DP-750Preview

Problem Statements -
The company’s existing analytics environment has several issues:

Ingestion -
Telemetry pipelines fall behind during peak loads.
Telemetry ingestion fails when schema drift occurs.
Streaming pipelines reprocess events after a pipeline restarts.

Compute -
Production and development workloads run on the same all-purpose clusters.
Production and development workloads do NOT support autoscaling or workload isolation.

Governance -
The ERP data is duplicated across systems and development teams.
Naming conventions are inconsistent across development teams, regions, and products.
Ownership of the IoT sensors changes over time, and analysts must track the full history of the ownership.
Occasionally, equipment manufacturers must correct data-entry mistakes in equipment names. Historical values are NOT required.

Pipeline operations -
Pipelines lack resiliency, alerting, and centralized scheduling.

Requirements -

Planned Changes -
Contoso plans to implement the following changes:
Implement scalable data pipeline orchestration.
Create a managed analytics catalog in Unity Catalog.
Implement a consistent approach to creating curated datasets.
Establish a centralized governance model across ingestion, cleansed, and curated layers.
Grant data engineers access to the ERP tables by using minimal development effort.
Adopt a compute strategy that isolates production workloads and supports autoscaling.
Adopt a slowly changing dimension (SCD) approach to address current data modeling issues.

Technical Requirements -
Contoso identifies the following environment and compute requirements:
Ensure that production ingestion workloads run on compute clusters that can scale automatically during telemetry spikes.
Provide fast and consistent performance for business intelligence (BI) workloads.
Prevent development activity from affecting production pipelines.
Production ingestion workloads must run as scheduled, non-interactive pipelines rather than on shared interactive development clusters.
Contoso identifies the following data ingestion and processing requirements:
Auto-scale ingestion pipelines to handle bursty workloads.
Handle schema drift for the maintenance and telemetry data.
Ingest file-based telemetry data by using minimal operational effort.
Store all the ingested data in a format that supports incremental processing.
Support the continuous ingestion of telemetry data from the event hubs by using exactly-once semantics.
Support the ingestion of the structured maintenance data from the Azure Database for PostgreSQL server.
Build a new telemetry pipeline that ingests raw events from the event hubs, cleanses the data, and publishes curated tables to Unity Catalog.
Ensure that the Apache Spark Structured Streaming pipelines reading from the event hubs write the data into a managed Delta table named telemetry.raw_events. The pipelines must support schema drift and resume processing after failures without reprocessing the data.
Contoso identifies the following data modeling and optimization requirements:
Build curated tables that standardize business logic.
Overwrite equipment metadata attributes, such as name, manufacturer, model, and commissioning date, when the attributes change. Historical values are NOT required.
Contoso identifies the following pipeline deployment and operation requirements:
Orchestrate multi-step ingestion and transformation workflows.
Define a clear execution order and dependencies.
Automatically retry failed steps and notify operators.
Schedule ingestion and transformation workloads consistently.

Governance Requirements -
Contoso identifies the following governance requirements:
Centralize the metadata catalog.
Provide isolated development areas that follow standard naming conventions.
Establish a consistent structure for organizing raw, cleansed, and curated data.
Provide a read-only mechanism to reference the ERP data through a foreign catalog.

Business Requirements -
Contoso identifies the following business requirements:
Improve ingestion reliability and reduce operational effort.
Standardize data definitions across development teams.
You need to configure compute for the ingestion of telemetry data. The solution must meet the data ingestion and processing requirements.
What should you do?

You have an Azure Databricks workspace that is attached to a Unity Catalog metastore named metastore1, metastore1 contains a catalog named catalog1.
You need to create a new schema named schema2 that meets the following requirements:

Is contained in catalog1 -
Uses abfss://[email protected]/data as the managed location
Which SQL statement should you execute?

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 effort.
What should you use?

DRAG DROP -
You have an Azure Databricks workspace that contains an all-purpose compute cluster named Cluster1. Cluser1 is used for interactive development.
You need to configure Cluster1 to meet the following requirements:
Automatically add and remove worker nodes based on workload demand.
Automatically shut down when the cluster has been idle for a specific period.
What should you configure for each requirement? To answer, drag the appropriate options to the correct requirements. Each option may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

You have an Azure Databricks workspace named Workspace1.
You create a compute cluster named Cluster1 that will be used to ingest data.
You need to install the required libraries on Cluster1. The solution must use Unity Catalog for access control.
What should you do?

