensuring parity is critical for reducing 'it works on my machine' scenarios. By utilizing these tools, engineers can verify cluster settings, library versions, and code versions to isolate why a process fails in production but succeeds in development.

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Use the 'Cluster Logs' to compare the OS kernel versions of the nodes.

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Enable the 'Debug Mode' on the Spark Driver to see raw system calls.

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Query the 'workspace_users' table to see who ran the job last.

Question 2

A data engineering team is modeling a large Delta Lake fact table that stores clickstream events. Analysts frequently run queries that filter by event_date and then aggregate by user_id, and the table receives continuous appends plus occasional late-arriving corrections. The team wants to reduce bytes scanned and improve join performance. Which two design choices are most appropriate? (Choose two.)

Accepted Answer

Partition the table by event_date and apply Z-ORDER on user_id within each partition. The workload filters on event_date and aggregates by user_id, so layout must accelerate both. Partitioning by event_date with Z-ORDER on user_id gives reliable partition pruning plus co-located user data, while liquid clustering on both columns achieves similar skipping without rigid high-cardinality directories and can be adapted incrementally as access patterns change.

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Convert the table to a Parquet external table and rely on the query engine to infer partitioning

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Enable Change Data Feed on the table so late-arriving corrections are captured automatically

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Partition the table by user_id to maximize file skipping for user-level aggregations

Question 3

A data engineer is implementing fine-grained access control on a Delta table in Unity Catalog that contains sensitive customer data. The requirement is to mask the `credit_card` column for all users except members of the `finance` group, and to filter out rows where the `region` column is not in the user's allowed regions. Which two Unity Catalog features should the engineer use? (Choose two.)

Accepted Answer

Row filter using a SQL UDF that checks the user's allowed regions against the `region` column.. Unity Catalog provides native row filters and column masks for fine-grained access control. A column mask with a SQL UDF can conditionally reveal or obscure the credit card column based on group membership, while a row filter with a SQL UDF can restrict rows by region. Together they implement the required masking and filtering directly on the table without requiring a separate view.

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Table ACLs that grant SELECT only on specific columns.

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Workspace-level IP access list to restrict access to the table.

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Dynamic view that joins the table with a mapping table of user regions.

Question 4

A data engineer is tasked with reducing compute costs for an interactive SQL analytics workspace that runs sporadic, highly unpredictable queries. The jobs experience cold start delays and occasional out-of-memory errors due to sudden concurrency spikes. Which TWO strategies should the engineer implement to balance cost efficiency and performance?

Accepted Answer

Migrate interactive SQL workloads to Databricks Serverless Compute to dynamically scale resources and eliminate idle billing.. Implementing serverless compute eliminates idle cluster costs while absorbing concurrency spikes instantly through elastic scaling. Enabling Photon on standard clusters provides vectorized query execution that speeds up scans and aggregations, reducing runtime and lowering total execution costs. Together, these choices optimize both resource utilization and user concurrency responsiveness.

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Configure single-node clusters with maximum autoscaling limits to handle unpredictable concurrency peaks without cluster management overhead.

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Provision pools of pre-warmed idle driver nodes to ensure zero-second startup latency for all analytical queries.

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Disable automatic cluster termination and keep all worker nodes running 24/7 to guarantee immediate resource availability.

Question 5

Which TWO of the following are true regarding Unity Catalog's ability to govern external locations?

Accepted Answer

External locations require a storage credential to function.. Unity Catalog can manage access to external cloud storage locations by creating 'External Locations'. This allows administrators to grant specific permissions to users to read from or write to these storage buckets without providing them with direct cloud provider credentials. This abstraction is vital for security, as it centralizes control and auditing of data access within the platform while keeping the underlying storage infrastructure shielded from the users.

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Users can directly mount external locations as DBFS paths.

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External locations are automatically created for every S3 bucket in the account.

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External locations are only supported for Delta-formatted data.

