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Microsoft DP-750 Exam Syllabus Topics:

SectionWeightObjectives
Prepare and process data30-35%- 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. 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. Auto Loader and CDC ingestion patterns
                  • 3. Streaming ingestion using Spark Structured Streaming
                    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. Attribute-based access control (ABAC)
                          • 2. Row-level and column-level security
                            • 3. Tags and policy enforcement
                              Configure and manage Azure Databricks environments15-20%- Workspace and compute configuration
                              • 1. Runtime, Spark, and Photon configuration
                                • 2. Cluster types and configuration (job, all-purpose, serverless)
                                  • 3. Autoscaling, termination, and performance tuning
                                    - Security and authentication setup
                                    • 1. Service principals and managed identities
                                      • 2. Access control for compute resources
                                        • 3. Azure Key Vault integration
                                          Deploy and manage data pipelines and workloads30-35%- 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
                                                  - Operational reliability
                                                  • 1. Monitoring and logging (Azure Monitor integration)
                                                    • 2. Error handling and retries

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                                                      Seit Neuem aktualisierte DP-750 Examfragen für Microsoft DP-750 Prüfung

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                                                      Microsoft Implementing Data Engineering Solutions Using Azure Databricks DP-750 Prüfungsfragen mit Lösungen (Q36-Q41):

                                                      36. Frage
                                                      Hotspot Question
                                                      You have an Azure Databricks workspace that contains an all-purpose cluster named Cluster1.
                                                      You discover that out-of-memory (OOM) errors intermittently cause jobs running on Cluster1 to fail.
                                                      You need to identify the root cause of the failures by analyzing the runtime execution behavior.
                                                      What should you do? To answer, select the appropriate options in the answer area.
                                                      NOTE: Each correct selection is worth one point.

                                                      Antwort:

                                                      Begründung:


                                                      37. Frage
                                                      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?

                                                      Antwort: D

                                                      Begründung:
                                                      To reduce compute costs from idle clusters without impacting active workloads, you must configure Auto-Termination and use Cluster Policies.
                                                      Core Remedies
                                                      *-> Auto-Termination: Set a strict inactivity timeout (e.g., 20-30 minutes) on all-purpose clusters to automatically shut them down when idle.
                                                      Cluster Policies: Enforce maximum auto-termination limits across the workspace so users cannot disable or set excessively long idle timeouts.
                                                      Single User Access Mode: Use this mode where possible, as it tracks idleness more accurately than Shared mode by monitoring the specific user's activity.
                                                      Reference:
                                                      https://medium.com/@sujathamudadla1213/databricks-lakehouse-platform-describe-how- clusters-are-terminated-and-the-impact-of-terminating-a-b6236689fd2e


                                                      38. Frage
                                                      You have an Azure Databricks workspace named Workspace1 that contains a lakehouse 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 Databricks access connector.
                                                      Does this meet the goal?

                                                      Antwort: B

                                                      Begründung:
                                                      The correct answer is B - No.
                                                      A Databricks Access Connector is an Azure resource that provides a managed identity for Databricks to authenticate to Azure Storage services. It is an authentication bridge - nothing more. Creating one doesn't register any external database, doesn't create any Unity Catalog objects representing DB1, and doesn't enable querying of SQL Server data from Databricks.
                                                      An Access Connector is a prerequisite for setting up storage credentials and external locations in Unity Catalog (to access ADLS Gen2, for example), but it has no role in Lakehouse Federation or foreign catalog creation. It cannot expose an external database's schemas and tables in Unity Catalog alongside native objects.
                                                      The correct solution for this scenario is a foreign catalog (Q47), which requires a connection object pointing to DB1 - not an Access Connector.
                                                      Reference: https://learn.microsoft.com/en-us/azure/databricks/connect/unity-catalog/storage-credentials


                                                      39. Frage
                                                      Hotspot Question
                                                      You have an Azure Databricks workspace.
                                                      You have an Azure key vault named kv-secure that stores a secret named storageKey. The value of storageKey is managed and updated by the cloud security team at your company.
                                                      You need to enable a Databricks notebook named Notebook1 to retrieve the value of storageKey securely at runtime. The solution must follow the principle of least privilege and always retrieve the latest value.
                                                      What should you do? To answer, select the appropriate options in the answer area.
                                                      NOTE: Each correct selection is worth one point.

                                                      Antwort:

                                                      Begründung:


                                                      40. Frage
                                                      You have an Azure Databricks job named Job1 that contains an ingestion task named Task1 and transformation task named Task2. You need to ensure that if Task1 fails, the task retries automatically, and Task2 is prevented from running How should you configure Job1? To answer, select the appropriate options in the answer area.
                                                      NOTE: Each correct selection is worth one point.

                                                      Antwort:

                                                      Begründung:

                                                      Explanation:
                                                      Two task-level settings solve this:
                                                      Task1 retry policy: configure Task1 with a maximum number of retries and a retry interval. When Task1 fails, Lakeflow Jobs automatically re-runs it up to the retry limit without any manual intervention. This handles transient infrastructure failures transparently.
                                                      Task2 run condition set to ' All succeeded ' with Task1 as its dependency: this means Task2 only starts when Task1 has succeeded. If Task1 fails and exhausts all retries, Task2 remains blocked - it never runs on data from a failed upstream ingestion. The dependency is declared in Task2 ' s ' Depends on ' setting in the job configuration.
                                                      These two settings are independent and composable. Task1 ' s retry policy gives it multiple chances to recover. Task2 ' s dependency and run condition ensure the downstream transformation only runs on clean, successfully ingested data.
                                                      Reference: https://learn.microsoft.com/en-us/azure/databricks/jobs/configure-jobs#task-retries


                                                      41. Frage
                                                      ......

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