Tag: AutoML

AI, AI-900, Artificial Intelligence (AI), Machine Learning (ML), Microsoft Certification

Practice Questions: Describe Capabilities of Automated Machine Learning (AI-900 Exam Prep)

Practice Exam Questions

Question 1

What is the primary purpose of Automated Machine Learning (AutoML) in Azure?

A. To replace data scientists
B. To automatically label data
C. To select and optimize machine learning models
D. To deploy models without evaluation

Correct Answer: C

Explanation:
AutoML automatically selects algorithms and tunes parameters to identify the best-performing model for a given dataset.

Question 2

Which machine learning scenarios are supported by Azure Automated Machine Learning?

A. Clustering only
B. Regression and classification
C. Reinforcement learning
D. Rule-based automation

Correct Answer: B

Explanation:
AutoML supports supervised learning scenarios such as regression and classification, which are core to AI-900.

Question 3

How does AutoML reduce the need for deep machine learning expertise?

A. By eliminating the need for training data
B. By automatically selecting models and hyperparameters
C. By generating business requirements
D. By replacing human oversight

Correct Answer: B

Explanation:
AutoML handles model selection and hyperparameter tuning automatically, reducing manual effort and expertise requirements.

Question 4

Which task is handled automatically by Azure AutoML?

A. Defining business objectives
B. Cleaning poor-quality data
C. Hyperparameter tuning
D. Approving model deployment

Correct Answer: C

Explanation:
AutoML automatically adjusts hyperparameters to improve model performance.

Question 5

A team wants to quickly build a sales forecasting model with minimal manual configuration.
Which Azure capability should they use?

A. Azure Cognitive Services
B. Azure Bot Service
C. Automated Machine Learning
D. Azure Logic Apps

Correct Answer: C

Explanation:
AutoML is designed to quickly build supervised ML models, including time-series forecasting.

Question 6

Which statement about Automated Machine Learning is TRUE?

A. AutoML guarantees perfect model accuracy
B. AutoML removes the need for human review
C. AutoML compares multiple models automatically
D. AutoML works only with unlabeled data

Correct Answer: C

Explanation:
AutoML evaluates and compares multiple models to identify the best-performing option.

Question 7

Which Azure service provides Automated Machine Learning capabilities?

A. Azure Functions
B. Azure Machine Learning
C. Azure App Service
D. Azure Synapse Analytics

Correct Answer: B

Explanation:
Automated Machine Learning is a feature within Azure Machine Learning.

Question 8

What is a key benefit of using AutoML?

A. Manual feature engineering
B. Faster model development
C. Elimination of data preparation
D. Guaranteed regulatory compliance

Correct Answer: B

Explanation:
AutoML speeds up model development by automating model selection, tuning, and evaluation.

Question 9

Which of the following is NOT a capability of Automated Machine Learning?

A. Automatic model evaluation
B. Automatic algorithm selection
C. Automatic business decision-making
D. Hyperparameter tuning

Correct Answer: C

Explanation:
AutoML supports model creation and evaluation but does not make business decisions.

Question 10

Why is Automated Machine Learning especially useful for beginners?

A. It removes the need for labeled data
B. It eliminates model deployment steps
C. It simplifies model creation and experimentation
D. It replaces Azure Machine Learning

Correct Answer: C

Explanation:
AutoML simplifies experimentation by automating many steps involved in building machine learning models.

Exam Strategy Tip

On AI-900, think of AutoML as a productivity accelerator:

  • You provide the data and goal
  • AutoML handles model selection, tuning, and evaluation
  • Humans still review and deploy the model

If a question mentions automatic selection, minimal configuration, or quick model building, the answer is might be related to Automated Machine Learning.

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AI, AI-900, Artificial Intelligence (AI), Machine Learning (ML), Microsoft Certification

Describe Capabilities of Automated Machine Learning (AI-900 Exam Prep)

This section of the AI-900: Microsoft Azure AI Fundamentals exam focuses on understanding what Automated Machine Learning (AutoML) is and what it can do within Azure Machine Learning. The emphasis is on recognizing capabilities and use cases, not on configuring pipelines or writing code.

This topic appears under: Describe fundamental principles of machine learning on Azure (15–20%) → Describe Azure Machine Learning capabilities

What Is Automated Machine Learning?

Automated Machine Learning (AutoML) is a capability in Azure Machine Learning that automatically selects the best machine learning model and tuning settings for a given dataset and problem.

AutoML helps users:

  • Build machine learning models faster
  • Reduce the need for deep data science expertise
  • Focus on business problems rather than algorithms

For AI-900, you only need to understand what AutoML does, not how to implement it.

Problems AutoML Can Solve

Automated Machine Learning in Azure supports common supervised learning scenarios:

  • Regression – Predicting numeric values (for example, sales forecasts)
  • Classification – Predicting categories or classes (for example, fraud detection)
  • Time-series forecasting – Predicting values over time (for example, demand prediction)

AutoML does not focus on unsupervised learning scenarios such as clustering for AI-900.

Key Capabilities of Automated Machine Learning

Automatic Model Selection

AutoML automatically:

  • Tries multiple machine learning algorithms
  • Compares model performance
  • Selects the best-performing model based on evaluation metrics

Users do not need to manually choose algorithms.

Automated Hyperparameter Tuning

AutoML automatically adjusts hyperparameters to improve model performance, such as:

  • Learning rate
  • Number of trees
  • Regularization settings

This removes the need for manual trial-and-error tuning.

Built-in Feature Engineering

AutoML can automatically create and transform features, including:

  • Normalizing numeric data
  • Encoding categorical values
  • Handling missing values

This simplifies data preparation for machine learning.

Model Evaluation and Comparison

AutoML evaluates models using validation data and metrics such as:

  • Accuracy
  • Precision and recall
  • Mean absolute error

It then ranks models so users can easily compare results.

Integration with Azure Machine Learning

AutoML is fully integrated into Azure Machine Learning, allowing users to:

  • Track experiments
  • View model performance
  • Deploy selected models

This integration supports repeatable and responsible ML workflows.

Example Scenario

A retail company wants to predict monthly product sales but does not have a data science team.

Using Automated Machine Learning:

  • The company provides historical sales data
  • AutoML tests multiple regression models
  • The best-performing model is automatically selected

This allows faster model creation with minimal manual effort.

What AutoML Does NOT Do (Exam-Relevant)

It is important to recognize AutoML limitations for AI-900:

  • It does not eliminate the need for quality data
  • It does not automatically define business goals
  • It does not replace human oversight

AutoML assists model creation but does not remove responsibility from users.

Azure Context for AI-900

In Azure Machine Learning, AutoML:

  • Simplifies model creation
  • Supports beginners and non-experts
  • Accelerates experimentation and deployment

AI-900 questions often focus on why AutoML is useful rather than how it works internally.

Exam Tips for AI-900

  • If the question mentions automatic model selection or tuning, think AutoML
  • AutoML is best for quickly building supervised ML models
  • Remember: AutoML helps choose models, but humans still provide data and goals

Key Takeaways

  • Automated Machine Learning automates model selection, tuning, and evaluation
  • It supports regression, classification, and forecasting scenarios
  • AutoML reduces the need for deep ML expertise
  • Understanding its capabilities is essential for AI-900

This topic connects directly to Azure Machine Learning services and helps bridge core ML concepts with real-world Azure AI capabilities.

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