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Take a look at our Free Microsoft AI-102 Exam Questions and Answers to check how perfect they are for your exam preparation. Once you buy it, you will be able to get free updates for Microsoft AI-102l exam questions for up to 12 months. We also ensure that our support team and the core team of AI-102 provide services to resolve all your issues. There is a high probability that you will be successful in the Microsoft AI-102 exam on the first attempt after buying our prep material.
Prep Options to Choose
The preparation process for the exam is a vital step on your way of passing the test, that’s why you need to find the most actual and reliable resources. Microsoft offers two options for you to choose from online free training or instructor-led, which is a paid one. For example, the free training represents a collection of learning paths each of which contains the different number of modules, from one to five. Thus, you can choose which ones to follow: Prepare for AI engineering (1 module), Process and Translate Speech with Azure Cognitive Speech Services (2 modules), Create computer vision solutions with Azure Cognitive Services (3 modules), to name a few. The paid course is known to be Course AI-102T00: Designing and Implementing a Microsoft Azure AI Solution and lasts for 4 days. It is intended for software developers interested in developing skills to build AI infused apps that use Azure Cognitive Search and Services, and Microsoft Bot Framework.
In addition, you can check the Amazon website to find the books on the topics included in the exam to ace it from the first attempt. Only after the successful passing the AI-102 Exam you will earn the Microsoft Certified: Azure AI Engineer Associate certification. So, good luck!
Reliable Microsoft AI-102 Exam Cram & AI-102 Real Sheets
This format is for candidates who do not have the time or energy to use a computer or laptop for preparation. The Microsoft AI-102 PDF file includes real Microsoft AI-102 questions, and they can be easily printed and studied at any time. Dumpkiller regularly updates its PDF file to ensure that its readers have access to the updated questions.
Topics of AI-102: Designing and Implementing an Azure AI Solution Exam
Candidates should apprehend the examination topics before they begin of preparation.because it'll extremely facilitate them in touch the core. Our AI-102 exam dumps will include the following topics:
1. Analyze solution requirements (25-30%)
Recommend Cognitive Services APIs to meet business requirements
- Select the processing architecture for a solution
- Identify components and technologies required to connect service endpoints
- Select the appropriate AI models and services
- Select the appropriate data processing technologies
- Identify automation requirements
Map security requirements to tools, technologies, and processes
- Identify auditing requirements
- Identify which users and groups have access to information and interfaces
- Identify appropriate tools for a solution
- Identify processes and regulations needed to conform with data privacy, protection, and regulatory requirements
Select the software, services, and storage required to support a solution
- Identify appropriate services and tools for a solution
- Identify storage required to store logging, bot state data, and Cognitive Services output
- Identify integration points with other Microsoft services
2. Design AI solutions (40-45%)
Design solutions that include one or more pipelines
- Design a strategy for ingest and egress data
- Select an AI solution that meet cost constraints
- Design pipelines that call Azure Machine Learning models
Design solutions that uses Cognitive Services
- Design solutions that use vision, speech, language, knowledge, search, and anomaly detection APIs
Design solutions that implement the Bot Framework
- Integrate bots with Azure app services and Azure Application Insights
- Design bot services that use Language Understanding (LUIS)
- Design bots that integrate with channels
- Integrate bots and AI solutions
Design the compute infrastructure to support a solution
- Select a compute solution that meets cost constraints
- Identify whether to use a cloud-based, on-premises, or hybrid compute infrastructure
- Identify whether to create a GPU, FPGA, or CPU-based solution
Design for data governance, compliance, integrity, and security
- Define how users and applications will authenticate to AI services
- Ensure appropriate governance of data
- Design a content moderation strategy for data usage within an AI solution
- Design strategies to ensure that the solution meets data privacy regulations and industry standards
- Ensure that data adheres to compliance requirements defined by your organization
3. Implement and monitor AI solutions (25-30%)
Implement an AI workflow
- Manage the flow of data through the solution components
- Develop AI pipelines
- Develop streaming solutions
Integrate AI services with solution components
- Configure prerequisite components and input datasets to allow the consumption of Cognitive Services APIs
- Configure integration with Cognitive Services
- Implement Azure Search in a solution
- Configure prerequisite components to allow connectivity to the Bot Framework
Monitor and evaluate the AI environment
- Recommend changes to an AI solution based on performance data
- Identify the differences between expected and actual workflow throughput
- Maintain an AI solution for continuous improvement
- Monitor AI components for availability
- Identify the differences between KPIs, reported metrics, and root causes of the differences
Microsoft Designing and Implementing a Microsoft Azure AI Solution Sample Questions (Q333-Q338):
NEW QUESTION # 333
You are planning the product creation project.
