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Microsoft DP-750 Exam Syllabus Topics:

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

                                                      Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

                                                      You need to curate Unity Catalog objects that reference the ERP data. The solution must meet the governance requirements.
                                                      What should you do?

                                                      • A. Run the ALTER TABLE command on the foreign catalog tables to add new columns that are required for analytics.
                                                      • B. Create Delta tables directly inside the foreign catalog by running the CREATE TABLE AS SELECT (CTAS) command.
                                                      • C. Create a volume in the foreign catalog and store curated Delta tables in the volume.
                                                      • D. In the managed analytics catalog, create views that reference foreign catalog tables that use three-level naming.
                                                      Reveal Solution  Discussion  0

                                                      Correct Answer: D  🗳️

                                                      Explanation: Only visible for Fast2test members. You can sign-up / login (it's free).

                                                      You have an Azure Databricks workspace that is enabled for Unity Catalog.
                                                      You need to profile a table to meet the following requirements:
                                                      * The count of null values per column must be evaluated repeatedly as new records are added to the table.
                                                      * Changes in the count of null values must be observable over the progression of the dataset.
                                                      Which type of profile should you create?

                                                      • A. snapshot
                                                      • B. time series
                                                      • C. inference
                                                      Reveal Solution  Discussion  0

                                                      Correct Answer: B  🗳️

                                                      Explanation: Only visible for Fast2test members. You can sign-up / login (it's free).

                                                      You have an Azure Databricks workspace that is enabled for Unity Catalog.
                                                      You have a Lakeflow Spark Declarative Pipelines (SDP) pipeline that writes numerical data to a table named Table1 by using a data quality validation rule named rule1.
                                                      You need to modify rule1 to meet the following requirements:
                                                      Ensure that amount is always greater than 0.
                                                      Prevent an update to Table1 from being committed when data that violates rule1 is detected.
                                                      Which statement should you execute?

                                                      • A. @dlt.expect_or_fail( " rule1 " , " amount > 0 " )
                                                      • B. @dlt.expect_all_or_drop({ " rule1 " : " amount > 0 " })
                                                      • C. @dlt.expect_or_drop( " rule1 " , " amount > 0 " )
                                                      • D. @dlt.expect( " rule1 " , " amount > 0 " )
                                                      Reveal Solution  Discussion  0

                                                      Correct Answer: A  🗳️

                                                      Explanation: Only visible for Fast2test members. You can sign-up / login (it's free).

                                                      You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Tabid.
                                                      Table! is written by batch jobs every hour and is queried frequently by filtering two columns named Customerld 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.

                                                      Reveal Solution  Discussion  0

                                                      Correct Answer:


                                                      Explanation:
                                                      Two features work together to keep performance consistent and update costs low:
                                                      OPTIMIZE with ZORDER BY (CustomerId, EventDate). Z-Ordering co-locates rows with the same CustomerId and EventDate values in the same Parquet files. When a query filters on those columns, the Delta engine uses file statistics to skip files that can ' t possibly contain matching rows (data skipping). As the table grows, skipping scales proportionally - query time stays consistent.
                                                      Deletion Vectors (delta.enableDeletionVectors = true). When a row is updated or deleted, instead of rewriting the entire Parquet file, Delta marks the affected row in a small companion deletion vector file. This dramatically reduces write amplification for the frequent compliance-driven updates and deletions the question describes. Actual file rewrites are deferred to the next OPTIMIZE run.
                                                      Reference: https://learn.microsoft.com/en-us/azure/databricks/delta/data-skipping

                                                      You have an Azure Databricks workspace that is enabled for Unity Catalog.
                                                      You need to recommend a pipeline that ingests files from cloud storage, performs cleansing and enrichment transformations, and writes created Delta tables for analytics. The solution must minimize development effort and provide built-in monitoring and automatic retries.
                                                      What should you include in the recommendation?

                                                      • A. an Azure Data Factory pipeline that uses data flows
                                                      • B. a Lakeflow Spark Declarative Pipelines (SDPJ pipeline
                                                      • C. an Apache Spark Structured Streaming job
                                                      • D. a Databricks notebook triggered by a scheduled job
                                                      Reveal Solution  Discussion  0

                                                      Correct Answer: B  🗳️

                                                      Explanation: Only visible for Fast2test members. You can sign-up / login (it's free).

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