The latest Microsoft AI-900 Azure AI Fundamentals certification actual real practice exam question and answer (Q&A) dumps are available free, which are helpful for you to pass the Microsoft AI-900 Azure AI Fundamentals exam and earn Microsoft AI-900 Azure AI Fundamentals certification.
Table of Contents
Question 571
HOTSPOT (Drag & Drop is not supported)
To complete the sentence, select the appropriate option in the answer area.
Ensuring that the numeric variables in training data are on a similar scale is an example of __________.
A. data ingestion.
B. feature engineering.
C. feature selection.
D. model training.
Answer
C. feature selection.
Question 572
HOTSPOT (Drag & Drop is not supported)
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Statement 1: A validation set includes the set of input examples that will be used to train a mode.
Statement 2: A validation set can be used to determine how well a model predicts labels.
Statement 3: A validation set can used to verify that all the training data was used to train the model.
Answer
Statement 1: A validation set includes the set of input examples that will be used to train a mode. No
Statement 2: A validation set can be used to determine how well a model predicts labels. Yes
Statement 3: A validation set can used to verify that all the training data was used to train the model. No
Explanation
Box 1: No
The validation dataset is different from the test dataset that is held back from the training of the model.
Box 2: Yes
A validation dataset is a sample of data that is used to give an estimate of model skill while
tuning model’s hyperparameters.
Box 3: No
The Test Dataset, not the validation set, used for this. The Test Dataset is a sample of data used to provide an unbiased evaluation of a final model fit on the training dataset.
Question 573
HOTSPOT (Drag & Drop is not supported)
To complete the sentence, select the appropriate option in the answer area.
Ensuring an AI system does not provide a prediction when important fields contain unusual or missing values is __________ principle for responsible AI.
A. an inclusiveness
B. a privacy and security
C. a reliability and safety
D. a transparency
Answer
C. a reliability and safety
Question 574
HOTSPOT (Drag & Drop is not supported)
To complete the sentence, select the appropriate option in the answer area.
Azure Machine Learning designer lets you create machine learning models by
A. adding and connecting modules on a visual canvas.
B. automatically performing common data preparation tasks.
C. automatically selecting and algorithm to build the most accurate model.
D. using a code-first notebook experience.
Answer
A. adding and connecting modules on a visual canvas.
Question 575
HOTSPOT (Drag & Drop is not supported)
To complete the sentence, select the appropriate option in the answer area.
__________ is the calculated probability of a correct image classification.
A. Accuracy
B. Confidence
C. Root Mean Square Error
D. Sentiment
Answer
A. Accuracy
Question 576
Match the principles of responsible AI to appropriate requirements. Each principle may be used once, more than once, or not at all.
Principles:
- Fairness
- Privacy and security
- Reliability and safety
- Transparency
Requirements:
- The system must not discriminate based on gender, race
- Personal data must be visible only to approve
- Automated decision-making processes must be recorded so that approved users can identify why a decision was made
Answer
- Fairness: The system must not discriminate based on gender, race
- Reliability and safety: Personal data must be visible only to approve
- Transparency: Automated decision-making processes must be recorded so that approved users can identify why a decision was made
Question 577
From Azure Machine Learning designer, to deploy a real-time inference pipeline as a service for others to consume, you must deploy the model to __________.
A. a local web service.
B. Azure Container Instances.
C. Azure Kubernetes Service (AKS).
D. Azure Machine Learning compute.
Answer
C. Azure Kubernetes Service (AKS).
Explanation
To perform real-time inferencing, you must deploy a pipeline as a real-time endpoint.
Real-time endpoints must be deployed to an Azure Kubernetes Service cluster.
Question 578
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
Statement 1: Automated machine learning provides you with the ability to include custom Python scripts in a training pipeline.
Statement 2: Automated machine learning implements machine learning solutions without the need for programming experience.
Statement 3: Automated machine learning provides you with the ability to visually connect datasets and modules on an interactive canvas.
Answer
Statement 1: Automated machine learning provides you with the ability to include custom Python scripts in a training pipeline. Yes
Statement 2: Automated machine learning implements machine learning solutions without the need for programming experience. Yes
Statement 3: Automated machine learning provides you with the ability to visually connect datasets and modules on an interactive canvas. Yes
Question 579
You are building a tool that will process images from retail stores and identify the products of competitors.
The solution will use a custom model.
Which Azure Cognitive Services service should you use?
A. Custom Vision
B. Form Recognizer
C. Face
D. Computer Vision
Answer
A. Custom Vision
Question 580
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
Statement 1: A validation set includes the set of input examples that will be used to train a model.
Statement 2: A validation set can be used to determine how well a model predicts labels.
Statement 3: A validation set can be used to verify that all the training data was used to train the model.
Answer
Statement 1: A validation set includes the set of input examples that will be used to train a model. No
Statement 2: A validation set can be used to determine how well a model predicts labels. Yes
Statement 3: A validation set can be used to verify that all the training data was used to train the model. No
Explanation
Statement 1: A validation set includes the set of input examples that will be used to train a model: No
The validation dataset is different from the test dataset that is held back from the training of the model.
Statement 2: A validation set can be used to determine how well a model predicts labels: Yes
A validation dataset is a sample of data that is used to give an estimate of model skill while tuning model’s hyperparameters.
Statement 3: A validation set can be used to verify that all the training data was used to train the model: No
The Test Dataset, not the validation set, used for this. The Test Dataset is a sample of data used to provide an unbiased evaluation of a final model fit on the training dataset.