Q01 - Question
You need to configure access to customer transaction data in a PostgreSQL database. Copying the data into Azure Databricks would take weeks. Security requires that access to this data is governed by Unity Catalog permissions and appears in Unity Catalog audit logs. You have the CREATE CONNECTION privilege, and your compute uses a supported runtime and access mode. Which approach should you use?
Domain: Set up and configure an Azure Databricks environment (15–20%) Type: Single choice
- A. Create a foreign catalog directly, without a connection, by entering the database name in the catalog options.
- B. Create a materialized view over the PostgreSQL tables before any connection exists.
- C. Build an ETL pipeline that copies all tables into managed tables in a new catalog.
- D. Create a connection that references credentials stored in Databricks secrets, then create a foreign catalog that uses the connection and names the external database.
D is correct.
Explanation: A connection stores the details needed to reach the external database, including host, port, and credentials. A foreign catalog uses that connection and mirrors the named external database in Unity Catalog. Users then query the foreign tables with standard SQL under Unity Catalog permissions, and the queries appear in the audit logs. No data migration is required.
A is incorrect: The foreign catalog is created with the USING CONNECTION clause. The connection provides the authentication and network details, so a connection is required first.
B is incorrect: A materialized view needs a source that is already accessible in Unity Catalog. The connection and foreign catalog make the external tables accessible.
C is incorrect: An ETL copy would work against the requirement to avoid weeks of migration effort. A foreign catalog provides access without migrating the data.
Q02 - Question
You need to configure a production catalog. Production data must be stored in a dedicated Azure Data Lake Storage Gen2 container. Development workspaces must not be able to query or modify production data. An external location is already configured in Unity Catalog, and you have the CREATE MANAGED STORAGE privilege on it. Which two actions should you take?
Domain: Set up and configure an Azure Databricks environment (15–20%) Type: Multiple choice
- A. Keep the default workspace bindings so that all workspaces attached to the metastore can access the catalog.
- B. Create the catalog with a MANAGED LOCATION that points to the dedicated container.
- C. Use one shared catalog for all environments and separate production from development by schema name only.
- D. Configure workspace bindings so that only production workspaces can access the catalog.
B and D are correct.
Explanation: A managed location defines where Unity Catalog stores the data files for managed tables and volumes in the catalog, so the data is stored in the dedicated container. Workspace bindings control which workspaces can access the catalog. Restricting a production catalog to production workspaces prevents development environments from querying or modifying production data.
A is incorrect: New catalogs are accessible from all workspaces attached to the metastore by default. Development workspaces would still be able to access production data.
C is incorrect: Catalogs are the primary unit of data isolation, and each catalog can have its own storage location and access policies. Schema names alone do not provide a dedicated storage location or workspace restriction.
Q03 - Question
Production jobs on classic compute intermittently fail with a CLOUD_PROVIDER_RESOURCE_STOCKOUT error because the cloud provider has no capacity for the specified instance type. The jobs have no strict instance type requirement. A workspace admin wants new classic compute resources to fall back to compatible instance types automatically. What should the admin do?
Domain: Set up and configure an Azure Databricks environment (15–20%) Type: Single choice
- A. Increase the maximum worker count in the autoscaling settings
- B. Change the worker nodes to spot instances
- C. Enable auto flexible node types in the workspace Compute admin settings
- D. Set alternate_node_type_ids to an empty list by using the Clusters API
C is correct.
Explanation: Flexible node types fall back to compatible alternative instance types when the preferred type is unavailable. Compatible types share the same vCPU count, memory (within 100–110%), local disk configuration, CPU architecture, and OS image support. A workspace admin enables the feature by using the Enable auto flexible node types toggle in the workspace Compute admin settings. After that, all new classic compute resources use fallback instance types.
A is incorrect: Autoscaling adjusts the number of workers between a minimum and a maximum. It does not substitute a different instance type when the specified type is unavailable.
B is incorrect: Spot instances reduce cost, but Azure can reclaim them when it needs the capacity. They do not provide a fallback instance type for a stockout.
D is incorrect: Setting alternate_node_type_ids to an empty list disables flexible node types for an individual compute resource. Use it only for workloads with strict instance type requirements.
Q04 - Question
A data engineering group needs a compute resource assigned to the whole group. The workloads use RDD APIs. Only members of the group may attach notebooks to the compute. You plan to select Dedicated access mode and choose the group in the Single user or group field. Which two requirements must you meet? Select two.
