HOTSPOT (Drag and Drop is not supported)You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Table1.Table1 is written by batch jobs every hour and is queried frequently by filtering two columns named CustomerId and EventDate.You expect Table1 to grow significantly over time.The rows in Table1 are frequently updated and deleted to support compliance requests.You need to keep query performance consistent as Table1 grows. The solution must minimize update and deletion effort.What should you include in the solution? To answer, select the appropriate options in the answer area.Note: Each correct selection is worth one point.Hot Area:
Answer(s): A
Box 1: Liquid Clustering To optimize query performance, implement:To optimize query performance for a rapidly growing, frequently updated managed Delta table filtered by customer and date in Unity Catalog, you must implement Liquid Clustering on both the customer and date columns, while running regular OPTIMIZE and VACUUM maintenance.Liquid Clustering replaces traditional partitioning and Z-Ordering, drastically improving performance for tables with high-frequency write, update, and delete workloads.Box 2: Enable deletion vectors. To optimize updates and deletions:Write and Execution Strategies Use Deletion Vectors: Ensure Deletion Vectors are enabled (default in Databricks Runtime 12.1+). They record deletes and updates in a separate lightweight file instead of rewriting the entire data file immediately.Leverage MERGE INTO: Execute updates and deletions in a single atomic batch using the MERGE command. Ensure your ON search condition explicitly includes filters for both the customer and date columns to trigger file pruning.
You have an Azure Databricks workspace that contains a cluster named Cluster1.Performance monitoring shows that Cluster1 is consistently overprovisioned for its batch workload:-CPU utilization remains below 20 percent, including peak processing periods.-The workload is highly predictable and does not spike.-The current node type already meets the workload requirements.You need to reduce compute costs without increasing job duration.What should you do?
Answer(s): D
To drastically reduce compute costs without extending the job duration, you should reduce the number of worker nodes in the cluster configuration.Low Utilization: A consistent CPU utilization below 20% confirms that the cluster has far more processing power than the batch workload requires.Predictable Workload: Because the workload is completely stable and does not experience random spikes, you do not need to keep excess buffer nodes active.Appropriate Node Type: The prompt explicitly states the current node type meets the requirements. Changing the VM size/type to a smaller variant could risk running out of memory (OOM) or causing disk spilling, which would degrade performance and extend job duration.Linear Cost Reduction: Reducing the node count directly cuts down both the Azure VM infrastructure costs and Databricks Unit (DBU) consumption.Best Practices for Implementation To safely execute this change for your batch workload, configure the following settings: *-> Use a Fixed Size (Disable Autoscaling): Since the workload is highly predictable and does not spike, use a fixed-size cluster. Autoscaling can add unnecessary cluster startup and step-up latency, which can negatively impact batch job windows.Migrate to Job Compute: If this batch workload is currently running on an All-Purpose cluster, migrate it to an Automated Job Cluster. Job clusters cost significantly less per DBU than interactive clusters and automatically terminate immediately after the job finishes.
https://www.linkedin.com/posts/nupur-zavery-4a47811b0_databricks-jobs-are-slow-usually-for-these-activity-7395852001772867584-WF58
You have an Azure Databricks workspace.You are creating a Lakeflow Spark Declarative Pipelines (SDP) pipeline that scales automatically.You need to configure compute for the pipeline. The solution must minimize operational costs and administrative effort.What should you use?
To best achieve an automatically scaling compute environment for Lakeflow Spark Declarative Pipelines (SDP) while keeping operational and administrative costs low, you should deploy the pipeline using Serverless Compute configured in Standard Performance Mode with an enforced Max Workers limit.This combination eliminates the administrative overhead of infrastructure management, leverages enhanced vertical and horizontal auto-scaling, limits cost exposure from unexpected spikes, and optimizes financial efficiency.
https://docs.databricks.com/aws/en/ldp/auto-scaling
You have an Azure Databricks solution that was developed by multiple engineers and deployed to development, test, and production environments.You need to implement an application lifecycle management (ALM) process that meets the following requirements:-Deploys and provides source control for Databricks notebooks, workflows, and jobs in a consistent and repeatable way-Parameterizes the target deployment environmentWhat should you include in the solution?
You should add Databricks Asset Bundles (DABs) to your Git repository to fulfill these Application Lifecycle Management (ALM) requirements.Databricks Asset Bundles represent the modern, declarative toolset specifically designed by Databricks to manage notebooks, workflows, pipelines, and jobs as code across multi-workspace environments (Development, Test, and Production).
https://www.freshgravity.com/insights-blogs/streamlining-databricks-deployments/
HOTSPOT (Drag and Drop is not supported)You have an Azure Databricks workspace that contains a cluster named Cluster1.Cluster1 has the following characteristics:-Configured as a single node cluster-Uses a general purpose virtual machine node typeThe cluster runtime environment has the following configurations:-Uses Databricks Runtime for Machine Learning (Databricks Runtime ML)-Includes common machine learning libraries-Supports Python workloadsFor each of the following statements, select Yes if the statement is true. Otherwise, select No.Note: Each correct selection is worth one point.Hot Area:
Box 1: No No - Cluster1 can be used to train learning models that require GPU acceleration.GPU Acceleration is not supported in Cluster1: General Purpose VM: General purpose virtual machines (like Azure's Standard_D or Standard_E series) run on standard CPUs, not GPUs.Missing Hardware: Deep learning frameworks (like PyTorch or TensorFlow) need physical GPU cores (Tensor Cores) to speed up model training.Runtime Mismatch: The standard Databricks Runtime ML installs CPU versions of machine learning libraries unless a GPU-specific runtime version is selected.Box 2: No No - Cluster1 can distribute machine learning workloads across multiple nodes.Why Distribution is Not Possible in Cluster1: Single Node Architecture: A single node cluster operates entirely on one virtual machine.No Worker Nodes: It contains only a driver node and zero worker nodes.Local Execution Only: All computations, library executions, and Python workloads run locally on that single machine.Box 3: Yes Yes - Cluster1 can run Python workloads that rely on preinstalled machine learning libraries.The setup perfectly aligns with the requirements for single-node machine learning workflows:Databricks Runtime ML: This specialized runtime environment automatically packages and preinstalls popular open-source data science and machine learning packages. These include frameworks like scikit-learn, TensorFlow, PyTorch, and XGBoost, allowing Python workloads to run right out of the box without requiring manual package installations.Single Node Configuration: In Azure Databricks, Single Node compute is specifically designed for light-duty data engineering or non-distributed machine learning libraries. It runs both the Spark driver and worker tasks on a single virtual machine node.General Purpose VM Node Type: Standard general-purpose Azure virtual machines (such as D-series SKUs) are fully compatible with CPU-optimized ML workloads (e.g., scikit-learn models or CPU-based neural networks).
