認定するMicrosoft DP-750関連資料 &合格スムーズDP-750復習問題集 |信頼できるDP-750トレーリングサンプル

当社のDP-750実践教材は一流の専門家によって編集され、DP-750スタディガイドは思いやりのあるサービスとアクセス可能なコンテンツのパッケージ全体を提供します。 さらに、DP-750 Actual Testは、さまざまな側面で効率を改善します。 専門的な知識を十分に身に付けることは、あなたの人生に大いに役立ちます。 知識の時代の到来により、私たちはすべて、DP-750などの専門的な証明書を必要としています。

Microsoft DP-750 Exam Syllabus Topics:

SectionWeightObjectives
Deploy and manage data pipelines and workloads30-35%- Pipeline design and orchestration
  • 1. Notebook-based vs declarative pipelines
    • 2. Databricks Jobs and Workflows
      - 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
              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. Access control for compute resources
                      • 2. Service principals and managed identities
                        • 3. Azure Key Vault integration
                          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
                                    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. SQL and PySpark transformations
                                            • 2. Joins, aggregations, and normalization/denormalization
                                              • 3. Delta Lake table design and SCD patterns
                                                - Data ingestion
                                                • 1. Streaming ingestion using Spark Structured Streaming
                                                  • 2. Auto Loader and CDC ingestion patterns
                                                    • 3. Batch ingestion using COPY INTO and CTAS

                                                      >> DP-750関連資料 <<

                                                      DP-750復習問題集、DP-750トレーリングサンプル

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                                                      Microsoft Implementing Data Engineering Solutions Using Azure Databricks 認定 DP-750 試験問題 (Q24-Q29):

                                                      質問 # 24
                                                      What ensures failure recovery in Databricks Structured Streaming?

                                                      正解：B

                                                      解説：
                                                      Checkpointing stores streaming state and progress, allowing recovery after failures without data loss or duplication. Auto scaling adjusts compute resources but does not ensure reliability.
                                                      Partition pruning improves query performance. Broadcast joins optimize joins but are unrelated to recovery.


                                                      質問 # 25
                                                      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.

                                                      正解：

                                                      解説：

                                                      Explanation:
                                                      Reading from Azure Event Hubs in Spark Structured Streaming requires three things:
                                                      An EventHubsConf object built with the Event Hubs connection string (eventhubs.connectionString). This object is then converted to a map with .toMap before being passed to Spark.
                                                      spark.readStream.format( ' eventhubs ' ).options(**ehConf).load() to create the streaming DataFrame. The ' eventhubs ' format is provided by the azure-eventhubs-spark connector library.
                                                      A cast( ' string ' ) on the body column to decode the binary payload. Event Hubs delivers messages with the raw event bytes in a column called body - without the cast, you get binary data rather than the readable JSON or text payload.
                                                      This is the standard, documented integration pattern for connecting Azure Databricks to Event Hubs with Structured Streaming, providing the checkpoint-based exactly-once semantics required by the Contoso telemetry pipeline.
                                                      Reference: https://learn.microsoft.com/en-us/azure/databricks/connect/storage/events/eventhubs


                                                      質問 # 26
                                                      Hotspot Question
                                                      You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.
                                                      Job1 contains three tasks named Task1, Task2, and Task3.
                                                      If Task1 fails, Task2 and Task3 must be prevented from running. Successfully completed tasks must NOT rerun during recovery.
                                                      You need to configure Job1 to support controlled failure handling and recovery.
                                                      What should you configure? To answer, select the appropriate options in the answer area.
                                                      NOTE: Each correct selection is worth one point.

                                                      正解：

                                                      解説：


                                                      質問 # 27
                                                      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?

                                                      正解：B

                                                      解説：
                                                      The correct answer is B - No.
                                                      A native catalog in Unity Catalog is a standard Databricks-managed catalog. Creating one provisions a metadata namespace inside Unity Catalog, but it has no connection whatsoever to DB1 or any external SQL Server database. There is no mechanism to point a native catalog at an external database - it simply doesn't serve that purpose.
                                                      The requirement is to expose an external SQL Server's schemas and tables inside Unity Catalog without copying the data. That requires a foreign catalog, which is created through Lakehouse Federation using a registered connection to the external database. A foreign catalog acts as a read-only, virtual mirror of the external database - queries run against the live external data in place.
                                                      Creating a native catalog and then trying to manually recreate DB1's structure inside it would involve copying all the data, violating the 'data is NOT copied' requirement.
                                                      Reference: https://learn.microsoft.com/en-us/azure/databricks/query-federation/lakehouse-federation


                                                      質問 # 28
                                                      Hotspot Question
                                                      You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a catalog named Catalog1. Catalog1 contains a schema named Schema1 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.

                                                      正解：

                                                      解説：


                                                      質問 # 29
                                                      ......

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