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NEW QUESTION 1

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 are deploying an Azure Machine Learning model to an Azure Kubernetes Service (AKS) container. You need to monitor the accuracy of each run of the model.
Solution: You configure Azure Application Insights.
Does this meet the goal?

  • A. Yes
  • B. No

Answer: A

NEW QUESTION 2

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?

  • A. graphics processing units (GPUs)
  • B. field-programmable gate arrays (FPGAs)
  • C. central processing units (CPUs)
  • D. application-specific integrated circuits (ASICs)

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.

NEW QUESTION 3

You have a container image that contains an Al solution. The solution will be used on demand and will only be needed a few hours each month.
You plan to use Azure Functions to deploy the environment on-demand.
You need to recommend the deployment process. The solution must minimize costs.
Which four actions should you recommend Azure Functions 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.
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Answer: A

Explanation:
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NEW QUESTION 4

You are designing an AI solution that will use IoT devices to gather data from conference attendees, and then later analyze the data. The IoT devices will connect to an Azure IoT hub.
You need to design a solution to anonymize the data before the data is sent to the IoT hub.
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.
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Answer: A

Explanation:
Step 1: Create a storage container
ASA Edge jobs run in containers deployed to Azure IoT Edge devices. Step 2: Create an Azure Stream Analytics Edge Job
Azure Stream Analytics (ASA) on IoT Edge empowers developers to deploy near-real-time analytical intelligence closer to IoT devices so that they can unlock the full value of device-generated data.
Scenario overview:
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Step 3: Add the job to the IoT devices in IoT References:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-edge

NEW QUESTION 5

Your company has factories in 10 countries. Each factory contains several thousand IoT devices. The devices present status and trending data on a dashboard.
You need to ingest the data from the IoT devices into a data warehouse.
Which two Microsoft Azure technologies should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

  • A. Azure Stream Analytics
  • B. Azure Data Factory
  • C. an Azure HDInsight cluster
  • D. Azure Batch
  • E. Azure Data Lake

Answer: CE

Explanation:
With Azure Data Lake Store (ADLS) serving as the hyper-scale storage layer and HDInsight serving as the Hadoop-based compute engine services. It can be used for prepping large amounts of data for insertion into a Data Warehouse
References:
https://www.blue-granite.com/blog/azure-data-lake-analytics-holds-a-unique-spot-in-the-modern-dataarchitectur

NEW QUESTION 6

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

Answer: AB

Explanation:
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

NEW QUESTION 7

You are designing an Al solution that must meet the following processing requirements:
• Use a parallel processing framework that supports the in-memory processing of high volumes of data.
• Use in-memory caching and a columnar storage engine for Apache Hive queries.
What should you use to meet each requirement? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
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Answer: A

Explanation:
Box 1: Apache Spark
Apache Spark is a parallel processing framework that supports in-memory processing to boost the performance of big-data analytic applications. Apache Spark in Azure HDInsight is the Microsoft implementation of Apache Spark in the cloud.
Box 2: Interactive Query
Interactive Query provides In-memory caching and improved columnar storage engine for Hive queries. References:
https://docs.microsoft.com/en-us/azure/hdinsight/spark/apache-spark-overview https://docs.microsoft.com/bs-latn-ba/azure/hdinsight/interactive-query/apache-interactive-query-get-started

NEW QUESTION 8

A data scientist deploys a deep learning model on an Fsv2 virtual machine. Data analysis is slow.
You need to recommend which virtual machine series the data scientist must use to ensure that data analysis occurs as quickly as possible.
Which series should you recommend?

  • A. ND
  • B. B
  • C. DC
  • D. Ev3

Answer: A

Explanation:
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.
The ND-series is focused on training and inference scenarios for deep learning. It uses the NVIDIA Tesla P40 GPUs. The latest version - NDv2 - features the NVIDIA Tesla V100 GPUs.
References:
https://azure.microsoft.com/en-in/pricing/details/virtual-machines/series/

NEW QUESTION 9

You are designing a solution that will analyze bank transactions in real time. The transactions will be evaluated by using an algorithm and classified into one of five groups. The transaction data will be enriched with information taken from Azure SQL Database before the transactions are sent to the classification process. The enrichment process will require custom code. Data from different banks will require different stored procedures.
You need to develop a pipeline for the solution.
Which components should you use for data ingestion and data preparation? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
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Answer: A

Explanation:
References:
https://docs.microsoft.com/bs-latn-ba/azure/architecture/example-scenario/data/fraud-detection

NEW QUESTION 10

You design an Al workflow that combines data from multiple data sources for analysis. The data sources are composed of:
• JSON files uploaded to an Azure Storage account
• On-premises Oracle databases
• Azure SQL databases
Which service should you use to ingest the data?

  • A. Azure Data Factory
  • B. Azure SQL Data Warehouse
  • C. Azure Data Lake Storage
  • D. Azure Databricks

Answer: A

Explanation:
References:
https://docs.microsoft.com/en-us/azure/data-factory/introduction

NEW QUESTION 11

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

Answer: D

Explanation:
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

NEW QUESTION 12

Your company has recently deployed 5,000 Internet-connected sensors for a planned AI solution.
You need to recommend a computing solution to perform a real-time analysis of the data generated by the sensors.
Which computing solution should you recommend?

