Are you preparing for the Microsoft Azure AI Solution certification exam (AI-102)? Here is a set of 25+ free practice questions, designed to help you get ready for the exam. These questions closely resemble those you might encounter in the real test and are aligned with the key topics covered in the exam.
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Key Areas Covered in the AI-102 Exam: A Comprehensive Guide to Your Exam Preparation
The AI-102 exam, officially known as Designing and Implementing an Azure AI Solution, is a certification exam offered by Microsoft for professionals who wish to prove their proficiency in working with Azure AI technologies. Passing this exam demonstrates your ability to plan, build, and manage AI solutions on Microsoft Azure, making it a crucial credential for anyone aiming to specialize in AI solutions, machine learning, or data science within the Azure ecosystem.
The AI-102 exam evaluates your expertise in several domains, with each domain representing a key component of Azure AI services and solutions. Below is a breakdown of the main domains covered in the AI-102 exam and an overview of the skills and knowledge required to succeed in each area.
1. Planning and Managing Azure AI Solutions (15–20%)
This domain focuses on the ability to plan and manage AI solutions within Azure, including the integration of various Azure AI services, evaluating requirements, and ensuring alignment with business objectives. Candidates will need to demonstrate a comprehensive understanding of the following aspects:
- Identifying AI Solution Requirements: You must be able to assess business needs and determine the most appropriate Azure AI services and tools to address those needs.
- Managing AI Project Lifecycles: This includes planning the development, testing, and deployment phases of AI projects. Understanding how to monitor AI solutions post-deployment and manage iterative improvements is also essential.
- Budgeting and Cost Management: It’s important to be familiar with estimating costs associated with using Azure AI services, including how to track and manage spending to ensure the solution remains within budget.
- Security and Compliance: Ensure that the AI solution adheres to industry standards and regulatory requirements, particularly in regard to data privacy, security, and governance policies.
2. Implementing Decision Support Solutions (10–15%)
In this domain, candidates are expected to implement solutions that support decision-making processes using Azure AI. This includes both predictive analytics and recommendation systems that assist businesses in making data-driven decisions.
Key areas of focus include:
- Building Predictive Models: Using machine learning to create predictive models that help in forecasting future trends, such as sales projections or customer behavior.
- Recommendation Systems: Implementing recommendation algorithms (e.g., collaborative filtering, content-based) to provide personalized content, product suggestions, or other types of recommendations to users.
- Integrating with Azure Cognitive Services: Leveraging Azure services like Azure Machine Learning and Azure Cognitive Services to enhance decision-making with powerful data insights.
3. Building Computer Vision Solutions (15–20%)
Computer vision is one of the most widely used applications of AI today, and the AI-102 exam covers the tools and techniques necessary to build these solutions. In this domain, you will be expected to build, implement, and manage computer vision solutions using Azure AI services.
Topics covered in this section include:
- Image Classification: Using tools like Azure Custom Vision to train AI models that can classify and categorize images.
- Object Detection: Developing solutions that can identify and locate objects within an image or video stream.
- Face Recognition: Implementing facial recognition solutions using Azure Face API to detect and recognize faces in images and videos.
- OCR (Optical Character Recognition): Extracting text from images or scanned documents using Azure’s Computer Vision API.
- Video Analysis: Using Azure’s Video Indexer to analyze and extract meaningful information from video files.
4. Developing Natural Language Processing (NLP) Solutions (30–35%)
Natural Language Processing (NLP) is one of the most crucial and expansive areas of AI development. The AI-102 exam places significant emphasis on developing solutions that can process, analyze, and understand human language. This domain is the largest in terms of exam weight, and candidates should be well-versed in the following:
- Text Analytics: Using Azure services like Text Analytics API to extract key information such as sentiment, entities, language, and key phrases from text.
- Speech-to-Text and Text-to-Speech: Implementing solutions that can convert speech to text or generate speech from written text using Azure’s Speech SDK.
- Language Understanding (LUIS): Developing conversational AI applications by leveraging Language Understanding Intelligent Service (LUIS) to enable machines to interpret user inputs in a natural language.
