Download Microsoft.AI-100.PassGuide.2019-06-18.50q.vcex

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Exam Designing and Implementing an Azure AI Solution
Number AI-100
File Name Microsoft.AI-100.PassGuide.2019-06-18.50q.vcex
Size 127 KB
Posted Jun 18, 2019
Download Microsoft.AI-100.PassGuide.2019-06-18.50q.vcex

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Demo Questions

Question 1

Your company recently deployed several hardware devices that contain sensors. 
The sensors generate new data on an hourly basis. The data generated is stored on-premises and retained for several years. 
During the past two months, the sensors generated 300 GB of data. 
You plan to move the data to Azure and then perform advanced analytics on the data. 
You need to recommend an Azure storage solution for the data. 
Which storage solution should you recommend?


  1. Azure Queue storage
  2. Azure Cosmos DB
  3. Azure Blob storage
  4. Azure SQL Database
Correct answer: C
Explanation:
References:https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/data-storage
References:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/data-storage



Question 2

You are designing an AI solution that will analyze millions of pictures. 
You need to recommend a solution for storing the pictures. The solution must minimize costs. 
Which storage solution should you recommend?


  1. an Azure Data Lake store
  2. Azure File Storage
  3. Azure Blob storage
  4. 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). References: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). 
References:
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?


  1. Azure Blob storage
  2. Azure Cosmos DB
  3. Azure HDInsight
  4. 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



Question 4

You have an Azure Machine Learning model that is deployed to a web service. 
You plan to publish the web service by using the name ml.contoso.com. 
You need to recommend a solution to ensure that access to the web service is encrypted. 
Which three actions should you recommend? Each correct answer presents part of the solution. 
NOTE: Each correct selection is worth one point.


  1. Generate a shared access signature (SAS)
  2. Obtain an SSL certificate
  3. Add a deployment slot
  4. Update the web service
  5. Update DNS
  6. Create an Azure Key Vault
Correct answer: BDE
Explanation:
The process of securing a new web service or an existing one is as follows:1. Get a domain name. 2. Get a digital certificate. 3. Deploy or update the web service with the SSL setting enabled. 4. Update your DNS to point to the web service. Note: To deploy (or re-deploy) the service with SSL enabled, set the ssl_enabled parameter to True, wherever applicable. Set the ssl_certificate parameter to the value of the certificate file and the ssl_key to the value of the key file.References:https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-secure-web-service
The process of securing a new web service or an existing one is as follows:
1. Get a domain name. 
2. Get a digital certificate. 
3. Deploy or update the web service with the SSL setting enabled. 
4. Update your DNS to point to the web service. 
Note: To deploy (or re-deploy) the service with SSL enabled, set the ssl_enabled parameter to True, wherever applicable. Set the ssl_certificate parameter to the value of the certificate file and the ssl_key to the value of the key file.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-secure-web-service



Question 5

You plan to design an application that will use data from Azure Data Lake and perform sentiment analysis by using Azure Machine Learning algorithms. 
The developers of the application use a mix of Windows- and Linux-based environments. The developers contribute to shared GitHub repositories. 
You need all the developers to use the same tool to develop the application. 
What is the best tool to use? More than one answer choice may achieve the goal.


  1. Microsoft Visual Studio Code
  2. Azure Notebooks
  3. Azure Machine Learning Studio
  4. Microsoft Visual Studio
Correct answer: C
Explanation:
References:https://github.com/MicrosoftDocs/azure-docs/blob/master/articles/machine-learning/studio/algorithm-choice.md
References:
https://github.com/MicrosoftDocs/azure-docs/blob/master/articles/machine-learning/studio/algorithm-choice.md



Question 6

You have several AI applications that use an Azure Kubernetes Service (AKS) cluster. The cluster supports a maximum of 32 nodes. 
You discover that occasionally and unpredictably, the application requires more than 32 nodes. 
You need to recommend a solution to handle the unpredictable application load. 
Which scaling method should you recommend?


