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 711
You have the Predicted vs. True chart shown in the following exhibit.
Which type of model is the chart used to evaluate?
A. classification
B. regression
C. clustering
Answer
B. regression
Explanation
What is a Predicted vs. True chart? Predicted vs. True shows the relationship between a predicted value and its correlating true value for a regression problem. This graph can be used to measure performance of a model as the closer to the y=x line the predicted values are, the better the accuracy of a predictive model.
Question 712
Which type of machine learning should you use to predict the number of gift cards that will be sold next month?
A. classification
B. regression
C. clustering
Answer
B. regression
Explanation
In the most basic sense, regression refers to prediction of a numeric target. Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable. You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions.
Question 713
You have a dataset that contains information about taxi journeys that occurred during a given period.
You need to train a model to predict the fare of a taxi journey.
What should you use as a feature?
A. the number of taxi journeys in the dataset
B. the trip distance of individual taxi journeys
C. the fare of individual taxi journeys
D. the trip ID of individual taxi journeys
Answer
B. the trip distance of individual taxi journeys
Explanation
The label is the column you want to predict. The identified Features are the inputs you give the model to predict the Label.
Example: The provided data set contains the following columns:
- vendor_id: The ID of the taxi vendor is a feature.
- rate_code: The rate type of the taxi trip is a feature.
- passenger_count: The number of passengers on the trip is a feature.
- trip_time_in_secs: The amount of time the trip took. You want to predict the fare of the trip before the trip is completed. At that moment, you don’t know how long the trip would take. Thus, the trip time is not a feature and you’ll exclude this column from the model.
- trip_distance: The distance of the trip is a feature.
- payment_type: The payment method (cash or credit card) is a feature.
- fare_amount: The total taxi fare paid is the label.
Question 714
You need to predict the sea level in meters for the next 10 years. Which type of machine learning should you use?
A. classification
B. regression
C. clustering
Answer
B. regression
Explanation
In the most basic sense, regression refers to prediction of a numeric target. Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable. You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions.
Question 715
HOTSPOT
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: Automated machine learning is the process of automating the time-consuming, iterative tasks of machine learning model development.
Statement 2: Automated machine learning can automatically infer the training data from the use case provided.
Statement 3: Automated machine learning works by running multiple training iterations that are scored and ranked by the metrics you specify.
Statement 4: Automated machine learning enables you to specify a dataset and will automatically understand which label to predict.
Answer
Statement 1: Automated machine learning is the process of automating the time-consuming, iterative tasks of machine learning model development. Yes
Statement 2: Automated machine learning can automatically infer the training data from the use case provided. No
Statement 3: Automated machine learning works by running multiple training iterations that are scored and ranked by the metrics you specify. Yes
Statement 4: Automated machine learning enables you to specify a dataset and will automatically understand which label to predict. No
Explanation
Box 1: Yes -Automated machine learning, also referred to as automated ML or AutoML, is the process of automating the time consuming, iterative tasks of machine learning model development. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality.
Box 2: No –
Box 3: Yes -During training, Azure Machine Learning creates a number of pipelines in parallel that try different algorithms and parameters for you. The service iterates throughML algorithms paired with feature selections, where each iteration produces a model with a training score. The higher the score, the better the model is considered to “fit” your data. It will stop once it hits the exit criteria defined in the experiment.
Box 4: No -Apply automated ML when you want Azure Machine Learning to train and tune a model for you using the target metric you specify. The label is the column you want to predict.
Question 716
To complete the sentence, select the appropriate option in the answer area.
A banking system that predicts whether a loan will be repaid is an example of the __________ type of machine learning.
A. classification
B. regression
C. clustering
Answer
A. classification
Explanation
Two-class classification provides the answer to simple two-choice questions such as Yes/No or True/False.
Question 717
HOTSPOT -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: Labelling is the process of tagging training data with known values.
Statement 2: You should evaluate a model by using the same data used to train the model.
Statement 3: Accuracy is always the primary metric used to measure a model’s performance.
Answer
Statement 1: Labelling is the process of tagging training data with known values. Yes
Statement 2: You should evaluate a model by using the same data used to train the model. No
Statement 3: Accuracy is always the primary metric used to measure a model’s performance. No
Explanation
Box 1: Yes -In machine learning, if you have labeled data, that means your data is marked up, or annotated, to show the target, which is the answer you want your machine learning model to predict. In general, data labeling can refer to tasks that include data tagging, annotation, classification, moderation, transcription, or processing.
Box 2: No –
Box 3: No -Accuracy is simply the proportion of correctly classified instances. It is usually the first metric you look at when evaluating a classifier. However, when the test data is unbalanced (where most of the instances belong to one of the classes), or you are more interested in the performance on either one of the classes, accuracy doesn’t really capture the effectiveness of a classifier.
Question 718
Which service should you use to extract text, key/value pairs, and table data automatically from scanned documents?
A. Form Recognizer
B. Text Analytics
C. Language Understanding
D. Custom Vision
Answer
A. Form Recognizer
Explanation
Accelerate your business processes by automating information extraction. Form Recognizer applies advanced machine learning to accurately extract text, key/ value pairs, and tables from documents. With just a few samples, Form Recognizer tailors its understanding to your documents, both on-premises and in the cloud. Turn forms into usable data at a fraction of the time and cost, so you can focus more time acting on the information rather than compiling it.
Question 719
To complete the sentence, select the appropriate option in the answer area.
The ability to extract subtotals and totals from a receipt is a capability of the __________ service.
A. Custom Vision
B. Form Recognizer
C. Ink Recognizer
D. Text Analytics
Answer
B. Form Recognizer
Explanation
Accelerate your business processes by automating information extraction. Form Recognizer applies advanced machine learning to accurately extract text, key/ value pairs, and tables from documents. With just a few samples, Form Recognizer tailors its understanding to your documents, both on-premises and in the cloud. Turn forms into usable data at a fraction of the time and cost, so you can focus more time acting on the information rather than compiling it.
Question 720
You use Azure Machine Learning designer to publish an inference pipeline.
Which two parameters should you use to access the web service? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. the model name
B. the training endpoint
C. the authentication key
D. the REST endpoint
Answer
C. the authentication key
D. the REST endpoint
Explanation
You can consume a published pipeline in the Published pipelines page. Select a published pipeline and find the REST endpoint of it.
To consume the pipeline, you need:
- The REST endpoint for your service
- The Primary Key for your service