- Question-Answering Systems: Building and fine-tuning systems capable of providing accurate answers to questions posed in natural language, using tools like QnA Maker.
- Text Translation: Using the Translator Text API to automatically translate text from one language to another, which is critical for applications with a global user base.
5. Implementing Knowledge Mining and Document Intelligence (10–15%)
This domain focuses on how to extract valuable insights from large volumes of unstructured data (such as documents, forms, and emails). Azure provides a range of services to enable knowledge mining and document understanding.
Key areas of focus in this domain include:
- Azure Cognitive Search: Implementing a powerful search solution that leverages AI to index and search documents, images, and other content across multiple data sources.
- Document Analysis: Using Form Recognizer to extract and analyze data from documents, such as invoices, receipts, or forms, with minimal human intervention.
- Knowledge Mining: Utilizing Azure Cognitive Services to mine data and automatically categorize, tag, and extract meaning from documents and other unstructured data sources.
6. Developing Generative AI Solutions (10–15%)
Generative AI is an exciting field where models are designed to generate new data based on learned patterns. The AI-102 exam includes a focus on this cutting-edge area of AI, and candidates should be familiar with the tools and techniques used to develop generative AI solutions on Azure.
Important topics include:
- Text Generation: Using models like GPT-3 or other natural language models to generate human-like text for applications such as content creation, chatbots, or interactive agents.
- Image Generation: Implementing solutions that can create new images, designs, or artwork based on a set of input parameters or prompts using generative models like DALL-E.
- AI-Assisted Creativity: Developing AI applications that assist in creative processes such as writing, art, music composition, or design, using Azure AI tools and APIs.
- Deep Learning Models: Building deep learning models for tasks like image or text generation using Azure Machine Learning and other relevant tools.
The AI-102 exam is an essential step for anyone looking to specialize in building, implementing, and managing AI solutions using Microsoft Azure. Understanding the key domains tested in the exam allows you to focus your study efforts on the most relevant areas. By mastering topics ranging from decision support systems to natural language processing and generative AI, you will be well-equipped to pass the exam and demonstrate your expertise in Azure AI technologies.
Now, let’s dive into your preparation. By reviewing these key areas, practicing with sample questions, and gaining hands-on experience with Azure AI services, you’ll be on your way to achieving your Azure AI certification and advancing your career in the AI field!
Domain: Building Computer Vision Solutions
Question 1: Converting a Standard Domain to a Compact Domain for Local Execution
The Custom Vision Service enables users to export trained models that can be used locally for real-time image classification. To convert a standard domain to a compact domain, certain steps must be followed. The correct sequence ensures that the model is retrained and optimized for local execution. Below is the correct order of actions to achieve this conversion:
Options:
- Select & save the new domain -> Retrain the model -> Select the project -> Export the model
B. Select the project -> Select & save the new domain -> Export the model -> Retrain the model
C. Select & save the new domain -> Select the project -> Export the model -> Retrain the model
D. Select the project -> Select & save the new domain -> Retrain the model -> Export the model
Correct Answer: D
Explanation:
To convert a standard domain to a compact domain for local execution, you must follow the correct sequence of steps:
- Select the project: Begin by selecting the project where the classifier resides. This is the first step in managing your vision model.
- Select & save the new domain: After selecting the project, you need to select and save the new compact domain. The compact domain is optimized for local execution.
- Retrain the model: Once the new domain is selected, you need to retrain the model to ensure it is compatible with the compact domain settings. Retraining ensures that the model performs well in the new domain context.
- Export the model: After retraining, export the model for use in local real-time classification.
This sequence ensures that the model is fully optimized and ready for local deployment.
Additional Information:
The Custom Vision Service offers several export options for models, including compact domains for offline usage. For detailed information on exporting and using models in local environments, refer to the official documentation provided by Microsoft. This process allows developers to integrate vision models into local applications without the need for continuous cloud connectivity.
Domain: Implementing Knowledge Mining and Document Intelligence Solutions
Question 2: Addressing User Access Issues for Azure Search Service
You have granted Reader access to a group of users so they can search and manage service operations, such as querying search data. However, the users are unable to perform their tasks. What should you do?