  1. horizontal pod autoscaler
  2. cluster autoscaler
  3. manual scaling
  4. Azure Container Instances
Correct answer: B
Explanation:
To keep up with application demands in Azure Kubernetes Service (AKS), you may need to adjust the number of nodes that run your workloads. The cluster autoscaler component can watch for pods in your cluster that can't be scheduled because of resource constraints. When issues are detected, the number of nodes is increased to meet the application demand. Nodes are also regularly checked for a lack of running pods, with the number of nodes then decreased as needed. This ability to automatically scale up or down the number of nodes in your AKS cluster lets you run an efficient, cost-effective cluster. References:https://docs.microsoft.com/en-us/azure/aks/cluster-autoscaler
To keep up with application demands in Azure Kubernetes Service (AKS), you may need to adjust the number of nodes that run your workloads. The cluster autoscaler component can watch for pods in your cluster that can't be scheduled because of resource constraints. When issues are detected, the number of nodes is increased to meet the application demand. Nodes are also regularly checked for a lack of running pods, with the number of nodes then decreased as needed. This ability to automatically scale up or down the number of nodes in your AKS cluster lets you run an efficient, cost-effective cluster. 
References:
https://docs.microsoft.com/en-us/azure/aks/cluster-autoscaler



Question 7

You deploy an infrastructure for a big data workload. 
You need to run Azure HDInsight and Microsoft Machine Learning Server. You plan to set the RevoScaleR compute contexts to run rx function calls in parallel. 
What are three compute contexts that you can use for Machine Learning Server? Each correct answer presents a complete solution. 
NOTE: Each correct selection is worth one point.


  1. SQL
  2. Spark
  3. local parallel
  4. HBase
  5. local sequential
Correct answer: ABC
Explanation:
Remote computing is available for specific data sources on selected platforms. The following tables document the supported combinations. RxInSqlServer, sqlserver: Remote compute context. Target server is a single database node (SQL Server 2016 R Services or SQL Server 2017 Machine Learning Services). Computation is parallel, but not distributed.RxSpark, spark: Remote compute context. Target is a Spark cluster on Hadoop.RxLocalParallel, localpar: Compute context is often used to enable controlled, distributed computations relying on instructions you provide rather than a built-in scheduler on Hadoop. You can use compute context for manual distributed computing.References:https://docs.microsoft.com/en-us/machine-learning-server/r/concept-what-is-compute-context
Remote computing is available for specific data sources on selected platforms. The following tables document the supported combinations. 
  • RxInSqlServer, sqlserver: Remote compute context. Target server is a single database node (SQL Server 2016 R Services or SQL Server 2017 Machine Learning Services). Computation is parallel, but not distributed.
  • RxSpark, spark: Remote compute context. Target is a Spark cluster on Hadoop.
  • RxLocalParallel, localpar: Compute context is often used to enable controlled, distributed computations relying on instructions you provide rather than a built-in scheduler on Hadoop. You can use compute context for manual distributed computing.
References:
https://docs.microsoft.com/en-us/machine-learning-server/r/concept-what-is-compute-context



Question 8

Your company has 1,000 AI developers who are responsible for provisioning environments in Azure. 
You need to control the type, size, and location of the resources that the developers can provision. 
What should you use?


  1. Azure Key Vault
  2. Azure service principals
  3. Azure managed identities
  4. Azure Security Center
  5. Azure Policy
Correct answer: B
Explanation:
When an application needs access to deploy or configure resources through Azure Resource Manager in Azure Stack, you create a service principal, which is a credential for your application. You can then delegate only the necessary permissions to that service principal. References:https://docs.microsoft.com/en-us/azure/azure-stack/azure-stack-create-service-principals
When an application needs access to deploy or configure resources through Azure Resource Manager in Azure Stack, you create a service principal, which is a credential for your application. You can then delegate only the necessary permissions to that service principal. 
References:
https://docs.microsoft.com/en-us/azure/azure-stack/azure-stack-create-service-principals



Question 9

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?


  1. graphics processing units (GPUs)
  2. field-programmable gate arrays (FPGAs)
  3. central processing units (CPUs)
  4. 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 10

You have a solution that runs on a five-node Azure Kubernetes Service (AKS) cluster. The cluster uses an N-series virtual machine. 
An Azure Batch AI process runs once a day and rarely on demand. 
You need to recommend a solution to maintain the cluster configuration when the cluster is not in use. The solution must not incur any compute costs. 
What should you include in the recommendation?


  1. Downscale the cluster to one node
  2. Downscale the cluster to zero nodes
  3. Delete the cluster
Correct answer: A
Explanation:
An AKS cluster has one or more nodes. References:https://docs.microsoft.com/en-us/azure/aks/concepts-clusters-workloads
An AKS cluster has one or more nodes. 
References:
https://docs.microsoft.com/en-us/azure/aks/concepts-clusters-workloads









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