DP-750試験の準備方法｜素敵なDP-750復習攻略問題試験｜検証するImplementing Data Engineering Solutions Using Azure Databricks認定デベロッパー

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Microsoft DP-750 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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. Azure Key Vault integration
          • 2. Service principals and managed identities
            • 3. Access control for compute resources
              Topic 2: Deploy and manage data pipelines and workloads30-35%- Pipeline design and orchestration
              • 1. Databricks Jobs and Workflows
                • 2. Notebook-based vs declarative pipelines
                  - Lakehouse architecture operations
                  • 1. Delta Live Tables pipelines
                    • 2. Delta Lake optimization and clustering strategies
                      - Operational reliability
                      • 1. Error handling and retries
                        • 2. Monitoring and logging (Azure Monitor integration)
                          Topic 3: Prepare and process data30-35%- Data transformation and modeling
                          • 1. Delta Lake table design and SCD patterns
                            • 2. Joins, aggregations, and normalization/denormalization
                              • 3. SQL and PySpark transformations
                                - Data ingestion
                                • 1. Streaming ingestion using Spark Structured Streaming
                                  • 2. Auto Loader and CDC ingestion patterns
                                    • 3. Batch ingestion using COPY INTO and CTAS
                                      - Data quality and validation
                                      • 1. Pipeline expectations and data quality constraints
                                        • 2. Handling nulls, duplicates, and missing data
                                          • 3. Schema enforcement and validation rules
                                            Topic 4: 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. Row-level and column-level security
                                                  • 2. Tags and policy enforcement
                                                    • 3. Attribute-based access control (ABAC)

                                                      >> DP-750復習攻略問題 <<

                                                      DP-750復習攻略問題 - Implementing Data Engineering Solutions Using Azure Databricksに合格する有効な資料を提供する

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                                                      Microsoft Implementing Data Engineering Solutions Using Azure Databricks 認定 DP-750 試験問題 (Q78-Q83):

                                                      質問 # 78
                                                      You have an Azure Databricks workspace named Workspace1. You create a compute cluster named Cluser1 that will be used to ingest data.
                                                      You need to install the required libraries on Cluster 1. The solution must use Unity Catalog for access control.
                                                      What should you do?

                                                      正解：D

                                                      解説：
                                                      The correct answer is B. The %pip install command (or pip3 in a terminal context) creates an isolated, per- session library environment in notebooks, which is the Unity Catalog-compatible approach. Unity Catalog workspaces require cluster access mode set to 'Shared' or 'Single User,' and %pip installs work seamlessly within those modes without requiring cluster restarts.
                                                      Option A (custom dependency script) introduces extra maintenance work for every environment change - exactly what the question says to avoid. Option C installs libraries at the cluster level and requires a manual restart, which disrupts other users sharing the cluster and bypasses the per-notebook isolation model that Unity Catalog recommends. Option D uploads libraries to the Workspace file system (legacy DBFS approach), which is being deprecated in favour of Unity Catalog Volumes for library storage.
                                                      Reference: https://learn.microsoft.com/en-us/azure/databricks/libraries/notebooks-python-libraries


                                                      質問 # 79
                                                      What ensures failure recovery in Databricks Structured Streaming?

                                                      正解：D

                                                      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 # 80
                                                      You have an Azure Databricks workspace named Workspace1.
                                                      You create a compute cluster named Cluster1 that will be used to ingest data.
                                                      You need to install the required libraries on Cluster1. The solution must use Unity Catalog for access control.
                                                      What should you do?

                                                      正解：D

                                                      解説：
                                                      The best action is uploading the libraries to the workspace and installing the libraries on the cluster (or ideally uploading them to Unity Catalog volumes).
                                                      Unity Catalog Compatibility: When using Unity Catalog for access control, compute clusters are typically configured with Standard (Shared) access mode. In this mode, traditional cluster init scripts [Not B.] face strict execution restrictions or are completely blocked to maintain secure user isolation.
                                                      Governance: Uploading your packages as Workspace Files or to Unity Catalog volumes allows administrators to manage access permissions directly and add them to an allowlist if needed.
                                                      Cluster-Wide Availability: Installing the libraries via the cluster's Libraries tab ensures that the required ingestion packages are automatically pre-installed and available across all nodes and notebooks running on that cluster.
                                                      Incorrect:
                                                      [Not A]
                                                      Running pip3 install manually on a cluster terminal or inside a notebook only applies to the specific notebook session (notebook-scoped). It does not natively persist across cluster restarts or handle cross-node execution effectively for data ingestion pipelines.
                                                      [Not B]
                                                      Running a custom script or a legacy init script to modify system-level paths introduces security risks and is generally incompatible with Unity Catalog's strict execution isolation policies for shared compute.
                                                      Reference:
                                                      https://docs.databricks.com/aws/en/libraries/


                                                      質問 # 81
                                                      You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a Delta table named Table1. Table! is used as a dimension table and contains the following columns:
                                                      * id
                                                      * name
                                                      * address
                                                      Vou need to apply a Type 1 slowly changing dimension (SCD) approach when updates are processed in the name and address columns.
                                                      What will occur when an update is processed?

                                                      正解：A

                                                      解説：
                                                      A Type 1 slowly changing dimension maintains only the current state of each dimension record. When the name or address associated with an existing id changes, the corresponding values in the existing row are overwritten with the new values. The previous name and address values are not retained, and no additional historical row or validity-period columns are created. Microsoft Learn The id column acts as the identifier used to locate the existing dimension record; it is not the attribute being changed in this scenario. Adding a new row for every change and preserving the earlier row with effective dates describes a Type 2 SCD implementation. Type 2 supports historical and point-in-time analysis, whereas Type 1 is appropriate when only the latest attribute values are required or when previous values do not need to be audited.
                                                      Therefore, when updates to name or address are processed using SCD Type 1, the existing values are replaced, and Table1 retains only the latest version of the record.


                                                      質問 # 82
                                                      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.

                                                      正解：

                                                      解説：


                                                      質問 # 83
                                                      ......

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