Domain: Set up and configure an Azure Databricks environment (15–20%) Type: Multiple choice
- A. Set the access mode to No isolation shared
- B. Enable Unity Catalog on the workspace and use Databricks Runtime 15.4 or above
- C. Grant each group member the unrestricted cluster creation entitlement
- D. Grant the group CAN MANAGE permission on a workspace folder where members store notebooks and other artifacts
B and D are correct.
Explanation: To create compute dedicated to a group, you must enable Unity Catalog on the workspace and use Databricks Runtime 15.4 or above. The group also needs CAN MANAGE permission on a workspace folder, such as /Workspace/Groups/<groupName>, for notebooks, MLflow experiments, and other artifacts used on the group cluster.
A is incorrect: No isolation shared is a legacy access mode that exposes service account keys to users with CAN ATTACH TO permission. Group compute uses Dedicated access mode.
C is incorrect: The unrestricted cluster creation entitlement lets non-admin users create clusters without size restrictions. It is not a requirement for assigning compute to a group.
Q05 - Question
You need to configure a Genie space for sales managers to ask questions about opportunities. They ask about "pipeline status," but the source column is named fcst_cat and Genie does not map their wording to it. You must resolve this without changing the underlying Unity Catalog objects. What should you do?
Domain: Set up and configure an Azure Databricks environment (15–20%) Type: Single choice
- A. Rename the fcst_cat column in the source table in Unity Catalog.
- B. In the Genie space, update the column description to explain the forecast categories and add synonyms such as "forecast," "category," and "pipeline status."
- C. Add up to 25 more tables to the Genie space so that Genie has more context.
- D. Create 100 example SQL queries that cover every possible question.
B is correct.
Explanation: You can customize metadata at the space level without altering the underlying Unity Catalog objects. Updating the column description and adding synonyms lets Genie match the managers' language to the fcst_cat column.
A is incorrect: Renaming the source column changes the underlying Unity Catalog object. You can refine descriptions and synonyms within the Genie space instead.
C is incorrect: A space should use a small, focused set of tables for a focused set of business questions. Adding tables does not explain what the fcst_cat column means.
D is incorrect: The instructions limit is 100, and the guidance is to start with minimal instructions and expand based on actual user questions. Example queries do not replace column descriptions and synonyms for this mapping problem.
Q06 - Question
A data science team plans to train deep learning models on GPU instances. The team wants popular machine learning libraries and GPU drivers to be available without manual setup. Which configuration meets these requirements?
Domain: Set up and configure an Azure Databricks environment (15–20%) Type: Single choice
- A. Select a Databricks Runtime ML version, use GPU-enabled instances, and disable Photon
- B. Select a standard Databricks Runtime LTS version, use GPU-enabled instances, and keep Photon enabled
- C. Select a Databricks Runtime ML version, use GPU-enabled instances, and keep Photon enabled
- D. Select a standard Databricks Runtime version and use memory-optimized E-series instances with Photon enabled
A is correct.
Explanation: Databricks Runtime ML includes pre-installed machine learning libraries, GPU drivers, and frameworks such as CUDA. GPU instances support deep learning training. Photon isn't supported on GPU-enabled clusters, so you must disable it.
B is incorrect: A standard runtime does not include the machine learning libraries and GPU drivers that Databricks Runtime ML provides. Photon must also be disabled on GPU instances.
C is incorrect: The runtime and instance choices are correct, but Photon isn't compatible with GPU instances. Leaving it enabled does not meet the requirement.
D is incorrect: E-series instances are memory-optimized and do not provide GPUs. A standard runtime also does not include the machine learning libraries and GPU drivers.
Q07 - Question
A team shares a cluster that uses standard access mode. The team must install the Microsoft JDBC Driver for SQL Server from Maven by using the coordinates com.microsoft.sqlserver:mssql-jdbc:13.2.1.jre11. The organization wants centralized review of libraries that run on shared compute. What should the team do?
Domain: Set up and configure an Azure Databricks environment (15–20%) Type: Single choice
- A. Run a pip3 command from a notebook to install the driver
- B. Upload the JAR to workspace files, because this method does not require allowlist approval
- C. Install the driver by using an init script instead of a cluster library
- D. Ask a metastore admin to add the Maven coordinates to the allowlist, then install the library on the cluster
D is correct.