Options:
- Use API keys to allow access for content operations
B. Grant Contributor Role through the IAM page of the Azure Portal
C. Use a Service Principal to grant content operation access
D. Grant Owner Role through the IAM page of the Azure Portal
Correct Answer: A
Explanation:
In the case of Azure Search Service, roles like Reader, Contributor, or Owner do not grant the necessary permissions for performing content operations, such as querying search data. These roles are typically used for managing Azure resources and service configurations, but they don’t cover access to the actual data within the search service.
To perform content operations like querying or managing the search index, you must use API keys. API keys are specifically designed to allow access to search service endpoints, including read and write operations on the search data. These keys are required for users or applications to interact with the search service data programmatically.
Here’s a breakdown of the other options:
- B. Grant Contributor Role: While the Contributor role allows users to manage resources, it still doesn’t grant permission to access the data within the search service, which is necessary for content operations like querying.
- C. Use a Service Principal: A Service Principal is generally used for automated access, but on its own, it does not grant permission to access the search service’s data. API keys are still needed for content operations.
- D. Grant Owner Role: The Owner role provides full access to Azure resources, but like the Contributor role, it does not grant the specific permissions needed to interact with the data in Azure Search Service. API keys are the correct approach for managing access to the search content.
Additional Information:
To manage and securely handle API keys for the Azure Cognitive Search service, you can generate and assign query keys and admin keys within the Azure portal under the search service’s “Keys” section. These keys are used to authenticate requests made to the search service endpoints.
For more information on managing search service access and the use of API keys, check out the official Azure Cognitive Search documentation.
Domain: Planning and Managing Azure Cognitive Services Solutions
Question 3: Mapping Cognitive Services Roles to Permissions
You are managing an Azure Cognitive Service Custom Vision resource. In this case, you need to correctly map the cognitive services roles to their respective permissions.
Role – Permission Mapping:
- Cognitive Services Custom Vision Reader: View projects and export models
- Cognitive Services Custom Vision Deployment: View projects, delete images, and add tags
- Cognitive Services Custom Vision Trainer: Edit projects and train models
- Cognitive Services Custom Vision Labeler: View projects, but cannot modify models
Which of the following options correctly maps the roles and permissions?
Options:
- R11 -> P14; R12 -> P12; R13 -> P13; R14 -> P11
B. R11 -> P11; R12 -> P13; R13 -> P12; R14 -> P14
C. R11 -> P14; R12 -> P11; R13 -> P13; R14 -> P12
D. R11 -> P14; R12 -> P13; R13 -> P12; R14 -> P11
Correct Answer: C
Explanation:
The correct mapping of roles to permissions is as follows:
- Cognitive Services Custom Vision Reader (R11) should have the ability to view projects and export models. Therefore, it maps to P14 (view and export models).
- Cognitive Services Custom Vision Deployment (R12) should be able to view projects, delete images, and add tags. This corresponds to P11 (view projects, delete images, and add tags).
- Cognitive Services Custom Vision Trainer (R13) can edit projects and train models, which aligns with P13 (edit projects and train models).
- Cognitive Services Custom Vision Labeler (R14) is limited to viewing projects but cannot modify models. This corresponds to P12 (view projects without modifying models).
Thus, the correct mapping is:
- R11 -> P14 (Custom Vision Reader can view projects and export models)
- R12 -> P11 (Custom Vision Deployment can view projects, delete images, and add tags)
- R13 -> P13 (Custom Vision Trainer can edit projects and train models)
- R14 -> P12 (Custom Vision Labeler can view projects, but cannot modify models)
Additional Information:
Understanding role-based access control (RBAC) for Azure Cognitive Services like Custom Vision is crucial to ensuring that users have the right level of permissions to perform their tasks. For more details on the various roles and permissions, refer to the official Azure documentation on Custom Vision and RBAC.
