Databricks-DE-Assoc Practice Test 10 — 10 Questions

Question 1

A data engineer is configuring Unity Catalog governance for a multi-department organization. Which TWO actions require the metastore admin or catalog owner to have a workspace-independent metastore assigned? (Choose TWO)

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

Accepted Answer

Binding a workspace to a Unity Catalog metastore to enable catalog-level data access.. A Unity Catalog metastore is the top-level container for metadata, governing three levels of namespacing (catalog, schema, table). Certain administrative tasks like assigning a metastore to workspaces or managing root storage locations require specific privileges and architectural scopes, ensuring centralized security governance across multiple workspaces.

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Creating a new catalog inside the Unity Catalog metastore.

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Granting the SELECT privilege on a specific schema to a group of data analysts.

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Defining a custom dynamic view with column-level masking functions for auditing.

Question 2

Which TWO of the following are benefits of using Unity Catalog over the legacy Hive Metastore?

Accepted Answer

Unified governance across all workspaces.. Unity Catalog represents a modern approach to data governance, solving the fragmentation caused by having separate metastores in every workspace. By providing a centralized, account-level view of all data, it enables enterprise-wide discovery, consistent security policies, and deep integration with lineage and audit features. This shift simplifies the data engineer's job by reducing configuration overhead while significantly improving the organization's security posture and audit readiness for compliance requirements.

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Support for more SQL dialects.

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Increased query performance.

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Lower storage costs.

Question 3

An engineer notices that a specific notebook job is consistently taking longer to start. They observe high 'initialization' times in the job logs. Which action should the engineer take to improve startup time?

Accepted Answer

Use a Databricks Pool for the job cluster.. Initialization time in Databricks jobs often stems from the time required to pull container images, install cluster-scoped libraries, or initialize the Spark context on a new cluster. By using an existing cluster (pool) or pre-warming the cluster, the overhead of provisioning hardware and installing dependencies is removed. This optimization is crucial for meeting strict SLAs in production pipelines where every minute of latency impacts downstream processes.

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Enable Auto-scaling on the cluster.

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Increase the number of worker nodes.

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Update the notebook code to use RDDs.

Question 4

An analytics team needs to frequently query a large Delta table by a high-cardinality customer_id column and a date column. To optimize query performance and reduce data scanning during filtering, how should the data engineer structure the table layout?

Accepted Answer

Partition the table by date and apply Liquid Clustering on customer_id.. Partitioning the Delta table by the low-cardinality date column avoids the creation of excessive tiny files, while Liquid Clustering on customer_id organizes the data layout dynamically based on clustering keys. This hybrid approach leverages the strengths of both features: partition pruning for temporal ranges and liquid clustering for high-cardinality point lookups, significantly improving downstream query execution efficiency.

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Partition the table by both customer_id and date simultaneously.

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Apply Z-Order clustering exclusively on date without any partitioning.

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Rely entirely on Spark AQE to automatically index customer_id at runtime.

Question 5

A data engineering team uses Databricks Asset Bundles (DABs) to manage a job that must deploy to both a staging and a production workspace. The team wants to avoid hardcoding workspace-specific values such as the cluster ID and the storage path in databricks.yml. Which approach should they use?

Accepted Answer

Define variables in the bundle and set target-specific values in databricks.yml under targets, then deploy with databricks bundle deploy -t <target>.. Databricks Asset Bundles use a declarative databricks.yml with variables and targets. Declaring variables once and overriding them per target lets one bundle deploy consistently to staging and production without hardcoded workspace-specific values. Selecting the target at deploy time applies the correct values, keeps a single source of truth in Git, and avoids drift from manual edits or post-deployment patching.

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Run databricks bundle deploy once, then use the Databricks REST API to patch the job's cluster ID and storage path after each deployment.

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Store the cluster ID and storage path in a Databricks secret scope and reference them with dbutils.secrets.get inside the job's notebook code.

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Create a separate Git branch for each workspace and manually edit the cluster ID and storage path in databricks.yml before each deployment.

Question 6

Which of the following is the primary benefit of using a 'Job Cluster' rather than an 'All-Purpose Cluster' for running scheduled data pipelines?

Accepted Answer

It provides lower pricing for compute resources.. Job clusters are ephemeral and specifically designed for automated tasks, offering significant cost savings compared to all-purpose clusters. By terminating automatically after the job completes, they eliminate idle compute time costs. Understanding this distinction is essential for data engineers to balance performance requirements with budgetary constraints, as choosing the correct compute type directly impacts the overall operational efficiency and cost-effectiveness of the data platform.

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It allows interactive development in notebooks.

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It supports faster library installation.

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It caches data indefinitely for all users.

Question 7

Which TWO of the following are primary benefits of using Delta Live Tables (DLT) for data pipeline development?

Accepted Answer

Declarative pipeline development. Delta Live Tables simplifies pipeline development by providing declarative syntax and automated infrastructure management. It handles complex tasks like dependency resolution and data quality enforcement automatically. By reducing the manual effort required to manage pipeline state and quality, DLT enables engineers to focus on business logic, ensuring that data pipelines are more robust, maintainable, and reliable across the enterprise, which is crucial for large-scale data engineering teams.

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Manual management of cluster nodes

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Exclusive support for Python-only pipelines

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Requires a dedicated standalone database

Question 8

A team is implementing a CI/CD process for their Delta Live Tables (DLT) pipelines. Which THREE of the following practices are recommended to ensure reliable deployment?

Accepted Answer

Use Git branches to manage feature development and production code.. Reliable DLT deployments depend on version control, automated testing, and environment abstraction. By defining DLT pipelines as code via DABs or Terraform, teams ensure consistent environments. Testing code before deployment prevents runtime errors, and using Git-based workflows allows for code reviews, which are essential for maintaining high-quality pipelines. These practices collectively ensure that DLT pipeline changes are predictable, verifiable, and safe to deploy into production environments without disrupting existing operations.

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Keep all DLT configuration values hardcoded within the pipeline notebook.

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Manually update the pipeline source code in the production workspace.

Question 9

Refer to the exhibit. A CI/CD pipeline fails with the provided error. What is the most likely cause?

Accepted Answer

The Service Principal lacks sufficient permissions for the workspace path.. The 403 Forbidden error indicates that the identity running the command lacks the necessary permissions to perform the action. In a CI/CD context, this usually happens because the Service Principal lacks 'Can Manage' or 'Can Edit' permissions on the target Repos folder. Ensuring the Service Principal has the correct workspace-level permissions is a standard requirement for successful automated deployments, preventing access issues during the sync process.

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The branch 'main' does not exist in the remote repository.

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The Databricks CLI is not installed on the runner.

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The Git repository URL is invalid.

Question 10

A data engineer is troubleshooting a slow-running Spark job on Databricks. Which TWO metrics in the Spark UI are most useful for identifying data skew?

Accepted Answer

Task Duration (max vs median). Identifying data skew requires looking for significant disparities in task execution times and data distribution across partitions. When one or two tasks take drastically longer than the others while processing similar data volumes, or when partition sizes are highly uneven, skew is likely present. Monitoring these metrics allows engineers to implement techniques like salting or broadcast joins to redistribute the data and balance the workload across executors.

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Executor CPU Usage

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Total Cluster Memory Usage

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Driver Memory Consumption