Question 6

Which TWO of the following are valid ways to trigger a job in Databricks?

Accepted Answer

Using the 'Jobs' UI to trigger a run.. Databricks provides multiple interfaces for job orchestration. The 'Jobs' UI in the workspace allows for interactive configuration and scheduling. Alternatively, the Databricks REST API provides a programmatic way to trigger jobs, which is crucial for CI/CD pipelines and integrating Databricks with external orchestrators like Airflow or Azure Data Factory. Using these methods ensures that pipelines can be automated, monitored, and integrated into broader enterprise data workflows.

Answer

Executing the 'run_job' command inside a notebook cell.

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Running the 'dbutils.jobs.run()' method.

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Creating a file named 'trigger.job' in DBFS.

Question 7

A data engineer is optimizing a Spark job that reads from a large Delta table and performs a join with a smaller dimension table. The job is running slowly, and the engineer suspects data skew and shuffle overhead are the main issues. Which two techniques should the engineer apply to improve performance and reduce cost? (Choose two.)

Accepted Answer

Enable adaptive query execution (AQE) and set spark.sql.adaptive.skewJoin.enabled to true.. Broadcast join eliminates shuffle for the smaller table, and enabling AQE with skew join optimization dynamically handles skew in the larger table. Together, they reduce shuffle overhead and mitigate stragglers, improving performance and lowering cost. Other options either introduce unnecessary shuffles or do not directly address skew.

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Increase the number of shuffle partitions to 2000.

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Cache the larger table in memory before the join.

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Repartition the larger table on the join key before the join.

Question 8

A data engineer is setting up a Delta Share to provide an external partner with access to a subset of data. The partner will use a non-Databricks client that supports the Delta Sharing protocol. Which two actions are required to enable the partner to access the shared data? (Choose two.)

Accepted Answer

Create a recipient in Unity Catalog and provide the partner with the activation link or credential file.. To enable an external partner to access a Delta Share, two key steps are required: creating a recipient and providing them with the credential file or activation link, and adding the table to a share while granting the recipient access to that share. These actions establish the secure sharing relationship and define the data scope. Other options are either not applicable to non-Databricks clients or not part of the Delta Sharing setup process.

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Configure a Databricks SQL warehouse for the recipient to query the shared data.

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Enable the Delta Sharing server on the Databricks workspace.

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Grant the recipient SELECT privileges on the shared table.

Question 9

A data engineer is designing a Unity Catalog governance model for a new data lakehouse. They need to ensure that data access is auditable and that sensitive data is protected. Which two actions should the engineer take to meet these requirements? (Choose two.)

Accepted Answer

Enable audit logging for the metastore to capture all access and permission changes.. Enabling audit logging captures all access and permission changes, providing the necessary audit trail. Applying tags to sensitive columns and using ABAC policies enforces fine-grained access control based on those tags, protecting sensitive data consistently. Together, these actions create a governance model that is both auditable and secure, leveraging Unity Catalog's native capabilities for centralized policy management.

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Use dynamic views to mask PII columns for all users except administrators.

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Create a separate metastore for each department to isolate data.

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Store all data in a single catalog and grant `ALL PRIVILEGES` to the data engineering team.

Question 10

A data engineer is preparing to deploy a production Databricks workflow. Which TWO best practices should be implemented to ensure maintainability and robust error handling?

Accepted Answer

Use Databricks Git folders for version control of production code.. Maintaining production environments requires strict adherence to modular code design and comprehensive observability. Using version control for notebooks and job configurations ensures that every change is tracked, audited, and reversible. Simultaneously, implementing robust notification alerts for job failures allows engineering teams to respond proactively to issues. These two practices collectively minimize the risk of deployment errors and reduce the Mean Time to Resolution (MTTR) when unexpected failures occur in production pipelines.

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Hardcode credentials directly into the notebook for ease of access.

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Avoid using libraries and rely only on built-in Spark functions.

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Deploy code directly from the workspace to production.