Domain: Building Computer Vision Solutions
Question 4: Using the Read API to Extract Text from Images
The Read API in Azure Computer Vision is designed to extract text from text-heavy images, such as documents, scanned pages, and other image-based content. This powerful tool provides users with the ability to perform Optical Character Recognition (OCR) on a variety of file formats and images.
Which of the following statements about the Read API are correct? Select three.
Options:
- The Read API fetches text from documents in a single step
B. It supports around 23 languages for printed text
C. The Read API supports formats like PDF, BMP, JPEG, PNG, and TIFF
D. The Read API can process up to 2000 pages in PDF format
E. The Read API supports images up to 10000×10000 pixels
Correct Answers: C, D, E
Explanation:
- C. The Read API supports formats like PDF, BMP, JPEG, PNG, and TIFF:
The Read API indeed supports a wide range of file formats, including PDF, BMP, JPEG, PNG, and TIFF. This flexibility allows users to extract text from various sources, whether they are scanned images, photographs, or documents. - D. The Read API can process up to 2000 pages in PDF format:
The Read API is capable of processing up to 2000 pages in PDF format, making it ideal for handling large documents such as books, reports, or multi-page contracts. This capability is crucial for businesses and organizations that deal with large volumes of text-based PDFs. - E. The Read API supports images up to 10000×10000 pixels:
The Read API can process high-resolution images up to 10000×10000 pixels. This allows for the recognition of text in very detailed or large images, ensuring that even high-quality documents or images with fine print can be processed efficiently.
Incorrect Answers:
- A. The Read API fetches text from documents in a single step:
This is not accurate. While the Read API simplifies the text extraction process, it typically involves multiple steps (like submitting the image, processing, and retrieving results). It doesn’t extract text in just a single step automatically; there is a waiting time for the result after submission. - B. It supports around 23 languages for printed text:
This is incorrect. The Read API supports more than 23 languages for printed text. In fact, it supports over 100 languages for OCR tasks, including many non-Latin script languages.
Additional Information:
The Read API is part of Azure’s Computer Vision suite and is designed for reading text from images. It can be used for a variety of tasks, including extracting printed text from documents, receipts, and business cards. It also provides advanced capabilities for handling different image sizes and formats, making it highly adaptable for many use cases.
For more detailed information about how to use the Read API and its full capabilities, refer to the official Azure Computer Vision documentation.
Domain: Implementing Natural Language Processing Solutions
Question 5: Language Detection with Confidence Scores
You are using the Language Detection feature of Azure Cognitive Language Service to detect languages from text input. Based on the confidence score in the JSON response, you need to determine which values should fill the “name” field for each output.
Output 1: Confidence score = 1
Output 2: Confidence score = 0
Which values should fill the “name” field in each output?
Options:
- “fr”
B. “French”
C. “countryHint”
D. “Unknown”
Correct Answers: B and D
Explanation:
- B. “French” (for Output 1 with Confidence Score = 1):
When the confidence score is 1, it indicates a high certainty in the detected language. In this case, the language detected is French, so the “name” field should be filled with “French”. The high confidence score signifies that the language is most likely French with no ambiguity. - D. “Unknown” (for Output 2 with Confidence Score = 0):
When the confidence score is 0, it indicates that the language cannot be reliably detected or is unknown. Therefore, the “name” field should be filled with “Unknown”. This shows that the service could not determine the language with any degree of certainty.
Incorrect Answers:
- A. “fr”:
While “fr” is the language code for French (ISO 639-1 code), the question specifically asks for the “name” field, which should be the language name (i.e., “French”) rather than the code. Hence, “fr” would not be correct in this context. - C. “countryHint”:
“countryHint” is not relevant to the language detection result. The name field should contain the language name or an indication that the language is unknown, but countryHint is not a valid choice for this scenario.
Additional Information:
Language detection in Azure Cognitive Language Service uses advanced machine learning models to determine the language of a given text. The confidence score reflects how certain the system is about its language detection. A score closer to 1 indicates high confidence in the detected language, while a score of 0 means no reliable language could be determined.
For further details on language detection and its response structure, refer to the official Azure Cognitive Language Service documentation.
