Microsoft  AI-900 Exam Dumps & Practice Test Questions

Question 1:

A company has a customer support team that assists clients through phone calls and emails. The company introduces a webchat bot to automatically respond to frequently asked questions. 

What is the primary business advantage the company can expect by implementing this webchat bot?

A. Higher sales revenue
B. Decreased workload for customer service representatives
C. Enhanced product reliability

Correct Answer: B

Explanation:

Implementing a webchat bot is primarily aimed at automating responses to common customer inquiries. This automation enables the company to manage a higher volume of requests without increasing the burden on its human customer service agents. By handling repetitive questions efficiently, the bot frees up the support team to focus on more complex or nuanced issues that require human intervention. This leads directly to a reduction in the workload of customer service representatives.

To clarify why the other options are less appropriate:

• A. Higher sales revenue: While the presence of a webchat bot can improve the customer experience by providing quick answers, it doesn’t directly result in increased sales. Sales growth generally depends on marketing strategies, product quality, or sales team efforts rather than support automation. The bot may contribute indirectly to customer satisfaction but isn’t a direct sales driver.

• C. Enhanced product reliability: Product reliability relates to how well the product functions over time, influenced by design, manufacturing quality, and testing. The webchat bot improves service efficiency but does not affect the product’s technical reliability or durability.

In summary, the core benefit of deploying a webchat bot lies in reducing the workload on customer service agents. This automation improves operational efficiency by allowing the team to focus on complex support cases, thereby improving overall service quality. Hence, option B is the most accurate answer.

Question 2:

When preparing data for a machine learning model, what is the correct way to divide the dataset into training and evaluation sets?

A. Use features for training and labels for evaluation
B. Randomly split the dataset’s rows into training and evaluation subsets
C. Use labels for training and features for evaluation
D. Randomly split the dataset’s columns into training and evaluation subsets

Correct Answer: B

Explanation:

In machine learning, it is crucial to train models on a portion of the data and then evaluate their performance on unseen data to ensure the model generalizes well. The dataset usually consists of features (input variables) and labels (target variables). The standard approach is to split the dataset by rows—meaning the individual data points or samples—into distinct training and evaluation sets.

Here’s why each option is evaluated as such:

• A. Use features for training and labels for evaluation: This is incorrect because both features and labels are used during training. The model learns the relationship between features and labels. During evaluation, predictions made from features are compared to the actual labels to measure accuracy, but labels themselves are not used as input during training or evaluation exclusively.

• B. Randomly split the dataset’s rows into training and evaluation subsets: This is the correct method. Randomly dividing data samples into training and evaluation sets (for example, 70% for training, 30% for testing) helps ensure that both sets are representative of the overall data distribution. This method allows for unbiased evaluation of model performance.

• C. Use labels for training and features for evaluation: This is not valid. Labels are the outcomes the model aims to predict and thus are never used as training inputs. Features are the inputs used for both training and evaluation.

• D. Randomly split the dataset’s columns into training and evaluation subsets: Splitting by columns breaks the relationship between features and labels and disrupts the integrity of data samples. This approach is invalid since columns represent variables, not individual data points.

In conclusion, the best practice is to randomly split rows of data into training and evaluation sets, ensuring the model learns from a representative sample and can be properly assessed on unseen data. Therefore, option B is the correct answer.

Question 3:

You have created a machine learning model using the Automated Machine Learning (AutoML) user interface. 

To comply with Microsoft’s transparency principle in responsible AI, what step should you take?

A. Set Validation type to Auto
B. Enable Explain best model
C. Set Primary metric to accuracy
D. Set Max concurrent iterations to 0

Correct Answer: B

Explanation:

Microsoft’s transparency principle for responsible AI stresses that AI systems must be understandable and interpretable by users. Transparency ensures that decisions made by AI models can be explained, promoting trust and accountability.

Let’s examine the options:
A. Set Validation type to Auto: This setting controls how the model validates its predictions, such as through cross-validation or holdout methods. While validation is important for assessing model performance, it does not improve the model’s transparency or explainability.