You need to recommend a process for analyzing videos.
Which four 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. (Choose four.)
Answer:
Explanation:
Reference:
https://azure.microsoft.com/en-us/blog/get-video-insights-in-even-more-languages/
https://docs.microsoft.com/en-us/azure/media-services/video-indexer/video-indexer-output-json-v2
NEW QUESTION # 334
You are building an app that will process incoming email and direct email messages to either French or English language support teams.
Which Azure Cognitive Services API should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
NEW QUESTION # 335
You have 100,000 images.
You need to build an app that will perform the following actions:
* Identify road signs in the images and extract the text on the signs.
* Analyze the text to identify well-known locations.
The solution must minimize development effort.
What should you use for each action? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
The app must:
Identify road signs and extract text from images
This is an OCR (Optical Character Recognition) task.
Azure AI Vision provides OCR capabilities to detect and extract text from images (including road signs).
Correct choice: Azure AI Vision.
Analyze the text to identify well-known locations
Once text is extracted, you need to detect if it refers to well-known places (e.g., cities, landmarks).
This is a Named Entity Recognition (NER) problem.
Azure AI Language service includes entity recognition for identifying locations, people, organizations, etc.
Correct choice: Azure AI Language.
Other options:
Azure AI Document Intelligence # Focused on structured documents (invoices, forms), not road signs.
Azure AI Search # Useful for indexing and search, not text extraction or entity recognition.
Correct Answer Matrix:
Extract the text # Azure AI Vision
Identify well-known locations # Azure AI Language
Microsoft References
Azure AI Vision - OCR text extraction
Azure AI Language - Named Entity Recognition (NER)
NEW QUESTION # 336
You are building a Language Understanding solution.
You discover that many intents have similar utterances containing airport names or airport codes.
You need to minimize the number of utterances used to fram the model.
Which type of custom entity should you use?
- A. Pattera.any
- B. list
- C. regular expression
- D. machine-learning
Answer: B
Explanation:
* When many utterances differ only by airport names or codes, you don't want to list them all.
* A Pattern.any entity acts as a placeholder for free-form input inside patterns, allowing the model to accept any word/phrase in that slot.
* Machine-learning entities require lots of labeled utterances.
* List entities work for fixed sets (e.g., specific known airport codes), but this scenario implies dynamic airport data.
* Regex entities are for structured formats (like postal codes).
Reference: Pattern.any entities in Language Understanding
NEW QUESTION # 337
You are developing an application that will recognize faults in components produced on a factory production line. The components are specific to your business.
You need to use the Custom Vision API to help detect common faults.
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.
Answer:
Explanation:
Explanation:
Comprehensive Detailed Explanation You are tasked with building a solution using Custom Vision to detect faults in factory components. Custom Vision follows a defined workflow:
* Create a project
* Before anything else, you must create a Custom Vision project in the Azure AI portal.
* This project specifies the type of model you are building: classification (classifying entire images) or object detection (locating items within images).
* Since the question is about detecting faults in specific components (a business-specific scenario), it aligns with classification unless explicitly stated otherwise.
* Upload and tag images
* You must provide training data.
* Upload a set of images showing both faulty and non-faulty components.
* Tag each image correctly so that the model can learn from labeled examples.
* Train the classifier model
* Once images are tagged, you run training on the dataset to generate the classifier model.
* The model can then be published and used for inference via API calls.
* Initialize the training dataset: This is not a direct step in Custom Vision; instead, uploading and tagging images inherently prepares the training dataset.
* Train the object detection model: Object detection is used if you need bounding boxes around items within an image. Since the question is about recognizing faults in a component (faulty vs non-faulty), a classifier model is appropriate.
Why not the other options?
Correct Sequence:
* Create a project
* Upload and tag images
* Train the classifier model
* Quickstart: Build an image classification project with the Custom Vision portal
* Custom Vision overview
* Train and test a Custom Vision model
Microsoft References
NEW QUESTION # 338
......
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