You have an Azure Databricks workspace that contains an all-purpose cluster named Cluster1.
You need to configure Cluster1 to meet the following requirements:
Scale up automatically when workloads increase
Scale down automatically when workloads decrease

Minimize costs -
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

You have an Azure Databricks workspace that is enabled for Unity Catalog.
You have an Apache Spark Structured Streaming job that writes data to a Delta table.
After the cluster restarts, the streaming job reprocesses previously ingested data.
You need to prevent the streaming job from reprocessing the data after the cluster restarts.
What should you do?

DRAG DROP -
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a catalog named finance, finance contains two schemas named default and procurement.
You need to create a table named assets in the procurement schema, assets must contain the following columns: asset_id asset_type asset_name
How should you complete the SQL statement? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

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?

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?

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 environment
What should you include in the solution?

HOTSPOT -
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.

HOTSPOT -
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 type
The cluster runtime environment has the following configurations:
Uses Databricks Runtime for Machine Learning (Databricks Runtime ML)
Includes common machine learning libraries

Supports Python workloads -
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

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?

DRAG DROP -
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.

HOTSPOT -
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.

HOTSPOT -
You have an Azure Databricks workspace.
You have an Azure key vault named kv-secure that stores a secret named storageKey. The value of storageKey is managed and updated by the cloud security team at your company.
You need to enable a Databricks notebook named Notebook1 to retrieve the value of storageKey securely at runtime. The solution must follow the principle of least privilege and always retrieve the latest value.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

HOTSPOT -
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a catalog named Catalog1. Catalog1 contains a schema named Schema1 and a table named Table1.
You need to ensure that access to the data in Table1 is controlled by using attribute-based access control (ABAC).
What should you apply to Table1, and how should you control access for users? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Table1. Table1 stores customer data.
You need to implement a data retention solution that meets the following requirements:
Deleted data must be retained for 30 days to support audits.
Deleted data that is older than 30 days must be removed permanently.
The solution must minimize administrative effort
Which two properties should you configure? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

You have an Azure Databricks workspace that is enabled for Unity Catalog and contains two catalogs named Catalog1 and Catalog2.
An external application uses a service principal named SP1 to connect to a SQL warehouse.
You need to ensure that SP1 can query the data in Catalog1 and Catalog2. The solution must follow the principle of least privilege.
Which permissions should you grant to SP1 for the catalogs?

HOTSPOT -
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to implement a data lifecycle and expiration solution that meets the following requirements:
Transaction logs and deleted data files that are older than 90 days must be removed from Delta tables to reclaim storage.
All the tables must remain available for querying during the cleanup process.
Administrative effort must be minimized.
What should you do for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a catalog named Catalog1. Catalog1 contains a table named Transactions. Transactions contains the following columns: transaction_id customer_name email_address credit_card_number transaction_amount
You need to ensure that business analysts can query all the rows in the Transactions table. The solution must meet the following requirements:
Prevent the analysts from seeing the full values in the email_address and credit_card_number columns.
Ensure that the analysts can see only the values after the @ character in each email address.
Ensure that the analysts can see only the last four digits of each credit card number.
Enable the analysts to query the table without errors.
Follow the principle of least privilege.
What should you do?

Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
You have an Azure Databricks workspace named Workspace1 that contains a lakehouse and is enabled for Unity Catalog.
You have a connection to a Microsoft SQL Server database named DB1.
You need to expose the schemas and tables of DB1 to meet the following requirements:
The schemas and tables can be queried in Databricks.
The schemas and tables appear alongside other Unity Catalog objects.
The data is NOT copied into Databricks-managed storage.
Solution: You create a Lakeflow Connect pipeline and connect it to DB1.
Does this meet the goal?

HOTSPOT -
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a catalog named catalog1.
You have a group named group1.
You plan to create a schema named schema1 in catalog1.
You need to ensure that group1 meets the following requirements:

Can create tables in schema1 -

Can modify and query tables -
Cannot grant permissions for the schema and its objects
How should you complete the SQL statements? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

HOTSPOT -
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to create an external volume named Volume1 in an existing schema. Volume1 must expose files from an Azure Storage container. The solution must meet the following requirements:
Ensure that authentication does NOT require storing credentials in Databricks.
Ensure that users can access the files, but NOT modify the files.
Follow the principle of least privilege.
Which type of authentication should you configure, and which permission should you grant to the users? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.