Databricks-DE-Assoc Practice Test 2 — 10 Questions

Question 1

A team stores Databricks notebooks and job definitions in a Git repository and wants every merge to the main branch to automatically deploy to production. Which combination of practices should the pipeline implement to achieve this safely?

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

Accepted Answer

Trigger the deployment workflow on pushes to the main branch, authenticate with a service principal whose credentials are stored in the CI system's secret manager, and run databricks bundle deploy.. Safe continuous deployment requires two things: a trigger scoped to the main branch so only reviewed, merged code reaches production, and non-interactive authentication via a service principal stored in the CI secret manager. Running databricks bundle deploy from the merged commit then applies the bundle definition reproducibly. Scheduling, branch-agnostic triggers, and personal tokens all undermine control or automation.

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Trigger the deployment workflow on a nightly schedule and use a personal access token for authentication so the deployment uses the developer's existing permissions.

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Trigger the deployment workflow on pushes to any branch and use the Databricks CLI with interactive browser login to authenticate to the production workspace.

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Trigger the deployment workflow on pull requests and use the developer's personal access token stored in the repository as a plaintext secret.

Question 2

When configuring a Databricks Job, which TWO factors most directly influence the choice between a 'Job Cluster' and an 'All-Purpose Cluster'?

Accepted Answer

The need for interactive debugging and notebook testing.. Choosing the right cluster type is vital for balancing cost and performance. Job Clusters are ephemeral, optimized for production tasks, and significantly cheaper, whereas All-Purpose Clusters are persistent and intended for interactive development. Understanding the cost implications and lifecycle management of these clusters is fundamental for any Databricks engineer responsible for managing operational budgets and ensuring efficient resource utilization across various environments.

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The requirement for high availability during query execution.

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The number of users concurrently using the cluster.

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The type of data source (e.g., S3 vs. ADLS).

Question 3

A data engineer is designing a workflow that requires running a series of tasks with dependencies. The workflow must be able to retry failed tasks automatically and send notifications on failure. The engineer also needs to ensure that the workflow can be triggered on a schedule and via an API. Which Databricks feature should the engineer use?

Accepted Answer

Databricks Jobs with task dependencies and retry policies.. Databricks Jobs are the primary orchestration feature for workflows with dependencies, retries, and notifications. They support multi-task DAGs, task-level retry policies, and alerting on failure. Jobs can be scheduled and triggered via API, making them the correct choice. The other options lack the necessary orchestration capabilities.

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Delta Live Tables pipelines with continuous mode.

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Databricks SQL dashboards with scheduled refresh.

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Databricks Repos with Git hooks.

Question 4

Which TWO of the following capabilities are native to the Databricks Unity Catalog?

Accepted Answer

Row and column-level security. Unity Catalog centralizes governance by providing a unified interface for managing permissions across workspaces. It enables fine-grained access control down to the row and column level and provides a centralized audit log. These capabilities are vital for organizations needing to manage data security at scale, ensuring that data engineers and scientists have consistent access control policies across the entire Lakehouse regardless of the specific compute resource or workspace utilized.

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Auto-scaling of compute clusters

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Automated ML model hyperparameter tuning

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Direct hardware driver installation

Question 5

A data engineer is configuring Unity Catalog row-level security on a table `prod.finance.transactions`. They want to ensure that users in the `us_team` group can only see rows where `region = 'US'`, while users in the `eu_team` group can only see rows where `region = 'EU'`. They create a row filter function `prod.security.region_filter`. Which TWO statements accurately describe how to implement and manage this row-level security? (Choose two.)

Accepted Answer

The row filter function must accept the column(s) as parameters and return a BOOLEAN value indicating whether the row should be visible.. Row-level security in Unity Catalog is implemented by attaching a row filter function to a table using ALTER TABLE ... SET ROW FILTER. The function receives column values and returns a boolean, often using identity functions to filter based on group membership. Both managed and external tables support row filters, and bypassing them requires the BYPASS ROW FILTER privilege.

Answer

Row filters can only be applied to external tables, not managed tables, because managed tables do not support fine-grained access control.

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The row filter function must be defined in the same schema as the table it protects.

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Users with the MODIFY privilege on the table automatically bypass the row filter and see all rows.

Question 6

A data engineer is working with a large Delta table and notices that queries filtering by 'region_id' are performing slowly. The table is currently partitioned by 'date'. Which strategy should the engineer use to optimize query performance for 'region_id' filtering without increasing the number of partitions?

Accepted Answer

Execute 'OPTIMIZE table_name ZORDER BY (region_id)'. Z-Ordering is the most effective technique for multidimensional clustering in Delta Lake, especially when partitioning by date. By Z-Ordering on 'region_id', the data engineer colocates related information within the same set of files. This significantly reduces the amount of data scanned during queries, improving performance without the overhead associated with high-cardinality partitioning, which could otherwise lead to the small file problem.

Answer

Change the partition column to 'region_id'

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Enable 'auto-compaction' on the table

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Convert the table to a Liquid Clustering table

Question 7

Which feature in Unity Catalog is primarily used to track data movement and transformation history for compliance and auditing?

Accepted Answer

Data Lineage. Data lineage provides a graphical and programmatic representation of data flow from source tables to downstream targets. It is vital for governance as it allows administrators to see exactly where data came from and how it was processed. This transparency is critical for compliance audits, allowing teams to quickly identify the impact of changes or potential data leakage points within the entire data ecosystem.

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Audit Logs

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Delta Sharing

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Catalog Explorer

Question 8

When integrating Databricks with a CI/CD tool like GitHub Actions, how should you securely manage the authentication token used for deployments?

Accepted Answer

Store the token as an encrypted secret in the CI/CD provider's secret store.. Managing secrets securely is vital for CI/CD. Storing tokens directly in code or pipeline definitions exposes them to unauthorized access. By using a secrets manager, you decouple the sensitive credentials from the pipeline logic. This ensures that the CI/CD platform can authenticate with Databricks dynamically during the execution phase, maintaining a high security posture while allowing for easy rotation of credentials without needing to refactor the entire codebase.

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Hardcode the Databricks Personal Access Token directly into the YAML pipeline file.

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Save the token in a public S3 bucket and download it when the pipeline starts.

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Use your own password as the token for easier memorization and access.

Question 9

A data engineer wants to run an incremental ingestion job every six hours. They want to ensure that each run processes all available data and then shuts down the cluster to save costs. Which Trigger should be used in the Structured Streaming code?

Accepted Answer

Trigger.AvailableNow. Trigger.AvailableNow is the modern replacement for Trigger.Once. It provides better scalability by processing all available data in multiple micro-batches if necessary, while still allowing the job to terminate once all data is processed. This is ideal for cost-effective, periodic batch-style processing of streaming data sources.

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Trigger.ProcessingTime('6 hours')

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Trigger.Once

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Trigger.Continuous('1 minute')

Question 10

Which of the following best describes the purpose of 'Credential Passthrough' in Databricks?

Accepted Answer

It enables users to access cloud storage using their own identity rather than the cluster's service principal.. Credential Passthrough allows users to authenticate to cloud storage using their own identity from the Databricks environment. By passing the user's credentials to the storage layer, Databricks ensures that the storage provider enforces access policies, creating a seamless audit trail and simplifying security management in environments that require strict alignment between user access in Databricks and direct cloud storage access.

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It allows a Databricks cluster to automatically scale based on storage latency.

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It encrypts data in transit between the Databricks workspace and the cloud storage.

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It forces all notebook users to use a single shared service principal for security.