Get AI-100 dumps & AI-100 Real Exam Questions
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AI-1 00
 Designing and Implementing an Azure AI
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Question #:1 
Your company plans to deploy an AI solution that processes IoT data in real-time.
You need to recommend a solution for the planned deployment that meets the following requirements:
Sustain up to 50 Mbps of events without throttling.
Retain data for 60 days.
What should you recommend?
A.  Apache Kafka
B.  Microsoft Azure IoT Hub
C.  Microsoft Azure Data Factory
D.  Microsoft Azure Machine Learning
Answer: A
Explanation
Apache Kafka is an open-source distributed streaming platform that can be used to build real-time streaming
data pipelines and applications.
References:
https://docs.microsoft.com/en-us/azure/hdinsight/kafka/apache-kafka-introduction
Microsoft - AI-100
Question #:2
Note: This question is part of a series of questions that present the same scenario. Each question in the
series contains a unique solution that might meet the stated goals. Some question sets might have more
than one correct solution, while others might not have a correct solution.
After you answer a question, you will NOT be able to return to it. As a result, these questions will not
appear in the review screen.
You have Azure IoT Edge devices that generate streaming data.
On the devices, you need to detect anomalies in the data by using Azure Machine Learning models. Once an
anomaly is detected, the devices must add information about the anomaly to the Azure IoT Hub stream.
Solution: You deploy Azure Functions as an IoT Edge module.
Does this meet the goal?
A.  Yes
B.  No
Answer: B
Explanation
Instead use Azure Stream Analytics and REST API.
Note. Available in both the cloud and Azure IoT Edge, Azure Stream Analytics offers built-in machine
learning based anomaly detection capabilities that can be used to monitor the two most commonly occurring
anomalies:
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temporary and persistent.
Stream Analytics supports user-defined functions, via REST API, that call out to Azure Machine Learning
endpoints.
References:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-machine-learning-anomaly-detection
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?
A.  Azure Blob storage
B.  Azure Cosmos DB
C.  Azure HDInsight
D.  Azure Table storage
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
Question #:4 
You have an Azure Machine Learning model that is deployed to a web service.
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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.
A.  Generate a shared access signature (SAS)
B.  Obtain an SSL certificate
C.  Add a deployment slot
D.  Update the web service
E.  Update DNS
F.  Create an Azure Key Vault
Answer: B D E
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
Microsoft - AI-100
Question #:5
You need to design the Butler chatbot solution to meet the technical requirements.
What is the best channel and pricing tier to use? More than one answer choice may achieve the goal Select the
BEST answer.
A.  standard channels that use the S1 pricing tier
B.  standard channels that use the Free pricing tier
C.  premium channels that use the Free pricing tier
D.  premium channels that use the S1 pricing tier
Answer: D
Explanation
References:
https://azure.microsoft.com/en-in/pricing/details/bot-service/
Question #:6
You need to recommend a data storage solution that meets the technical requirements.
What is the best data storage solution to recommend? More than one answer choice may achieve the goal.
Select the BEST answer.
A.  Azure Databricks
B.  Azure SQL Database
C.  Azure Table storage
D.  Azure Cosmos DB
Answer: B
Explanation
References:
https://docs.microsoft.com/en-us/azure/architecture/example-scenario/ai/commerce-chatbot
Microsoft - AI-100
Question #:7
You need to meet the testing requirements for the data scientists.
Which three actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A.  Deploy an Azure Kubernetes Service (AKS) cluster to the East US 2 region
B.  Get the docker image from mcr.microsoft.com/azure-cognitive-services/sentiment:latest
C.  Deploy an Azure an Azure Container Service cluster to the West Europe region
D.  Export the production version of the Language Understanding (LUIS) app
E.  Deploy a Kubernetes cluster to Azure Stack
F.  Get the docker image from mcr.microsoft.com/azure-cognitive-services/luis:latest
G.  Export the staging version of the Language and Understanding (LUIS) app
Answer: E F G
Explanation
Scenario: Data scientists must test Butler by using ASDK.
Note: Contoso wants to provide a new version of the Bookings app that will provide a highly available,
reliable service for booking travel packages by interacting with a chatbot named Butler.
E: The ASDK (Azure Stack Development Kit) is meant to provide an environment in which you can evaluate
Azure Stack and develop modern applications using APIs and tooling consistent with Azure in a
nonproduction environment.
Microsoft Azure Stack integrated systems range in size from 4-16 nodes, and are jointly supported by a
hardware partner and Microsoft.
F: The Language Understanding (LUIS) container loads your trained or published Language Understanding
model, also known as a LUIS app, into a docker container and provides access to the query predictions from
the container's API endpoints.
Use the docker pull command to download a container image from the
mcr.microsoft.com/azure-cognitiveservices/luis repository:
docker pull mcr.microsoft.com/azure-cognitive-services/luis:latest
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G: You can test using the endpoint with a maximum of two versions of your app. With your main or live
version of your app set as the production endpoint, add a second version to the staging endpoint.
Reference:
https://docs.microsoft.com/en-us/azure-stack/asdk/asdk-what-is
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-container-howto
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-concept-test
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