  • A. an Azure HDInsight Storm cluster
  • B. Azure Notification Hubs
  • C. an Azure HDInsight Hadoop cluster
  • D. an Azure HDInsight R cluster

Answer: C

Explanation:
Azure HDInsight makes it easy, fast, and cost-effective to process massive amounts of data.
You can use HDInsight to process streaming data that's received in real time from a variety of devices. References:
https://docs.microsoft.com/en-us/azure/hdinsight/hadoop/apache-hadoop-introduction

NEW QUESTION 13

You are designing an AI solution that will analyze media data. The data will be stored in Azure Blob storage. You need to ensure that the storage account is encrypted by using a key generated by the hardware security module (HSM) of your company.
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.
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Answer: A

Explanation:
References:
https://docs.microsoft.com/en-us/azure/storage/common/storage-encryption-keys-portal https://docs.microsoft.com/en-us/azure/key-vault/key-vault-hsm-protected-keys

NEW QUESTION 14

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

NEW QUESTION 15

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?

  • A. horizontal pod autoscaler
  • B. cluster autoscaler
  • C. manual scaling
  • D. Azure Container Instances

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

NEW QUESTION 16

You are designing an Azure infrastructure to support an Azure Machine Learning solution that will have multiple phases. The solution must meet the following requirements:
• Securely query an on-premises database once a week to update product lists.
• Access the data without using a gateway.
• Orchestrate the separate phases.
What should you use? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
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Answer: A

Explanation:
Box 1: Azure App Service Hybrid Connections
With Hybrid Connections, Azure websites and mobile services can access on-premises resources as if they were located on the same private network. Application admins thus have the flexibility to simply lift-and-shift specific most front-end tiers to Azure with minimal configuration changes, extending their enterprise apps for hybrid scenarios.
Incorrect Option: The VPN connection solution both use gateways. Box 2: Machine Learning pipelines
Typically when running machine learning algorithms, it involves a sequence of tasks including pre-processing, feature extraction, model fitting, and validation stages. For example, when classifying text documents might involve text segmentation and cleaning, extracting features, and training a classification model with
cross-validation. Though there are many libraries we can use for each stage, connecting the dots is not as easy as it may look, especially with large-scale datasets. Most ML libraries are not designed for distributed computation or they do not provide native support for pipeline creation and tuning.
Box 3: Azure Databricks References:
https://azure.microsoft.com/is-is/blog/hybrid-connections-preview/ https://databricks.com/glossary/what-are-ml-pipelines

NEW QUESTION 17

You are designing an Al workflow that will aggregate data stored in Azure as JSON documents. You expect to store more than 2 TB of new data daily.
You need to choose the data storage service for the data. The solution must minimize costs. Which data storage service should you choose?

  • A. Azure File Storage
  • B. Azure Manage Disks
  • C. Azure Data Lake Storage
  • D. Azure Blob storage

Answer: D

Explanation:
Generally, 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). Data Lake is priced on volume, so it will go up as you reach certain tiers of volume.
References:
http://blog.pragmaticworks.com/azure-data-lake-vs-azure-blob-storage-in-data-warehousing

NEW QUESTION 18

You are designing a solution that will ingest temperature data from loT 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.
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Answer: A

Explanation:
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

NEW QUESTION 19

You need to design an application that will analyze real-time data from financial feeds.
The data will be ingested into Azure IoT Hub. The data must be processed as quickly as possible in the order in which it is ingested.
Which service should you include in the design?

  • A. Azure Data Factory
  • B. Azure Queue storage
  • C. Azure Stream Analytics
  • D. Azure Notification Hubs

Answer: C

Explanation:
References:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/real-time-processing

NEW QUESTION 20

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.

  • A. Microsoft Visual Studio Code
  • B. Azure Notebooks
  • C. Azure Machine Learning Studio
  • D. Microsoft Visual Studio

Answer: C

Explanation:
References:
https://github.com/MicrosoftDocs/azure-docs/blob/master/articles/machine-learning/studio/algorithm-choice.md

NEW QUESTION 21

You create an Azure Cognitive Services resource.
A data scientist needs to call the resource from Azure Logic Apps.
Which two values should you provide to the data scientist? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

  • A. endpoint URL
  • B. resource name
  • C. access key
  • D. resource group name
  • E. subscription ID

Answer: DE

Explanation:
References:
https://social.technet.microsoft.com/wiki/contents/articles/36074.logic-apps-with-azure-cognitive-service.aspx

NEW QUESTION 22

You are developing a mobile application that will perform optical character recognition (OCR) from photos. The application will annotate the photos by using metadata, store the photos in Azure Blob storage, and then score the photos by using an Azure Machine Learning model.
What should you use to process the data?

  • A. Azure Event Hubs
  • B. Azure Functions
  • C. Azure Stream Analytics
  • D. Azure Logic Apps

Answer: A

NEW QUESTION 23
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