Domain: Implementing Natural Language Processing Solutions
Question 6: Migrating LUIS Authoring Resources
LUIS (Language Understanding Intelligent Service) authoring now requires authentication through Azure resources. As a result, there is a migration process to an Azure-based authoring resource. You need to understand which statements about this migration process are true.
Options:
- If you own the application, it will automatically migrate with you
B. Application owners can migrate a subset of the application
C. Collaborators are automatically added to the Azure authoring resource
D. If you are a collaborator, the application will migrate automatically
E. Before migration, coauthors are called collaborators; after migration, they are called contributors
Correct Answers: A and E
Explanation:
- A. If you own the application, it will automatically migrate with you:
When migrating to an Azure authoring resource, if you are the owner of the application, it will automatically migrate with you to the new resource. This streamlines the process for application owners, ensuring that their applications are transferred seamlessly to the Azure environment. - E. Before migration, coauthors are called collaborators; after migration, they are called contributors:
Prior to migration, coauthors in LUIS are referred to as collaborators. After migrating to the Azure authoring resource, the term for coauthors changes, and they are now called contributors. This change is part of the shift to the Azure platform’s terminology.
Incorrect Answers:
- B. Application owners can migrate a subset of the application:
This is incorrect. When migrating to the Azure authoring resource, you cannot migrate only a subset of the application. The migration process involves migrating the entire application to the Azure environment. - C. Collaborators are automatically added to the Azure authoring resource:
This is incorrect. Collaborators (coauthors) are not automatically added to the new Azure authoring resource. They must be manually added to the new resource by the owner or administrator of the Azure resource. - D. If you are a collaborator, the application will migrate automatically:
This is incorrect. Collaborators (coauthors) do not have the same privileges as the owner. They cannot initiate or automatically migrate the application. Only the owner can migrate the application, and collaborators will need to be manually added to the new resource after migration.
Additional Information:
LUIS’s migration to Azure resources involves changes in how access and authentication are managed. The Azure-based authoring resource brings additional features and integration options for users of LUIS. For more detailed steps and to understand how to handle the migration process, consult the official LUIS migration documentation.
Domain: Implementing Generative AI Solutions
Question 7: Creating Engaging Chatbot Interactions
In a chatbot application, you want to use multimedia attachments to enhance user interaction. The first interaction uses a basic card with an image, a button, and text. The second interaction uses a mix of text, images, and input fields. Which cards should you use?
- Interaction 1 -> Hero Card; Interaction 2 -> Adaptive Card
B. Interaction 1 -> Thumbnail Card; Interaction 2 -> Adaptive Card
C. Interaction 1 -> Adaptive Card; Interaction 2 -> Thumbnail Card
D. Interaction 1 -> Adaptive Card; Interaction 2 -> Hero Card
Correct Answer: A
Explanation:
- Hero Card is best suited for a large image, button, and text.
- Adaptive Card is ideal for rendering a rich, interactive experience with text, images, and inputs.
For more details on responding with cards, see the official documentation.
Domain: Implementing Natural Language Processing Solutions
Question 8: Prebuilt Entity Type in LUIS
You are working with LUIS to recognize entities in text. In the following JSON snippet, which entity type and subtype should be used for the recognized entity “last week”?
Correct Answers: A and B
Explanation:
- A: The entity type should be DateTime as it refers to a temporal expression.
- B: The subtype should be DateRange, as “last week” refers to a range of dates.
For further details, visit the LUIS documentation on DateTime prebuilt entities.
Domain: Managing Azure AI Solutions
Question 9: Selecting the Right API for Text Extraction from Images
A startup wants to build an app that extracts text from images. Which Azure API should be used?
- Text Moderation API
B. Content Moderator Review Tool
C. Custom Term API
D. Image Moderation API
Correct Answer: D
Explanation:
The Image Moderation API is designed for scanning and moderating images, including extracting text.
Summary
This set of questions helps you prepare for the AI-102 exam by covering key topics like computer vision, natural language processing, and generative AI solutions. Use these questions to assess your knowledge and focus on areas that need more attention. Good luck with your exam preparation!