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Microsoft AI-100 exam with answers
AI-100: Designing and Implementing an Azure AI Solution
- Analyze solution requirements (25-30%)
- Design AI solutions (40-45%)
- Implement and monitor AI solutions (25-30%)
https://docs.microsoft.com/en-us/learn/certifications/exams/ai-100
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QUESTION 1
You have an intelligent edge solution that processes data and outputs the data to an Azure Cosmos DB account that
uses the SQL API.
You need to ensure that you can perform full text searches of the data.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions
to the answer area and arrange them in the correct order.
Select and Place:
Correct Answer:
QUESTION 2
You are designing a solution that will ingest temperature data from IoT devices, calculate the average temperature, and
then take action based on the aggregated data. The solution must meet the following requirements:
Minimize the amount of uploaded data.
Take action based on the aggregated data as quickly as possible.
What should you include in the solution? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Hot Area:
Correct Answer:
Box 1: Azure Functions
Azure Function is a (serverless) service to host functions (little piece of code) that can be used for e. g. event driven
applications.
General rule is always difficult since everything depends on your requirement but if you have to analyze a data stream,
you should take a look at Azure Stream Analytics and if you want to implement something like a serverless event driven
or
timer-based application, you should check Azure Function or Logic Apps.
Note: Azure IoT Edge allows you to deploy complex event processing, machine learning, image recognition, and other
high value AI without writing it in-house. Azure services like Azure Functions, Azure Stream Analytics, and Azure
Machine
Learning can all be run on-premises via Azure IoT Edge.
Box 2: An Azure IoT Edge device
Azure IoT Edge moves cloud analytics and custom business logic to devices so that your organization can focus on
business insights instead of data management.
References:
https://docs.microsoft.com/en-us/azure/iot-edge/about-iot-edge
QUESTION 3
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?
A. Azure Queue storage
B. Azure Cosmos DB
C. Azure Blob storage
D. Azure SQL Database
Correct Answer: C
References: https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/data-storage
QUESTION 4
Your company has an Azure subscription that contains an Azure Active Directory (Azure AD) tenant.
Azure AD contains 500 user accounts for your company\\’s employees. Some temporary employees do NOT have user accounts in Azure AD You are designing a storage solution for video files and metadata files.
You plan to deploy an application to perform analysis of the metadata files. You need to recommend an authentication
solution to provide links to the video files. The solution must provide access to each file for only five minutes.
What should you include in the in the recommendation?
A. Secondary Storage Key
B. Primary Storage Key
C. Shared Access Signature
D. Azure Active Directory
Correct Answer: A
QUESTION 5
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
Correct Answer: A
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
QUESTION 6
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.
A. SQL
B. Spark
C. local parallel
D. HBase
E. local sequential
Correct Answer: ABC
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 builtin
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 7
You are designing an AI solution that will be used to find buildings in aerial pictures.
Users will upload the pictures to an Azure Storage account. A separate JSON document will contain for the pictures.
The solution must meet the following requirements:
Store metadata for the pictures in a data store.
Run a custom vision Azure Machine Learning module to identify the buildings in a picture and the position of the
buildings
Correct Answer:
Box 1: Azure Blob Storage
Containers and blobs support custom metadata, represented as HTTP headers.
Box 2: NV
The NV-series enables powerful remote visualisation workloads and other graphics-intensive applications backed by the
NVIDIA Tesla M60 GPU.
Note: The N-series is a family of Azure Virtual Machines with GPU capabilities. GPUs are ideal for compute and
graphics-intensive workloads, helping customers to fuel innovation through scenarios like high-end remote visualisation,
deep
learning and predictive analytics.
Box 3: F
F-series VMs feature a higher CPU-to-memory ratio. Example use cases include batch processing, web servers,
analytics and gaming.
Incorrect:
A-series VMs have CPU performance and memory configurations best suited for entry level workloads like development
and test.