B. Enable Explain best model: This option directly supports the transparency principle. By enabling explanations for the best performing model, you can generate insights into how the model makes decisions. It typically highlights feature importance, model behavior, and decision pathways, which are essential for interpreting and trusting the model’s outputs. This aligns perfectly with responsible AI practices, making the AI’s decisions clearer to stakeholders.

C. Set Primary metric to accuracy: Choosing accuracy as a metric helps evaluate the model’s correctness but does not provide insight into why the model makes certain predictions. Accuracy alone does not contribute to transparency or explainability.

D. Set Max concurrent iterations to 0: This parameter controls the number of training iterations running simultaneously to manage computational resources. It relates to performance optimization rather than transparency or interpretability.

In conclusion, to meet Microsoft’s transparency principle in responsible AI, the best practice is to enable the feature that explains how the best model works. This approach ensures that model decisions are interpretable and helps users understand the AI system’s behavior. Therefore, B is the correct answer.

Question 4:

You are developing an AI system intended to empower all users, including those with hearing, vision, and other disabilities. Which Microsoft responsible AI principle does this represent?

A. Fairness
B. Inclusiveness
C. Reliability and safety
D. Accountability

Correct Answer: B

Explanation:

Microsoft’s responsible AI principles guide the ethical design and deployment of AI systems to ensure they benefit everyone while minimizing harm. Key principles include fairness, inclusiveness, reliability and safety, and accountability.

The scenario involves designing an AI system that supports users with various disabilities, such as hearing or visual impairments. This approach aims to ensure the technology is accessible and usable by people with diverse abilities.

Inclusiveness is the principle focused on creating AI solutions that empower all individuals, including those with disabilities or from underrepresented groups. It ensures that AI is designed to be accessible and beneficial to a broad spectrum of users, addressing barriers caused by physical or cognitive impairments. By prioritizing inclusiveness, AI systems work toward equity in access and usability.

Other principles in context:
Fairness ensures AI treats all users equitably, avoiding bias and discrimination. While fairness is important, the core focus here is on accessibility for people with disabilities, which falls under inclusiveness.

Reliability and safety refer to building AI that performs consistently and avoids harm. Although essential, this principle is more about dependable functioning than empowering users with impairments.

Accountability means developers and organizations are responsible for the outcomes of AI systems, ensuring oversight and governance. While critical, it does not directly address the design focus on accessibility.

Therefore, the best match to the scenario is inclusiveness, as it reflects the commitment to empower everyone, including those with impairments. Thus, the correct answer is B.

Question 5:

You have been assigned to develop an AI system for a client that needs to categorize images into classes such as animals, vehicles, and people. 

Which Azure service should you choose to create this image classification model?

A Azure Cognitive Services - Computer Vision
B Azure Machine Learning
C Azure Cognitive Services - Face API
D Azure Bot Services

Correct Answer: A

Explanation:

When building an AI solution for image classification, selecting the right Azure service is essential. In this case, the goal is to classify images into multiple categories like animals, vehicles, and people. Let’s analyze why Azure Cognitive Services - Computer Vision is the best option.

Azure Computer Vision is a specialized service designed for image analysis. It offers pre-trained models capable of identifying and classifying a wide range of objects within images. These models are built on deep learning techniques and have been trained on large datasets, making them highly accurate for common image recognition tasks. Additionally, Computer Vision supports custom training through the Custom Vision feature, allowing you to tailor the model to your specific categories, such as different types of animals or vehicles.

In contrast, Azure Machine Learning is a more general-purpose platform for creating and deploying machine learning models. While powerful, it requires more expertise and effort to build and train custom image classification models from scratch. It’s suitable for experienced data scientists but less convenient for quick, out-of-the-box solutions.

Azure Cognitive Services - Face API is specialized in detecting and recognizing human faces, not general object classification. This makes it unsuitable for categorizing non-human subjects like animals or vehicles.

Lastly, Azure Bot Services are focused on building conversational agents, not image processing or classification, so they are irrelevant to this use case.

