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 581
What are two metrics that you can use to evaluate a regression model?
A. coefficient of determination (R2)
B. F1 score
C. root mean squared error (RMSE)
D. area under curve (AUC)
E. balanced accuracy
Answer
A. coefficient of determination (R2)
C. root mean squared error (RMSE)
Explanation
A: R-squared (R2), or Coefficient of determination represents the predictive power of the model as a value between -inf and 1.00. 1.00 means there is a perfect fit, and the fit can be arbitrarily poor so the scores can be negative.
C: RMS-loss or Root Mean Squared Error (RMSE) (also called Root Mean Square Deviation, RMSD), measures the difference between values predicted by a model and the values observed from the environment that is being modeled.
Incorrect Answers:
B: F1 score also known as balanced F-score or F-measure is used to evaluate a classification model.
D: aucROC or area under the curve (AUC) is used to evaluate a classification model.
Question 582
You need to use Azure Machine Learning designer to build a model that will predict automobile prices.
Which type of modules should you use to complete the model?
Answer
Explanation
box 1: Select Columns in Dataset
For Columns to be cleaned, choose the columns that contain the missing values you want to change. You can choose multiple columns, but you must use the same replacement method in all selected columns.
Example:
box 2: Split data
Splitting data is a common task in machine learning. You will split your data into two separate datasets. One dataset will train the model and the other will test how well the model performed.
box 3: Linear regression
Because you want to predict price, which is a number, you can use a regression algorithm. For this example, you use a linear regression model.
Question 583
__________ models can be used to predict the sale price of auctioned items.
A. Classification
B. Clustering
C. Regression
Answer
C. Regression
Explanation
Regression is a machine learning task that is used to predict the value of the label from a set of related features.
Question 584
Which metric can you use to evaluate a classification model?
A. true positive rate
B. mean absolute error (MAE)
C. coefficient of determination (R2)
D. root mean squared error (RMSE)
Answer
A. true positive rate
Explanation
What does a good model look like?
An ROC curve that approaches the top left corner with 100% true positive rate and 0% false positive rate will be the best model. A random model would display as a flat line from the bottom left to the top right corner. Worse than random would dip below the y=x line.
Question 585
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 586
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
Statement 1: When creating an object detection model in the Custom Vision service, you must choose a classification type of either Multilabel or Multiclass.
Statement 2: You can create an object detection model in the Custom Vision service to find the location of content within an image.
Statement 3: When creating an object detection model in the Custom Vision service, you can select from a set of predefined domains.
Answer
Statement 1: When creating an object detection model in the Custom Vision service, you must choose a classification type of either Multilabel or Multiclass. No
Statement 2: You can create an object detection model in the Custom Vision service to find the location of content within an image. Yes
Statement 3: When creating an object detection model in the Custom Vision service, you can select from a set of predefined domains. Yes
Question 587
Match the types of machine learning to the appropriate scenarios. Each machine learning type may be used once, more than once, or not at all.
Machine Learning Types:
- Facial detection
- Facial recognition
- Image classification
- Object detection
- Optical character recognition (OCR)
- Semantic segmentation
Scenarios:
- Separate images of polar bears and brown bears.
- Determine the location of a bear in a photo.
- Determine which pixels in an image are part of a bear.
Answer
- Image classification: Separate images of polar bears and brown bears.
- Object detection: Determine the location of a bear in a photo.
- Semantic segmentation: Determine which pixels in an image are part of a bear.
Explanation
<box 1>: Image classification
Image classification is a supervised learning problem: define a set of target classes (objects to identify in images), and train a model to recognize them using labeled example photos.
<box 2>: Object detection
Object detection is a computer vision problem. While closely related to image classification, object detection performs image classification at a more granular scale. Object detection both locates and categorizes entities within images.
<box 3>: Semantic Segmentation
Semantic segmentation achieves fine-grained inference by making dense predictions inferring labels for every pixel, so that each pixel is labeled with the class of its enclosing object ore region.
Question 588
Match the facial recognition tasks to the appropriate questions. Each task may be used once, more than once, or not at all.
Tasks:
- grouping
- identification
- similarity
- verification
Questions:
- Do two images of a face belong to the same person?
- Does this person look like other people?
- Do all the faces belong together?
- Who is this person in this group of people?
Answer
- verification: Do two images of a face belong to the same person?
- similarity: Does this person look like other people?
- grouping: Do all the faces belong together?
- identification: Who is this person in this group of people?
Explanation
<box 1>: verification
Face verification: Check the likelihood that two faces belong to the same person and receive a confidence score.
<box 2>: similarity
<box 3>: grouping
<box 4>: identification
Face detection: Detect one or more human faces along with attributes such as: age, emotion, pose, smile, and facial hair, including 27 landmarks for each face in the image.
Question 589
You need to make the written press releases of your company available in a range of languages.
Which service should you use?
A. Translator
B. Text Analytics
C. Speech
D. Language Understanding (LUIS)
Answer
A. Translator
Explanation
Translator is a cloud-based machine translation service you can use to translate text in near real-time through a simple REST API call. The service uses modern neural machine translation technology and offers statistical machine translation technology. Custom Translator is an extension of Translator, which allows you to build neural translation systems.
Question 590
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
Statement 1: The Text Analytics service can identify in which language text is written.
Statement 2: The Text Analytics service can detect handwritten signatures in a document.
Statement 3: The Text Analytics can identify companies and organizations mentioned in a document.
Answer
Statement 1: The Text Analytics service can identify in which language text is written. Yes
Statement 2: The Text Analytics service can detect handwritten signatures in a document. No
Statement 3: The Text Analytics can identify companies and organizations mentioned in a document. Yes
Explanation
The Text Analytics API is a cloud-based service that provides advanced natural language processing over raw text, and includes four main functions: sentiment analysis, key phrase extraction, named entity recognition, and language detection.
Statement 1: Yes
You can detect which language the input text is written in and report a single language code for every document submitted on the request in a wide range of languages, variants, dialects, and some regional/cultural languages. The language code is paired with a score indicating the strength of the score.
Statement 2: No
Statement 3: Yes
Named Entity Recognition: Identify and categorize entities in your text as people, places, organizations, date/time, quantities, percentages, currencies, and more. Well-known entities are also recognized and linked to more information on the web.