References:
https://azure.microsoft.com/en-in/pricing/details/virtual-machines/series/
QUESTION 8
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
Correct Answer: B
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:
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 9
You are designing a solution that uses drones to monitor remote locations for anomalies. The drones have Azure IoT
Edge devices. The solution must meet the following requirements:
Email a user the picture and location of an anomaly when an anomaly is detected.
Use a video stream to detect anomalies at the location.
Send the pictures and location information to Azure.
Use the latest amount of code possible.
You develop a custom vision Azure Machine Learning module to detect the anomalies.
Which service should you use for each requirement? To answer, drag the appropriate services to the correct
requirements. Each service 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.
Select and Place:
Correct Answer:
Explanation/Reference:
Box 1: Azure IOT Edge Example:
You configure the Remote Monitoring solution to respond to anomalies detected by an IoT Edge device. IoT Edge
devices let you process telemetry at the edge to reduce the volume of telemetry sent to the solution and to enable faster
responses to events on devices. Box 2: Azure Functions Box 3: Azure Logic Apps
References: https://docs.microsoft.com/en-us/azure/iot-accelerators/iot-accelerators-remote-monitoring-edge
QUESTION 10
You design an AI solution that uses an Azure Stream Analytics job to process data from an Azure IoT hub.
The IoT hub receives time series data from thousands of IoT devices at a factory. The job outputs millions of messages
per second. Different applications consume the messages as they are available. The messages must be purged.
You need to choose an output type for the job.
What is the best output type to achieve the goal? More than one answer choice may achieve the goal.
A. Azure Event Hubs
B. Azure SQL Database
C. Azure Blob storage
D. Azure Cosmos DB
Correct Answer: D
Stream Analytics can target Azure Cosmos DB for JSON output, enabling data archiving and low- latency queries on
unstructured JSON data.
References: https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-documentdb-output
QUESTION 11
Your company develops an AI application that is orchestrated by using Kubernetes. You need to deploy the application.
Which three actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct
selection is worth one point.
A. Create a Kubernetes cluster.
B. Create an Azure Container Registry instance.
C. Create a container image file.
D. Create a Web App for Containers.
E. Create an Azure container instance.
Correct Answer: CDE
QUESTION 12
You have a solution that runs on a five-node Azure Kubernetes Service (AKS) cluster. The cluster uses an Nseries
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?
A. Downscale the cluster to one node
B. Downscale the cluster to zero nodes
C. Delete the cluster
Correct Answer: A
An AKS cluster has one or more nodes.
References: https://docs.microsoft.com/en-us/azure/aks/concepts-clusters-workloads
QUESTION 13
You have a database that contains sales data.
You plan to process the sales data by using two data streams named Stream1 and Stream2. Stream1 will be used for
purchase order data. Stream2 will be used for reference data.
The reference data is stored in CSV files.
You need to recommend an ingestion solution for each data stream.
What two solutions should you recommend? Each correct answer is a complete solution.
NOTE: Each correct selection is worth one point.
A. an Azure event hub for Stream1 and Azure Blob storage for Stream2
B. Azure Blob storage for Stream1 and Stream2
C. an Azure event hub for Stream1 and Stream2
D. Azure Blob storage for Stream1 and Azure Cosmos DB for Stream2
E. Azure Cosmos DB for Stream1 and an Azure event hub for Stream2
Correct Answer: AB
Stream1 – Azure Event Stream2 – Blob Storage Azure Event Hubs is a highly scalable data streaming platform and
event ingestion service, capable of receiving and processing millions of events per second. Event Hubs can process
and store events, data, or telemetry produced by distributed software and devices. Data sent to an event hub can be
transformed and stored using any real-time analytics provider or batching/storage adapters. Event Hubs provides
publishsubscribe capabilities with low latency at massive scale, which makes it appropriate for big data scenarios.
Stream1, Stream2 – Blob Storage Stream Analytics has first-class integration with Azure data streams as inputs from
three kinds of resources: Azure Event Hubs Azure IoT Hub Azure Blob storage These input resources can live in the
same Azure subscription as your Stream Analytics job or a different subscription.
References: https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/real-time- ingestion
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