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Microsoft DP-750 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Secure and govern data using Unity Catalog15-20%- Data governance fundamentals
  • 1. Data lineage and auditing
    • 2. Catalog, schema, and table management
      - Access control and policies
      • 1. Row-level and column-level security
        • 2. Tags and policy enforcement
          • 3. Attribute-based access control (ABAC)
            Topic 2: Deploy and manage data pipelines and workloads30-35%- Pipeline design and orchestration
            • 1. Notebook-based vs declarative pipelines
              • 2. Databricks Jobs and Workflows
                - Operational reliability
                • 1. Error handling and retries
                  • 2. Monitoring and logging (Azure Monitor integration)
                    - Lakehouse architecture operations
                    • 1. Delta Lake optimization and clustering strategies
                      • 2. Delta Live Tables pipelines
                        Topic 3: Configure and manage Azure Databricks environments15-20%- Security and authentication setup
                        • 1. Azure Key Vault integration
                          • 2. Service principals and managed identities
                            • 3. Access control for compute resources
                              - Workspace and compute configuration
                              • 1. Autoscaling, termination, and performance tuning
                                • 2. Runtime, Spark, and Photon configuration
                                  • 3. Cluster types and configuration (job, all-purpose, serverless)
                                    Topic 4: Prepare and process data30-35%- Data ingestion
                                    • 1. Auto Loader and CDC ingestion patterns
                                      • 2. Batch ingestion using COPY INTO and CTAS
                                        • 3. Streaming ingestion using Spark Structured Streaming
                                          - Data transformation and modeling
                                          • 1. Delta Lake table design and SCD patterns
                                            • 2. SQL and PySpark transformations
                                              • 3. Joins, aggregations, and normalization/denormalization
                                                - Data quality and validation
                                                • 1. Schema enforcement and validation rules
                                                  • 2. Pipeline expectations and data quality constraints
                                                    • 3. Handling nulls, duplicates, and missing data

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                                                      Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions (Q96-Q101):

                                                      NEW QUESTION # 96
                                                      Drag and Drop Question
                                                      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.

                                                      Answer:

                                                      Explanation:


                                                      NEW QUESTION # 97
                                                      You have an Azure Databricks account that contains a single workspace named Workspace1. Workspace1 is enabled for Unity Catalog.
                                                      You discover that data access events for Unity Catalog tables fail to appear in the logs.
                                                      You need to ensure that all the data access events are captured centrally for auditing purposes. The log data must be available for analysis as quickly as possible.
                                                      What should you do? To answer, select the appropriate options in the answer area.
                                                      NOTE: Each correct selection is worth one point.

                                                      Answer:

                                                      Explanation:

                                                      Explanation:
                                                      Enable audit logging for: The Databricks account
                                                      Send audit logs to: Log Analytics
                                                      Enabling audit logging at the Databricks account level provides centralized coverage of account and workspace activities, including Unity Catalog data-access events. Enabling logging only for Workspace1 would provide a narrower configuration and would not offer the same centralized account-level auditing model. Individual Unity Catalog tables and the Azure subscription are not the appropriate Databricks audit- log scope selections. Log Analytics is the suitable destination because it supports rapid ingestion and immediate querying of log records through Azure Monitor Logs. Auditors can search, filter, correlate, and analyze the captured events without first developing a separate data-processing solution. An Azure Storage account is more suitable for inexpensive archival, while Event Hubs is primarily a streaming transport that normally requires an additional consumer before investigators can analyze the records.


                                                      NEW QUESTION # 98
                                                      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?

                                                      Answer: C

                                                      Explanation:
                                                      To protect sensitive customer information while allowing a specific group to run queries, use Unity Catalog's Dynamic Column Masking. This grants the group table access while applying User- Defined Functions (UDFs) to redact the email addresses and credit card numbers dynamically.
                                                      Here is the exact SQL implementation to apply:
                                                      1. Create the masking SQL UDFs
                                                      Define functions to apply the exact partial masking rules requested.
                                                      2. Apply the column masks to the tableAlter the transaction table to attach these UDFs to the respective columns.
                                                      3. Grant least privilege permissionsGrant the group the SELECT permission on the table, along with the foundational permissions to access the catalog and schema.
                                                      Reference:
                                                      https://docs.databricks.com/aws/en/data-governance/unity-catalog/filters-and-masks


                                                      NEW QUESTION # 99
                                                      You have an Azure Databricks workspace that is enabled for Unity Catalog and contains:
                                                      A catalog named Finance
                                                      A schema named Purchases in the Finance catalog
                                                      A table named Transactions in the Purchases schema
                                                      You need to ensure that a user named finance_user can query the Transactions table. The solution must follow the principle of least privilege.
                                                      Which permission should you grant to finance_user for each object? To answer, drag the appropriate permissions to the correct objects. Each permission 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.

                                                      Answer:

                                                      Explanation:

                                                      Explanation:
                                                      The Finance catalog: USE CATALOG
                                                      The Purchases schema: USE SCHEMA
                                                      The Transactions table: SELECT


                                                      NEW QUESTION # 100
                                                      You need to deploy Databricks Asset Bundles to a development environment. The solution must support automated and repeatable deployments across environments.
                                                      What should you use?

                                                      Answer: A

                                                      Explanation:
                                                      The correct answer is C - the Databricks CLI.
                                                      Databricks Asset Bundles are deployed using the Databricks CLI (v0.205+) with the commands databricks bundle validate, databricks bundle deploy, and databricks bundle run. The CLI reads the databricks.yml file, resolves the target environment settings, and creates or updates all declared resources (jobs, pipelines, apps) in the workspace. This is the documented, supported deployment mechanism for DABs and integrates naturally into CI/CD pipelines.
                                                      Option A (Azure Developer CLI / azd) is a general Azure IaC tool with no native understanding of Databricks bundles. Option B (Git folders) syncs notebook and file content from a Git repository into a workspace - useful for code, but it doesn't deploy Lakeflow Jobs, SDP pipelines, or bundle configurations. Option D (Azure CLI) manages Azure infrastructure (resource groups, storage accounts, etc.) and has no native DAB deployment support.
                                                      Reference: https://learn.microsoft.com/en-us/azure/databricks/dev-tools/bundles/deploy-bundle


                                                      NEW QUESTION # 101
                                                      ......

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