In summary, Azure Cognitive Services - Computer Vision is purpose-built for image recognition and classification tasks, offers ready-to-use models, and supports customization, making it the most practical and effective choice for this project.

Question 6:

Which Azure service is primarily designed to enable users to build and deploy machine learning models with minimal or no coding?

A Azure Cognitive Services
B Azure Machine Learning
C Azure Bot Service
D Azure Functions

Correct Answer: B

Explanation:

Understanding Azure’s AI services is critical, especially for those preparing for certifications like AI-900. The question asks which service allows the creation and deployment of machine learning models with minimal coding.

Azure Machine Learning stands out as the correct choice. It is a cloud-based platform that supports the entire machine learning lifecycle—from data preparation and model training to deployment and monitoring. Azure Machine Learning offers tools suitable for both developers and non-experts, including a drag-and-drop interface known as Azure Machine Learning Studio. This visual environment makes it accessible to users with limited programming skills, allowing them to create and deploy models without writing extensive code.

On the other hand, Azure Cognitive Services provides pre-built AI capabilities via APIs for vision, speech, language, and decision-making. These services are easy to integrate but do not offer tools to build custom models. Instead, they supply ready-to-use models for common AI tasks.

Azure Bot Service is designed to develop conversational chatbots and virtual assistants. While bots can incorporate AI models, the service itself is not for building or training machine learning models.

Azure Functions offers serverless computing to execute small pieces of code triggered by events. Although it can run code related to AI inference or data processing, it is not a dedicated platform for building or managing machine learning workflows.

In summary, Azure Machine Learning provides a comprehensive and user-friendly platform for creating and deploying custom machine learning models with little to no coding, distinguishing it as the best fit for this requirement.

Question 7:

What is a major advantage of leveraging Azure Cognitive Services when developing AI applications?

A. It enables the creation of custom AI models without requiring any programming skills.
B. It mandates manual management of infrastructure for operating AI models.
C. It provides pre-built AI models for common tasks such as speech recognition, sentiment analysis, and computer vision.
D. It only offers tools for building speech-related AI models.

Answer: C

Explanation:

Azure Cognitive Services is a collection of ready-to-use AI APIs designed to simplify the integration of artificial intelligence into applications. One of its biggest advantages is offering pre-built models that address widely used AI functions without the need for building models from scratch. These functions include tasks like speech recognition, sentiment analysis, computer vision, language understanding, and more.

Choosing Option C is correct because Azure Cognitive Services allows developers to quickly add sophisticated AI features by calling APIs that Microsoft has already trained and optimized. This greatly reduces the time and expertise required to embed AI functionalities in software solutions. For example, instead of creating a speech-to-text model from zero, developers can use Cognitive Services’ speech recognition API, enabling rapid development cycles.

Option A is somewhat misleading because, while Cognitive Services reduce the complexity of AI, they still require some coding knowledge to integrate APIs into applications. It’s not a no-code platform, but rather a low-code AI enhancement.

Option B is incorrect since Azure Cognitive Services is a fully managed cloud service. Microsoft handles the backend infrastructure, scaling, and maintenance, so developers can focus on application logic rather than server management.

Option D is too narrow—Azure Cognitive Services includes speech, but also extensive capabilities in vision, language, decision-making, and more.

In summary, the main strength of Azure Cognitive Services is its pre-built, scalable AI solutions that address common challenges like image recognition or sentiment detection, making it accessible and practical for a wide range of developers.

Question 8:

Which Azure service is best suited for creating, training, and deploying custom machine learning models, especially for advanced or highly specialized AI tasks?

A. Azure Cognitive Services
B. Azure Bot Services
C. Azure Machine Learning
D. Azure Speech Services

Answer: C

Explanation:

Azure Machine Learning is a powerful, end-to-end cloud platform designed for data scientists and developers to build, train, and deploy custom machine learning models. This service excels when AI workloads require specialized models tailored to unique datasets or business problems, unlike pre-packaged AI APIs.

Option C is the best choice because Azure Machine Learning offers comprehensive tools that cover the entire machine learning lifecycle—from data preparation and model experimentation to hyperparameter tuning and model deployment. It supports a wide range of frameworks and languages, including Python and R, and integrates well with popular ML libraries such as TensorFlow and PyTorch.

