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 671
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 672
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 673
Assigning classes to images before training a classification model is an example of __________.
A. evaluation
B. feature engineering
C. hyperparameter tuning
D. labeling
Answer
D. labeling
Question 674
You have an Azure Machine Learning model that predicts product quality. The model has a training dataset that contains 50,000 records. A sample of the data is shown in the following table.
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: Mass (kg) is a feature.
Statement 2: Quality Test is a label.
Statement 3L Temperature (C) is a label.
Answer
Statement 1: Mass (kg) is a feature. Yes
Statement 2: Quality Test is a label. Yes
Statement 3L Temperature (C) is a label. No
Explanation
Yes:
- Mass (kg) is a feature
- Quality Test is a label
No:
- Temperature (C) is a label
Question 675
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: You train a regression model by using unlabeled data.
Statement 2: The classification technique is used to predict sequential numerical data over time.
Statement 3: Grouping items by their common characteristics is an example of clustering.
Answer
Statement 1: You train a regression model by using unlabeled data. No
Statement 2: The classification technique is used to predict sequential numerical data over time. No
Statement 3: Grouping items by their common characteristics is an example of clustering. Yes
Question 676
Which two actions are performed during the data ingestion and data preparation stage of an Azure Machine Learning process? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. Calculate the accuracy of the model.
B. Score test data by using the model.
C. Combine multiple datasets.
D. Use the model for real-time predictions.
E. Remove records that have missing values.
Answer
C. Combine multiple datasets.
E. Remove records that have missing values.
Question 677
You need to predict the animal population of an area. Which Azure Machine Learning type should you use?
A. regression
B. clustering
C. classification
Answer
A. regression
Explanation
Regression is a supervised machine learning technique used to predict numeric values.
Question 678
Which two languages can you use to write custom code for Azure Machine Learning designer? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
A. Python
B. R
C. C#
D. Scala
Answer
A. Python
B. R
Explanation
Use Azure Machine Learning designer for customizing using Python and R code
Question 679
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: For a regression model, labels must be numeric.
Statement 2: For a clustering model, labels must be used.
Statement 3: For a classification model, labels must be numeric.
Answer
Statement 1: For a regression model, labels must be numeric. Yes
Statement 2: For a clustering model, labels must be used. No
Statement 3: For a classification model, labels must be numeric. No
Explanation
Box 1: Yes -For regression problems, the label column must contain numeric data that represents the response variable. Ideally the numeric data represents a continuous scale.
Box 2: No -K-Means Clustering -Because the K-means algorithm is an unsupervised learning method, a label column is optional. If your data includes a label, you can use the label values to guide selection of the clusters and optimize the model. If your data has no label, the algorithm creates clusters representing possible categories, based solely on the data.
Box 3: No -For classification problems, the label column must contain either categorical values or discrete values. Some examples might be a yes/no rating, a disease classification code or name, or an income group. If you pick a noncategorical column, the component will return an error during training.
Question 680
Your company wants to build a recycling machine for bottles. The recycling machine must automatically identify bottles of the correct shape and reject all other items.
Which type of AI workload should the company use?
A. anomaly detection
B. conversational AI
C. computer vision
D. natural language processing
Answer
C. computer vision
Explanation
Azure’s Computer Vision service gives you access to advanced algorithms that process images and return information based on the visual features you’re interested in. For example, Computer Vision can determine whether an image contains adult content, find specific brands or objects, or find human faces.