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Microsoft 070-475 Exam Overview:
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Design and Implement Big Data Analytics Solutions |
| Exam Number: | 70-475 |
| Exam Format: | Case study, Multiple choice, Scenario-based questions |
| Available Languages: | English |
| Related Certifications: | MCSA: Data Engineering with Azure Microsoft Azure certifications (legacy MCSA track) |
| Exam Duration: | 120 minutes |
| Certificate Validity Period: | Retired |
| 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 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Design big data analytics solution | - Select appropriate big data architecture (batch vs streaming vs hybrid) - Design data storage strategy using Azure data services
|
| Topic 2: Implement data processing solutions | - Batch processing with distributed computing
|
| Topic 3: Develop and optimize data pipelines | - Performance tuning and scaling considerations - Data ingestion and transformation workflows |
| Topic 4: Secure and monitor big data solutions | - Monitoring and troubleshooting data pipelines - Data security and access control |
Thinking About the 070-475 Exam? Read This First
The official Microsoft Design and Implement Big Data Analytics Solutions blueprint is split into 4 domains, starting with Develop and optimize data pipelines, Design big data analytics solution, Implement data processing solutions. The percentages tell you where the exam concentrates its questions, so budget your study time accordingly — the complete outline above lists every domain and subtopic.
The Microsoft Design and Implement Big Data Analytics Solutions is Microsoft's official exam for the Microsoft Certified Solutions Associate (MCSA): Data Engineering with Azure (Retired) certification, positioned at the Professional level. It is the credential hiring managers scan for, and passing it proves your skills meet the vendor's own standard. It also links to related certifications like MCSA: Data Engineering with Azure, Microsoft Azure certifications (legacy MCSA track), so this exam can serve as a building block in a longer certification plan.
Registration goes through the vendor's official channels:
Worth knowing before you book: the 070-475 exam is delivered Delivered via Pearson VUE (online proctored or test center), so pick the delivery option that works best for you.
Yes — a free PDF demo lets you inspect the question quality and format before you pay a cent. Once you purchase, your Microsoft Design and Implement Big Data Analytics Solutions material comes with 365 days of free updates, and if that update period ever lapses, extending it costs just 50% of the price in your member zone.
No formal prerequisite required, but experience with Azure data services, distributed systems, and big data processing is recommended. Vendor requirements do change from time to time, so confirm the current criteria before scheduling — the official 070-475 exam page has the final word.
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Yes — the vendor's recommended courses are listed here:
Courses teach the theory, but they cannot tell you whether you are ready. After finishing any of them, benchmark yourself with the 070-475 practice questions from ActualTestsQuiz and find the gaps before the exam does.
Microsoft Design and Implement Big Data Analytics Solutions Sample Questions:
You have a pipeline that contains an input dataset in Microsoft Azure Table Storage and an output dataset in Azure Blob storage. You have the following JSON data.
Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the JSON data.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation
Box 1: Every three days at 10.00
anchorDateTime defines the absolute position in time used by the scheduler to compute dataset slice boundaries.
"frequency": "<Specifies the time unit for data slice production. Supported frequency: Minute, Hour, Day, Week, Month>",
"interval": "<Specifies the interval within the defined frequency. For example, frequency set to 'Hour' and interval set to 1 indicates that new data slices should be produced hourly> Box 2: Every minute up to three times.
retryInterval is the wait time between a failure and the next attempt. This setting applies to present time. If the previous try failed, the next try is after the retryInterval period.
Example: 00:01:00 (1 minute)
Example: If it is 1:00 PM right now, we begin the first try. If the duration to complete the first validation check is 1 minute and the operation failed, the next retry is at 1:00 + 1min (duration) + 1min (retry interval) =
1:02 PM.
For slices in the past, there is no delay. The retry happens immediately.
retryTimeout is the timeout for each retry attempt.
maximumRetry is the number of times to check for the availability of the external data.
You have a web application that generates several terabytes (TB) of financial documents each day. The application processes the documents in batches.
You need to store the documents in Microsoft Azure. The solution must ensure that a user can restore the previous version of a document.
Which type of storage should you use for the documents?
- A. Azure Cosmos DB
- B. Azure File Storage
- C. Azure Data Lake
- D. Azure Blob storage
Correct Answer: A 🗳️
You have an application that displays data from a Microsoft Azure SQL database. The database contains credit card numbers.
You need to ensure that the application only displays the last four digits of each credit card number when a credit card number is returned from a query. The solution must NOT require any changes to the data in the database.
What should you use?
- A. cell-level security
- B. Dynamic Data Masking
- C. row-level security
- D. Transparent Data Encryption (TDE)
Correct Answer: B 🗳️
You have an Apache Storm cluster.
The cluster will ingest data from a Microsoft Azure event hub.
The event hub has the characteristics described in the following table.
You are designing the Storm application topology.
You need to ingest data from all of the partitions. The solution must maximize the throughput of the data ingestion.
Which setting should you use?
- A. Message Retention
- B. Partition Key
- C. Shared access policies
- D. Partition Count
Correct Answer: D 🗳️
You need to create a query that identifies the trending topics.
How should you complete the query? 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.
Correct Answer:
Explanation
From scenario: Topics are considered to be trending if they generate many mentions in a specific country during a 15-minute time frame.
Box 1: TimeStamp
Azure Stream Analytics (ASA) is a cloud service that enables real-time processing over streams of data flowing in from devices, sensors, websites and other live systems. The stream-processing logic in ASA is expressed in a SQL-like query language with some added extensions such as windowing for performing temporal calculations.
ASA is a temporal system, so every event that flows through it has a timestamp. A timestamp is assigned automatically based on the event's arrival time to the input source but you can also access a timestamp in your event payload explicitly using TIMESTAMP BY:
SELECT * FROM SensorReadings TIMESTAMP BY time
Box 2: GROUP BY
Example: Generate an output event if the temperature is above 75 for a total of 5 seconds SELECT sensorId, MIN(temp) as temp FROM SensorReadings TIMESTAMP BY time GROUP BY sensorId, SlidingWindow(second, 5) HAVING MIN(temp) > 75 Box 3: SlidingWindow Windowing is a core requirement for stream processing applications to perform set-based operations like counts or aggregations over events that arrive within a specified period of time. ASA supports three types of windows: Tumbling, Hopping, and Sliding.
With a Sliding Window, the system is asked to logically consider all possible windows of a given length and output events for cases when the content of the window actually changes - that is, when an event entered or existed the window.
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