競争力が激しい社会において、IT仕事をする人は皆、我々MogiExamのDP-750を通して自らの幸せを筑く建筑士になれます。我が社のMicrosoftのDP-750習題を勉強して、最も良い結果を得ることができます。我々のDP-750習題さえ利用すれば試験の成功まで近くなると考えられます。
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prepare and process data | 30–35% | - Ingest and transform data
|
| Topic 2: Set up and configure an Azure Databricks environment | 15–20% | - Select and configure compute resources
|
| Topic 3: Secure and govern Unity Catalog objects | 15–20% | - Manage data sharing and permissions
|
| Topic 4: Deploy and maintain data pipelines and workloads | 30–35% | - Build and orchestrate pipelines
|
当社MogiExamのDP-750学習教材は世界中で高い評価を得ていることは知られています。私たちの教材は、数千人の候補者によって承認されています。弊社製品に疑問があるか、合格率が疑われる場合がありますが、それは完全に不要であることが明確に伝えられます。それでも当社を信用していない場合は、DP-750テストトレントのデモをダウンロードすることを選択できます。 DP-750試験ツールの詳細をご紹介します。DP-750試験の質問を気に入っていただけることを願っています。
質問 # 101
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?
正解：A
解説：
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
質問 # 102
You have a Declarative Automation Bundle.
You plan to use the Databricks CLI to deploy the bundle to an environment named dev.
How should you complete the CLI command? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
The databricks bundle command group is used to validate, deploy, run, and manage Declarative Automation Bundles. The deploy subcommand deploys the bundle's defined resources-such as Lakeflow jobs, pipelines, dashboards, files, and notebooks-to the target Azure Databricks workspace.
The --target dev option selects the target named dev from the targets mapping in the bundle's databricks.yml configuration. Therefore, the complete command is databricks bundle deploy --target dev. The shorter -t dev syntax is also valid, but the answer area provides the full --target form. Microsoft Learn databricks workspace manages workspace objects and is not the command group for deploying bundles. az databricks workspace manages Azure Databricks workspace resources through the Azure CLI. The run dev option would attempt to execute a deployed bundle resource, while sync --target dev synchronizes local files with the workspace but does not deploy the complete bundle configuration.
質問 # 103
You need to develop the task logic for a new job in Lakeflow Jobs that processes telemetry data.
Each task must contain only the appropriate logic for its step in the pipeline. The solution must support the planned changes and meet the data ingestion and processing requirements.
What should you do?
正解：D
解説：
The correct answer is D. Breaking the pipeline into separate tasks for ingestion, cleansing, and curation is the foundation of well-designed Lakeflow Jobs pipelines. Each task should own one responsibility - when a task does too much, debugging a failure becomes a hunt through unrelated code, and retry logic becomes expensive because you re-execute work that already succeeded.
Contoso's planned changes explicitly call for 'a clear execution order and dependencies' and 'orchestrate multi- step ingestion and transformation workflows.' Separate tasks map directly to those goals: Lakeflow Jobs tracks each task's status independently, so if cleansing fails, ingestion doesn't re-run.
Option A bundles everything into one notebook, which means a curation bug forces a full re-ingestion. Option B copies logic three times - any future change must be applied in triplicate, which is a maintenance hazard.
Option C forces everything through SQL MERGE, which is the wrong tool for raw-event ingestion and doesn't address cleansing or schema drift.
Reference: https://learn.microsoft.com/en-us/azure/databricks/jobs/
Topic 1, Contoso Case Study
Overview
Contoso has a single Azure Databricks workspace named Workspace1 in the West US Azure region.
Workspace1 is enabled for Unity Catalog.
Workspace1 contains all-purpose clusters for both development and production workloads.
The company's Azure environment contains:
* In the West US, Central US, and East US Azure regions, Azure event hubs that stream telemetry data and an Azure Data Lake Storage Gen2 account in each region for each hub
* A single Azure SQL database in the West US region that hosts enterprise resource planning (ERP) data
* An Azure Database for PostgreSQL server in the West US region that stores operational maintenance data Company information Contoso, Inc. is a renewable energy provider that operates solar and wind farms across North America.
Data Environment
Contoso ingests the following operational and business data:
* Telemetry data: More than 40,000 loT sensors across 28 sites emit JSON telemetry events every few seconds. Each site sends the events to the nearest event hub, which writes the data into the corresponding Data Lake Storage Gen2 account. These files frequently experience schema drift.
* Maintenance logs: Maintenance systems generate historical repair logs, daily incremental updates, technician notes, and unstructured attachments that are stored in the Data Lake Storage Gen2 accounts.
* Operational maintenance data: Structured operational maintenance data is stored on the Azure Database for PostgreSQL server.
* External weather data: Hourly weather forecasts are retrieved from a REST API and written to the Data Lake Storage Gen2 accounts.
* ERP data: Daily CSV extracts of 50 to 100 GB contain equipment metadata, work orders, and purchase order information.
Problem Statements
The company's existing analytics environment has several issues:
Ingestion
* Telemetry pipelines fall behind during peak loads.
* Telemetry ingestion fails when schema drift occurs.
* Streaming pipelines reprocess events after a pipeline restarts.
Compute
* Production and development workloads run on the same all-purpose clusters.
* Production and development workloads do NOT support autoscaling or workload isolation.
Governance
* The ERP data is duplicated across systems and development teams.
* Naming conventions are inconsistent across development teams, regions, and products.
* Ownership of the loT sensors changes over time, and analysts must track the full history of the ownership.
* Occasionally, equipment manufacturers must correct data-entry mistakes in equipment names. Historical values are NOT required.
Pipeline operations
* Pipelines lack resiliency, alerting, and centralized scheduling.
Planned Changes
Contoso plans to implement the following changes:
* Implement scalable data pipeline orchestration.
* Create a managed analytics catalog in Unity Catalog.
* Implement a consistent approach to creating curated datasets.
* Establish a centralized governance model across ingestion, cleansed, and curated layers.
* Grant data engineers access to the ERP tables by using minimal development effort.
* Adopt a compute strategy that isolates production workloads and supports autoscaling.
* Adopt a slowly changing dimension (SCD) approach to address current data modeling issues.
Technical Requirements
Contoso identifies the following environment and compute requirements:
* Ensure that production ingestion workloads run on compute clusters that can scale automatically during telemetry spikes.
* Provide fast and consistent performance for business intelligence (Bl) workloads.
* Prevent development activity from affecting production pipelines.
* Production ingestion workloads must run as scheduled, non-interactive pipelines rather than on shared interactive development clusters.
Contoso identifies the following data ingestion and processing requirements:
* Auto-scale ingestion pipelines to handle bursty workloads.
* Handle schema drift for the maintenance and telemetry data.
* Ingest file-based telemetry data by using minimal operational effort.
* Store all the ingested data in a format that supports incremental processing.
* Support the continuous ingestion of telemetry data from the event hubs by using exactly-once semantics.
* Support the ingestion of the structured maintenance data from the Azure Database for PostgreSQL server.
* Build a new telemetry pipeline that ingests raw events from the event hubs, cleanses the data, and publishes curated tables to Unity Catalog.
* Ensure that the Apache Spark Structured Streaming pipelines reading from the event hubs write the data into a managed Delta table named telemetry.raw_events. The pipelines must support schema drift and resume processing after failures without reprocessing the data.
Contoso identifies the following data modeling and optimization requirements:
* Build curated tables that standardize business logic.
* Overwrite equipment metadata attributes, such as name, manufacturer, model, and commissioning date, when the attributes change. Historical values are NOT required.
Contoso identifies the following pipeline deployment and operation requirements: |