File Info
| Exam | Designing and Implementing an Azure AI Solution |
| Number | AI-100 |
| File Name | Microsoft.AI-100.Pass4Sure.2020-03-04.88q.tqb |
| Size | 602 KB |
| Posted | Mar 04, 2020 |
| Download | Microsoft.AI-100.Pass4Sure.2020-03-04.88q.tqb |
How to open VCEX & EXAM Files?
Files with VCEX & EXAM extensions can be opened by ProfExam Simulator.
Coupon: MASTEREXAM
With discount: 20%
Demo Questions
Question 1
You are designing an AI solution in Azure that will perform image classification.
You need to identify which processing platform will provide you with the ability to update the logic over time. The solution must have the lowest latency for inferencing without having to batch.
Which compute target should you identify?
- graphics processing units (GPUs)
- field-programmable gate arrays (FPGAs)
- central processing units (CPUs)
- application-specific integrated circuits (ASICs)
Correct answer: B
Explanation:
FPGAs, such as those available on Azure, provide performance close to ASICs. They are also flexible and reconfigurable over time, to implement new logic. Incorrect Answers:D: ASICs are custom circuits, such as Google's TensorFlow Processor Units (TPU), provide the highest efficiency. They can't be reconfigured as your needs change.References:https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-accelerate-with-fpgas FPGAs, such as those available on Azure, provide performance close to ASICs. They are also flexible and reconfigurable over time, to implement new logic.
Incorrect Answers:
D: ASICs are custom circuits, such as Google's TensorFlow Processor Units (TPU), provide the highest efficiency. They can't be reconfigured as your needs change.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-accelerate-with-fpgas
Question 2
You are designing an AI solution that will analyze millions of pictures by using Azure HDInsight Hadoop cluster.
You need to recommend a solution for storing the pictures. The solution must minimize costs.
Which storage solution should you recommend?
- an Azure Data Lake Storage Gen1
- Azure File Storage
- Azure Blob storage
- Azure Table storage
Correct answer: C
Explanation:
Data Lake will be a bit more expensive although they are in close range of each other. Blob storage has more options for pricing depending upon things like how frequently you need to access your data (cold vs hot storage). Reference:http://blog.pragmaticworks.com/azure-data-lake-vs-azure-blob-storage-in-data-warehousing Data Lake will be a bit more expensive although they are in close range of each other. Blob storage has more options for pricing depending upon things like how frequently you need to access your data (cold vs hot storage).
Reference:
http://blog.pragmaticworks.com/azure-data-lake-vs-azure-blob-storage-in-data-warehousing
Question 3
You are configuring data persistence for a Microsoft Bot Framework application. The application requires a structured NoSQL cloud data store.
You need to identify a storage solution for the application. The solution must minimize costs.
What should you identify?
- Azure Blob storage
- Azure Cosmos DB
- Azure HDInsight
- Azure Table storage
Correct answer: D
Explanation:
Table Storage is a NoSQL key-value store for rapid development using massive semi-structured datasets You can develop applications on Cosmos DB using popular NoSQL APIs. Both services have a different scenario and pricing model. While Azure Storage Tables is aimed at high capacity on a single region (optional secondary read only region but no failover), indexing by PK/RK and storage-optimized pricing; Azure Cosmos DB Tables aims for high throughput (single-digit millisecond latency), global distribution (multiple failover), SLA-backed predictive performance with automatic indexing of each attribute/property and a pricing model focused on throughput. References:https://db-engines.com/en/system/Microsoft+Azure+Cosmos+DB%3BMicrosoft+Azure+Table+Storage Table Storage is a NoSQL key-value store for rapid development using massive semi-structured datasets
You can develop applications on Cosmos DB using popular NoSQL APIs.
Both services have a different scenario and pricing model.
While Azure Storage Tables is aimed at high capacity on a single region (optional secondary read only region but no failover), indexing by PK/RK and storage-optimized pricing; Azure Cosmos DB Tables aims for high throughput (single-digit millisecond latency), global distribution (multiple failover), SLA-backed predictive performance with automatic indexing of each attribute/property and a pricing model focused on throughput.
References:
https://db-engines.com/en/system/Microsoft+Azure+Cosmos+DB%3BMicrosoft+Azure+Table+Storage