In contrast, Azure Cognitive Services (A) delivers pre-built AI functionalities via APIs but does not support training custom models. It’s excellent for quickly adding AI features but lacks the flexibility needed for bespoke model development.

Azure Bot Services (B) is specialized for building conversational agents or chatbots. Although bots might incorporate AI, this service focuses on dialog management and bot hosting, not on training or deploying custom machine learning models.

Azure Speech Services (D) offers specific capabilities related to speech processing, such as speech-to-text, text-to-speech, and translation, but it does not provide tools for general machine learning model development.

By using Azure Machine Learning, organizations gain the ability to customize models extensively, optimize their performance, and automate deployment workflows. This makes it the go-to service for advanced AI projects requiring tailored machine learning solutions.

Question 9:

Which Azure service should you use to implement automatic, real-time translation of text from one language to another within your application?

A) Azure Cognitive Services - Text Analytics
B) Azure Cognitive Services - Translator
C) Azure Cognitive Services - Custom Vision
D) Azure Machine Learning

Correct Answer: B

Explanation:

For building an application that requires real-time translation of text between multiple languages, the optimal choice is Azure Cognitive Services - Translator. This service is specifically designed to translate text efficiently and accurately, enabling applications to handle multilingual content seamlessly.

The Translator service offers a comprehensive set of features for text translation, including real-time translation, batch translation for bulk content, and language detection. It supports over 70 languages and dialects, making it highly versatile for global applications. This makes it an ideal tool for scenarios such as customer support chatbots, user-generated content translation, and multilingual website content.

On the other hand, Azure Cognitive Services - Text Analytics provides useful capabilities like sentiment analysis, key phrase extraction, and language detection but does not offer translation services. Although it can identify the language of the input text, it cannot convert that text into another language, which is a key requirement here.

The Custom Vision service focuses on image classification and object detection rather than text processing. It allows developers to create custom models to identify images but has no functionality for text translation.

Azure Machine Learning is a powerful platform for creating custom machine learning models. While theoretically, you could build a custom translation model here, it involves a significant investment of time and expertise. It does not provide an out-of-the-box translation feature like the Translator service. For most real-time translation needs, Translator is the more practical and efficient choice.

In summary, for automatic, real-time translation integrated into your application, Azure Cognitive Services - Translator is the most suitable and purpose-built service.

Question 10:

Which Azure service is best suited for developing a custom image classification model that can identify and categorize different types of vehicles like cars, trucks, and motorcycles?

A) Azure Cognitive Services - Custom Vision
B) Azure Machine Learning
C) Azure Cognitive Services - Face API
D) Azure Cognitive Services - Speech API

Correct Answer: A

Explanation:

When the goal is to build a custom image classification system to recognize various vehicle types such as cars, trucks, and motorcycles, Azure Cognitive Services - Custom Vision stands out as the most appropriate choice. This service is tailored for users who want to quickly create and deploy image classification or object detection models without deep expertise in machine learning.

Custom Vision simplifies the model-building process by allowing users to upload labeled images, train a model on that data, and then use the trained model to classify new images. It supports iterative training, meaning you can improve accuracy over time by adding more images and retraining. It also provides an easy-to-use web interface and APIs, making it accessible for developers and business users alike.

While Azure Machine Learning is a highly flexible and powerful toolset for developing custom machine learning solutions, including image classification, it requires more advanced skills. It involves manual data preparation, model selection, training, and deployment, making it more complex and time-consuming compared to Custom Vision.

The Face API is specialized for detecting and recognizing human faces, including attributes like age, gender, and emotions. It is not designed for classifying general objects or vehicles.

Similarly, the Speech API deals exclusively with audio and speech-to-text processing, having no functionality for image or object recognition.

In conclusion, for a straightforward, efficient approach to building a vehicle image classification model, Azure Cognitive Services - Custom Vision offers the best combination of ease of use, functionality, and speed, making it the ideal choice for this scenario.