Microsoft Implementing Data Engineering Solutions Using Azure Databricks - DP-750무료 덤프문제 풀어보기
Hotspot Question
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to create an external volume named Volume1 in an existing schema. Volume1 must expose files from an Azure Storage container. The solution must meet the following requirements:
- Ensure that authentication does NOT require storing credentials in
Databricks.
- Ensure that users can access the files, but NOT modify the files.
- Follow the principle of least privilege.
Which type of authentication should you configure, and which permission should you grant to the users? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to create an external volume named Volume1 in an existing schema. Volume1 must expose files from an Azure Storage container. The solution must meet the following requirements:
- Ensure that authentication does NOT require storing credentials in
Databricks.
- Ensure that users can access the files, but NOT modify the files.
- Follow the principle of least privilege.
Which type of authentication should you configure, and which permission should you grant to the users? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
정답:
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You have a Lakeflow Spark Declarative Pipelines (SDP) pipeline that writes numerical data to a table named Table1 by using a data quality validation rule named rule1.
You need to modify rule1 to meet the following requirements:
- Ensure that amount is always greater than 0.
- Prevent an update to Table1 from being committed when data that
violates rule1 is detected.
Which statement should you execute?
You have a Lakeflow Spark Declarative Pipelines (SDP) pipeline that writes numerical data to a table named Table1 by using a data quality validation rule named rule1.
You need to modify rule1 to meet the following requirements:
- Ensure that amount is always greater than 0.
- Prevent an update to Table1 from being committed when data that
violates rule1 is detected.
Which statement should you execute?
정답: C
설명: (Fast2test 회원만 볼 수 있음)
Case Study 1 - Contoso, Inc.
Overview
Company Information
Contoso, Inc. is a renewable energy provider that operates solar and wind farms across North America.
Existing Environment
Azure Environment
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 Data Environment Contoso ingests the following operational and business data:
- Telemetry data: More than 40,000 IoT 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 IoT 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.
Requirements
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 (BI) 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:
- Orchestrate multi-step ingestion and transformation workflows.
- Define a clear execution order and dependencies.
- Automatically retry failed steps and notify operators.
- Schedule ingestion and transformation workloads consistently.
Governance Requirements
Contoso identifies the following governance requirements:
- Centralize the metadata catalog.
- Provide isolated development areas that follow standard naming conventions.
- Establish a consistent structure for organizing raw, cleansed, and curated data.
- Provide a read-only mechanism to reference the ERP data through a foreign catalog.
Business Requirements
Contoso identifies the following business requirements:
- Improve ingestion reliability and reduce operational effort.
- Standardize data definitions across development teams.
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?
Overview
Company Information
Contoso, Inc. is a renewable energy provider that operates solar and wind farms across North America.
Existing Environment
Azure Environment
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 Data Environment Contoso ingests the following operational and business data:
- Telemetry data: More than 40,000 IoT 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 IoT 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.
Requirements
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 (BI) 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:
- Orchestrate multi-step ingestion and transformation workflows.
- Define a clear execution order and dependencies.
- Automatically retry failed steps and notify operators.
- Schedule ingestion and transformation workloads consistently.
Governance Requirements
Contoso identifies the following governance requirements:
- Centralize the metadata catalog.
- Provide isolated development areas that follow standard naming conventions.
- Establish a consistent structure for organizing raw, cleansed, and curated data.
- Provide a read-only mechanism to reference the ERP data through a foreign catalog.
Business Requirements
Contoso identifies the following business requirements:
- Improve ingestion reliability and reduce operational effort.
- Standardize data definitions across development teams.
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?
정답: C
설명: (Fast2test 회원만 볼 수 있음)
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?
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?
정답: D
설명: (Fast2test 회원만 볼 수 있음)
What is the best way to reduce Databricks compute cost for intermittent workloads?
정답: A
설명: (Fast2test 회원만 볼 수 있음)
Drag and Drop Question
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a catalog named finance, finance contains two schemas named default and procurement.
You need to create a table named assets in the procurement schema, assets must contain the following columns:
- asset_id
- asset_type
- asset_name
How should you complete the SQL statement? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a catalog named finance, finance contains two schemas named default and procurement.
You need to create a table named assets in the procurement schema, assets must contain the following columns:
- asset_id
- asset_type
- asset_name
How should you complete the SQL statement? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
정답:
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?
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?
정답: B
설명: (Fast2test 회원만 볼 수 있음)
What does the VACUUM command do in Delta Lake?
정답: C
설명: (Fast2test 회원만 볼 수 있음)