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Microsoft DP-750 Exam Syllabus Topics:

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

                                                      Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

                                                      1. You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.
                                                      Job1 runs every hour.
                                                      Occasionally, Job1 takes longer than one hour to complete.
                                                      You need to configure the job scheduling behavior to meet the following requirements:
                                                      * Overlapping runs must be prevented to avoid data corruption.
                                                      * Scheduled runs must not be discarded when another run is already active.
                                                      What should you configure? To answer, select the appropriate options in the answer area.
                                                      NOTE: Each correct selection is worth one point.


                                                      2. You have an Azure Databricks workspace that uses Unity Catalog.
                                                      You have a Lakeflow Spark Declarative Pipelines (SDP) pipeline that ingests data into a managed Delta table named Table1. Table1 is used for analytics.
                                                      New columns are added to the source data, causing pipeline failures during writes to Table1.
                                                      You need to prevent the pipeline failures. The solution must ensure that schema changes are detected and handled.
                                                      What should you do?

                                                      A) Disable schema enforcement for Table1.
                                                      B) Use row filters to exclude records that have new columns.
                                                      C) Create a separate table for each schema version.
                                                      D) Enable schema evolution.


                                                      3. You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a catalog named CatalogV Catalog1 contains a schema named Schema! 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.


                                                      4. You have an Azure Databricks workspace that contains multiple all-purpose clusters. You discover that some clusters remain idle for long periods after users finish their work. You need to reduce compute costs without affecting active workloads. What should you do?

                                                      A) Convert the clusters into job clusters
                                                      B) Configure automatic termination.
                                                      C) Enable autoscaling.
                                                      D) Use spot instances.


                                                      5. 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) Repair the current job run.
                                                      B) Disable and reenable the job schedule.
                                                      C) Update the job parameters.
                                                      D) Restart Job!


                                                      Solutions:

                                                      Question # 1
                                                      Answer: Only visible for members
                                                      Question # 2
                                                      Answer: D
                                                      Question # 3
                                                      Answer: Only visible for members
                                                      Question # 4
                                                      Answer: B
                                                      Question # 5
                                                      Answer: A

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