Explanation: On clusters that use standard access mode, Maven coordinates and JAR file paths require allowlist approval before installation. A metastore admin adds the coordinates in the format groupId:artifactId:version to the allowlist. This gives admins review of the libraries that run on shared compute. You need CAN MANAGE permission on the cluster to install the library.
A is incorrect: Ad-hoc commands such as pip3 bypass centralized management and security controls. A pip3 command also does not install a Java JAR library.
B is incorrect: For clusters that use standard access mode, library file paths in workspace files must also be added to the allowlist before installation.
C is incorrect: Init scripts are not recommended for library installation, because cluster-scoped libraries are the better approach. On standard access mode, the init script path also requires its own allowlist entry.
Q08 - Question
You need to store machine learning model files in a Unity Catalog volume. One group of files must be restricted to the data science team. Another group of files must be readable by all analysts. You need to enforce this separation using Unity Catalog permissions. What should you do?
Domain: Set up and configure an Azure Databricks environment (15–20%) Type: Single choice
- A. Create a separate volume for each security boundary and grant READ VOLUME on each volume only to the appropriate group.
- B. Keep one volume and apply subdirectory-level ACLs to the restricted folder.
- C. Convert the volume to an external volume so that the folders can have separate permissions.
- D. Grant READ VOLUME at the catalog level to the analysts and the data science team.
A is correct.
Explanation: Volume permissions apply to the entire volume. Unity Catalog volumes do not support folder-level or subdirectory-level ACLs. To apply different access controls to different sets of files, create a separate volume for each security boundary.
B is incorrect: Volumes do not support subdirectory-level ACLs, so this configuration cannot be applied.
C is incorrect: Managed and external volumes use the same permission model. The main difference is storage control and data lifecycle, so converting the volume type does not add folder-level permissions.
D is incorrect: A READ VOLUME grant at the catalog level cascades to all volumes in that catalog. This gives analysts access to the restricted files.
Q09 - Question
A team runs a Python batch job on a nightly schedule. The job is not latency sensitive. Unity Catalog is enabled, and the job does not use RDD APIs, R, or JAR libraries. The team wants to reduce DBU consumption without managing clusters. Which configuration should the team use?
Domain: Set up and configure an Azure Databricks environment (15–20%) Type: Single choice
- A. Serverless compute for jobs in performance-optimized mode
- B. Serverless compute for jobs in standard performance mode
- C. A classic all-purpose cluster that uses an instance pool
- D. A pro SQL warehouse
B is correct.
Explanation: Standard performance mode is optimized for cost. It can reduce DBU consumption by up to 70% compared to performance-optimized mode. Jobs typically start within 4 to 6 minutes in this mode, which is acceptable for a scheduled batch job. Serverless compute also removes the need to manage clusters.
A is incorrect: Performance-optimized mode is the default and starts in seconds. It is intended for interactive or latency-sensitive workloads, so it does not give the DBU reduction that this scenario requires.
C is incorrect: A classic cluster requires you to manage configuration. An instance pool also means you pay for virtual machine costs while instances are idle.
D is incorrect: A pro SQL warehouse is optimized for SQL queries and is used when you need custom networking. It is not the compute option for a Python batch job.
Q10 - Question
You need to build the bronze layer of a medallion architecture. JSON files land continuously in a Unity Catalog volume, and rows only ever arrive as new records. The dataset grows without limit. You need to configure the ingestion so that each refresh processes only the rows that arrived since the last refresh. Which object should you create?
Domain: Set up and configure an Azure Databricks environment (15–20%) Type: Single choice
- A. A standard view that selects from the landing files.
- B. A materialized view that aggregates the landing files.
- C. A streaming table created with CREATE OR REFRESH STREAMING TABLE and the STREAM keyword.
- D. An external table that uses a LOCATION clause for the landing path.
C is correct.
Explanation: Streaming tables use streaming semantics to process only new rows that arrive after the last refresh. They are intended for incremental ingestion, such as landing files and building bronze layers where data only arrives as new rows. They do not reprocess previously seen data during a normal refresh.
A is incorrect: A standard view does not store data. It runs the underlying query each time you access it, so it does not provide incremental ingestion.
B is incorrect: Materialized views use batch semantics to precompute results over bounded data. They are suited to aggregations over a complete dataset where periodic recomputation is acceptable, not append-only ingestion.
D is incorrect: An external table registers existing data in place and is useful when other systems manage the data. It does not process only the new rows on each refresh.