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Microsoft DP-750 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Implementing Data Engineering Solutions Using Azure Databricks
Exam Number:DP-750
Passing Score:700
Available Languages:English
Exam Duration:100 minutes
Exam Format:Multiple choice, Case studies, Scenario-based questions, Interactive items
Related Certifications:Microsoft Certified: Azure Data Engineer Associate (DP-203)
Microsoft Certified: Fabric Data Engineer Associate (DP-700)
Recommended Training:DP-750 Training Course (DP-750T00)
Microsoft Learn DP-750 Study Guide
Exam Registration:Official Microsoft Certification Page
Pearson VUE Exam Scheduling
Sample Questions:Microsoft DP-750 Sample Questions
Exam Way:Online proctored exam via Pearson VUE
Pre Condition:Recommended experience with Azure Databricks, SQL, Python, and basic Azure services (Entra ID, Data Factory, Key Vault, Azure Storage).
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/implementing-data-engineering-solutions-using-azure-databricks/

Microsoft DP-750 Exam Syllabus Topics:

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

                                                      Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

                                                      1. You have an Azure Databricks workspace that is enabled for Unity Catalog.
                                                      You have 500 GB of sales data stored as multiple CSV files in cloud storage.
                                                      You plan to load the data into a Delta table.
                                                      You need to ingest the bulk data by using a solution that meets the following requirements:
                                                      * Minimize how long it takes to implement the solution.
                                                      * Minimize the amount of custom code required.
                                                      What should you use?

                                                      A) COPY INTO
                                                      B) Auto Loader
                                                      C) Apache Spark read APIs
                                                      D) file upload


                                                      2. You have an Azure Databricks workspace that contains a Git folder and uses Azure Repos as the Git provider.
                                                      From the main branch, you create a branch named Branch1. You commit changes to Branch1.
                                                      You need to incorporate the changes from Branch1 into main The solution must preserve the commit history in the repository. Which command should you run?

                                                      A) rebase
                                                      B) pull
                                                      C) Push
                                                      D) merge


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


                                                      4. You have an Azure Databricks workspace.
                                                      You need to ingest streaming data from Azure Event Hubs by using Apache Spark Structured Streaming The solution must authenticate to Event Hubs and read the event payload.
                                                      How should you complete the PySpark code segment? To answer, select the appropriate options in the answer area.
                                                      NOTE: Each correct selection is worth one point.


                                                      5. You have an Azure Databricks workspace named Workspace1 that contains a takehouse 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 new native catalog in Unity Catalog. Does this meet the goal?

                                                      A) Yes
                                                      B) No


                                                      Solutions:

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

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