Exam Name: Design and Implement Big Data Analytics Solutions (070-475日本語版)
Certification Provider: Microsoft
Corresponding Certification: Microsoft Azure
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Microsoft 070-475日本語 Exam Overview:
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Design and Implement Big Data Analytics Solutions |
| Exam Number: | 70-475 |
| Exam Duration: | 120 minutes |
| Available Languages: | English |
| Certificate Validity Period: | Retired |
| Exam Format: | Multiple choice, Case study, Scenario-based questions |
| Related Certifications: | MCSA: Data Engineering with Azure Microsoft Azure certifications (legacy MCSA track) |
| Recommended Training: | Azure Data Engineering learning paths Microsoft Learn Azure Big Data Path |
| Exam Registration: | Microsoft Certifications Portal Pearson VUE Microsoft Exams |
| Sample Questions: | DOWNLOAD DEMO |
| Exam Way: | Delivered via Pearson VUE (online proctored or test center) |
| Pre Condition: | No formal prerequisite required, but experience with Azure data services, distributed systems, and big data processing is recommended. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/ |
Microsoft 070-475日本語 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Design big data analytics solution | - Select appropriate big data architecture (batch vs streaming vs hybrid) - Design data storage strategy using Azure data services
|
| Secure and monitor big data solutions | - Data security and access control - Monitoring and troubleshooting data pipelines |
| Develop and optimize data pipelines | - Performance tuning and scaling considerations - Data ingestion and transformation workflows |
| Implement data processing solutions | - Batch processing with distributed computing
|
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This package includes 122 practice questions for the Microsoft Design and Implement Big Data Analytics Solutions (070-475日本語版) exam. Every answer is verified by an experienced team of experts who review the question bank regularly, so you can rely on what you study.
No formal prerequisite required, but experience with Azure data services, distributed systems, and big data processing is recommended.
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The Microsoft Design and Implement Big Data Analytics Solutions (070-475日本語版) practice questions are organized around the official exam objectives. Key topics include:
- Develop and optimize data pipelines ()
- Implement data processing solutions ()
- Secure and monitor big data solutions ()
Focusing on the core topics first keeps your preparation efficient instead of scattered.
Many candidates combine practice questions with official training. Recommended options include:
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Microsoft Design and Implement Big Data Analytics Solutions (070-475日本語版) Sample Questions:
Correct Answer:
Explanation
References: https://docs.microsoft.com/en-us/azure/iot-hub/iot-hub-live-data-visualization-in-power-bi
Correct Answer:
Explanation
From Scenario: Relecloud plans to implement a data warehouse named DB2.
Box 1: Temporal table
From Scenario:
Relecloud identifies the following requirements for DB2:
Users must be able to view previous versions of the data in DB2 by using aggregates.
DB2 must be able to store more than 40 TB of data.
A system-versioned temporal table is a new type of user table in SQL Server 2017, designed to keep a full history of data changes and allow easy point in time analysis. A temporal table also contains a reference to another table with a mirrored schema. The system uses this table to automatically store the previous version of the row each time a row in the temporal table gets updated or deleted. This additional table is referred to as the history table, while the main table that stores current (actual) row versions is referred to as the current table or simply as the temporal table.
Correct Answer:
Explanation
Step 1: Configure a Microsoft Data Management Gateway
Install and configure Azure Data Factory Integration Runtime.
The Integration Runtime is a customer managed data integration infrastructure used by Azure Data Factory to provide data integration capabilities across different network environments. This runtime was formerly called
"Data Management Gateway".
Step 2: Create a linked service for Azure Blob storage
Create an Azure Storage linked service (destination/sink). You link your Azure storage account to the data factory.
Step 3: Create a linked service for SQL Server
Create and encrypt a SQL Server linked service (source)
In this step, you link your on-premises SQL Server instance to the data factory.
Step 4: Create an input dataset and an output dataset.
Create a dataset for the source SQL Server database. In this step, you create input and output datasets. They represent input and output data for the copy operation, which copies data from the on-premises SQL Server database to Azure Blob storage.
Step 5: Create a pipeline..
You create a pipeline with a copy activity. The copy activity uses SqlServerDataset as the input dataset and AzureBlobDataset as the output dataset. The source type is set to SqlSource and the sink type is set to BlobSink.
References: https://docs.microsoft.com/en-us/azure/data-factory/tutorial-hybrid-copy-powershell
* Batch
* Speed
* Serving
Correct Answer:
Explanation
Box 1: No
Box 2: No
Output from the batch and speed layers are stored in the serving layer, which responds to ad-hoc queries by returning precomputed views or building views from the processed data.
Box 3: Yes.
We are excited to announce Interactive Queries, a new feature for stream processing with Apache Kafka.
Interactive Queries allows you to get more than just processing from streaming.
Note: Lambda architecture is a popular choice where you see stream data pipelines applied (speed layer).
Architects can combine Apache Kafka or Azure Event Hubs (ingest) with Apache Storm (event processing), Apache HBase (speed layer), Hadoop for storing the master dataset (batch layer), and, finally, Microsoft Power BI for reporting and visualization (serving layer).
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