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-configure-environment?view=azureml-api-2
You have an Azure Databricks workspace that contains the objects shown in the following table.Users often use the following words to refer to a sale: transaction, event, order, and invoice.You need to create a knowledge store. The solution must ensure that when the users use any of the words in Space1, Genie queries the Sales1 table. Any other Genie spaces must remain unaffected.To which object should you add the instructions?
Answer(s): C
To map specific user terminology to your table without affecting other spaces, you should add Synonyms to the knowledge store metadata of the Genie Space.Bridges Business Jargon: Adding transaction, event, order, and invoice as synonyms tells Genie that these terms are fully interchangeable with your target table or its columns.Space-Scoped Isolation: Metadata configured inside the knowledge store is explicitly scoped locally to that specific Genie Space. It will not overwrite your underlying Unity Catalog metadata or influence how other Genie Spaces interpret those words.How to apply it: Open your specific AI/BI Genie space.Click on Configure > Data.Select your target table. Under the table or column metadata, find the Synonyms field and add the words: transaction, event, order, and invoice.
https://www.databricks.com/blog/data-dialogue-best-practices-guide-building-high-performing-genie-spaces
DRAG DROP (Drag and Drop is not supported)You have an Azure Databricks workspace named Workspace1 that is attached to a Unity Catalog metastore named metastore1.You need to register an Azure Storage account named account1 that has a hierarchical namespace enabled as an external location. The external location must use a managed identity to authenticate to account1 and the solution must follow the principle of least privilege.Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.Select and Place:
To register an Azure Storage account (ADLS Gen2) as an external location using a managed identity and an Access Connector under the principle of least privilege, you must complete the following configuration workflow:Step 1: Create a Databricks access Connector Action 1: Create an Access Connector for Azure Databricks Navigate to the Azure Portal. Create a new resource and search for Access Connector for Azure Databricks. Configure it in the same Azure region as your ADLS Gen2 storage account. Ensure the System-assigned managed identity status is turned On (or attach a specific user-assigned managed identity).Once deployed, navigate to its properties and copy the Resource ID.Step 2: Assign Workspace1 the Storage Blob Data Contributor role for account1. Action 2: Configure Least-Privilege IAM Roles on the Storage Account Navigate to your hierarchical namespace-enabled Azure Storage Account. Open Access Control (IAM) and click Add role assignment. *-> Assign the Storage Blob Data Contributor role. Note on Least Privilege: To enforce true least privilege, avoid assigning this role at the entire storage account level. Instead, scope this role assignment specifically to the target container inside the storage account. Select Managed identity as the assignee type and select your Access Connector.Step 3: Register the access connector as a storage credential in metastore1 Action 3: The Databricks access connector must be registered in the metastore (via a Unity Catalog Storage Credential), not in the individual workspace.Centralized Security: Unity Catalog manages security at the account/metastore level.Cross-Workspace Access: Registering it in the metastore allows the credential to be safely shared across multiple workspaces attached to that same metastore.Object Hierarchy: In Unity Catalog, an External Location relies on a Storage Credential. Both are metastore-level securable objects.
HOTSPOT (Drag and Drop is not supported)You have an Azure Databricks workspace that is enabled for Unity Catalog.You need to ensure that data lineage is captured and can be reviewed for tables accessed by Databricks notebooks and jobs. The solution must minimize administrative effort.Which compute configuration should you use to capture the data lineage and what should you use to review the data lineage? To answer, select the appropriate options in the answer area.Note: Each correct selection is worth one point.Hot Area:
Box 1: A Serverless cluster To capture automated data lineage via Unity Catalog for both Databricks notebooks and jobs with low administrative overhead, you should use Serverless compute (or Shared access mode clusters if serverless is unavailable).Recommended Compute Options Serverless Compute (Highly Recommended): This option completely minimizes administrative overhead. It requires no infrastructure management, scales automatically, starts instantly, and captures lineage inherently for both Databricks Notebooks and Jobs.Box 2: Catalog Explorer To review data lineage in an Azure Databricks workspace enabled for Unity Catalog, you should use Catalog Explorer.Because you are using Unity Catalog and Serverless compute, table-level and column-level data lineage is automatically captured for notebooks and jobs without any manual configuration or administrative overhead.View Lineage in Catalog Explorer You can visually explore how data flows between tables by navigating through the Azure Databricks user interface: Open the Catalog icon in the sidebar. Select the desired catalog, schema, and table. Click the Lineage tab. Click See lineage graph to view an interactive map of upstream and downstream dependencies.
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