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

                                                      Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

                                                      Question #1

                                                      You have an Azure Databricks workspace that contains a Delta table named Table 1. Table 1 has accumulated obsolete files.
                                                      You need to reduce storage costs. The solution must preserve 30 days of time travel history. Which two actions should you perform? Each correct answer presents part of the solution.
                                                      NOTE: Each correct selection is worth one point.

                                                      • A. Set the delta.deletedFileRetentionDuration table property to 10 days
                                                      • B. Set the delta. logRetentionDuration table property to 30 days.
                                                      • C. Run the vacuum command on Table1.
                                                      • D. Run the OPTIMIZE command on Table1
                                                      • E. Reduce the deleted file retention period to one day.
                                                      Reveal Solution  Discussion  0

                                                      Correct Answer: A,C  🗳️

                                                      Explanation: Only visible for TrainingQuiz members. You can sign-up / login (it's free).

                                                      Question #2

                                                      You have an Azure Databricks workspace that is enabled for Unity Catalog You have a complex job named Job1 that contains eight tasks. Job! takes multiple hours to complete During the last job run, the final task fails due to a transient issue.
                                                      You need to retry the last task without rerunning tasks that have already completed.
                                                      What should you do?

                                                      • A. Update the job parameters.
                                                      • B. Disable and reenable the job schedule.
                                                      • C. Restart Job!
                                                      • D. Repair the current job run.
                                                      Reveal Solution  Discussion  0

                                                      Correct Answer: D  🗳️

                                                      Explanation: Only visible for TrainingQuiz members. You can sign-up / login (it's free).

                                                      Question #3

                                                      You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Sales. Sales stores transaction data and contains the following columns:
                                                      * transactionjd (string)
                                                      * transaction date (date)
                                                      * amount (decimal)
                                                      You need to implement the following data quality requirements by using table-level data quality enforcement:
                                                      * amount must be greater than 0.
                                                      * transaction id must never be null.
                                                      * Invalid records must be rejected when data is written to the Sales table.
                                                      What should you do?

                                                      • A. Create a view that filters out rows where transactionjd is null or amount is less than or equal to 0.
                                                      • B. Configure row-level security (RLS) where transactionjd is null or amount is less than or equal to 0.
                                                      • C. Add a not null constraint to transactionjd and a check constraint to amount.
                                                      • D. Use a select statement with where conditions to validate the data before querying.
                                                      Reveal Solution  Discussion  0

                                                      Correct Answer: C  🗳️

                                                      Explanation: Only visible for TrainingQuiz members. You can sign-up / login (it's free).

                                                      Question #4

                                                      You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Table1.
                                                      Table1 stores customer profile data.
                                                      Business users must analyze how customer profile records change over time. They must also be able to query earlier versions of the table.
                                                      You need to implement a solution that:
                                                      * Maintains persistent historical versions of customer profile records for long-term analysis.
                                                      * Allows users to query earlier versions of the Delta table.
                                                      * Minimizes maintenance effort.
                                                      What should you do? To answer, select the appropriate options in the answer area.
                                                      NOTE: Each correct selection is worth one point.

                                                      Reveal Solution  Discussion  0

                                                      Correct Answer:


                                                      Explanation:
                                                      To record historical changes: Implement a Type 2 slowly changing dimension (SCD).
                                                      To support temporal analysis: Use Delta Lake time travel.
                                                      A Type 2 slowly changing dimension preserves customer-profile history by inserting a new record whenever a tracked attribute changes instead of overwriting the existing record. Effective dates, expiration dates, version values, or current-record indicators can identify which version applied during a particular period. This provides persistent business history for long-term analysis. Delta Lake time travel supports temporal analysis of the physical table by allowing users to query an earlier version with VERSION AS OF or TIMESTAMP AS OF. Time travel is useful for auditing and reproducing previous results, but its availability depends on retained Delta log entries and data files. Therefore, it should not replace a Type 2 SCD for permanent customer history. Together, the two features satisfy the historical-record and earlier-version requirements.

                                                      Question #5

                                                      You need to configure resiliency for a job in Lakeflow Jobs named Job1 to meet the pipeline deployment and operation requirements.
                                                      What should you do?

                                                      • A. Enable a restart upon a job failure and set the retry count to 0.
                                                      • B. Enable task-level retries on the ingestion task and leave downstream tasks unchanged.
                                                      • C. Disable retries and configure Job1 to run manually only.
                                                      • D. Configure Job1 to start always from the first task when any task fails.
                                                      Reveal Solution  Discussion  0

                                                      Correct Answer: B  🗳️

                                                      Explanation: Only visible for TrainingQuiz members. You can sign-up / login (it's free).

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