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 681
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: When creating an object detection model in the Custom Vision service, you must choose a classification type of either Multilable 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 Multilable 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 682
In which two scenarios can you use the Form Recognizer service? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
A. Extract the invoice number from an invoice.
B. Translate a form from French to English.
C. Find image of product in a catalog.
D. Identify the retailer from a receipt.
Answer
A. Extract the invoice number from an invoice.
D. Identify the retailer from a receipt.
Explanation
A. Extract the invoice number from an invoice.
D. Identify the retailer from a receipt.
Question 683
Select the answer that correctly completes the sentence.
Counting the number of animals in an area based on a video feed is an example of __________.
A. forecasting.
B. computer vision.
C. conversational AI.
D. anomaly detection.
Answer
B. computer vision.
Question 684
You have a database that contains a list of employees and their photos.
You are tagging new photos of the employees.
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: The Face service can be used to perform facial recognition for employees.
Statement 2: The Face service will be more accurate if you provide more sample photos of each employee from different angles.
Statement 3: If an employee is wearing sunglasses, the Face service will always fail to recognize the employee.
Answer
Statement 1: The Face service can be used to perform facial recognition for employees. Yes
Statement 2: The Face service will be more accurate if you provide more sample photos of each employee from different angles. Yes
Statement 3: If an employee is wearing sunglasses, the Face service will always fail to recognize the employee. No
Question 685
You need to develop a mobile app for employees to scan and store their expenses while travelling.
Which type of computer vision should you use?
A. semantic segmentation
B. image classification
C. object detection
D. optical character recognition (OCR)
Answer
D. optical character recognition (OCR)
Explanation
Azure’s Computer Vision API includes Optical Character Recognition (OCR) capabilities that extract printed or handwritten text from images. You can extract text from images, such as photos of license plates or containers with serial numbers, as well as from documents – invoices, bills, financial reports, articles, and more.
Question 686
A company employs a team of customer service agents to provide telephone and email support to customers.
The company develops a webchat bot to provide automated answers to common customer queries.
Which business benefit should the company expect as a result of creating the webchat bot solution?
A. increased sales
B. a reduced workload for the customer service agents
C. improved product reliability
Answer
B. a reduced workload for the customer service agents
Question 687
For a machine learning progress, how should you split data for training and evaluation?
A. Use features for training and labels for evaluation.
B. Randomly split the data into rows for training and rows for evaluation.
C. Use labels for training and features for evaluation.
D. Randomly split the data into columns for training and columns for evaluation.
Answer
B. Randomly split the data into rows for training and rows for evaluation.
Explanation
The Split Data module is particularly useful when you need to separate data into training and testing sets. Use the Split Rows option if you want to divide the data into two parts. You can specify the percentage of data to put in each split, but by default, the data is divided 50-50. You can also randomize the selection of rows in each group, and use stratified sampling.
Question 688
You are developing a model to predict events by using classification. You have a confusion matrix for the model scored on test data as shown in the following exhibit.
There are __________ correctly predicted positives.
A. 5
B. 11
C. 1,033
D. 13,951
There are __________ false negatives.
A. 5
B. 11
C. 1,033
D. 13,951
Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.
Answer
There are 11 correctly predicted positives.
There are 1,033 false negatives.
Explanation
There are 11 correctly predicted positives.
TP = True Positive.
The class labels in the training set can take on only two possible values, which we usually refer to as positive or negative. The positive and negative instances that a classifier predicts correctly are called true positives (TP) and true negatives (TN), respectively. Similarly, the incorrectly classified instances are called false positives (FP) and false negatives (FN).
There are 1,033 false negatives.
FN = False Negative
Question 689
You build a machine learning model by using the automated machine learning user interface (UI).You need to ensure that the model meets the Microsoft transparency principle for responsible AI.
What should you do?
A. Set Validation type to Auto.
B. Enable Explain best model.
C. Set Primary metric to accuracy.
D. Set Max concurrent iterations to 0.
Answer
B. Enable Explain best model.
Explanation
Model Explain Ability. Most businesses run on trust and being able to open the ML “black box” helps build transparency and trust. In heavily regulated industries like healthcare and banking, it is critical to comply with regulations and best practices. One key aspect of this is understanding the relationship between input variables (features) and model output. Knowing both the magnitude and direction of the impact each feature (feature importance) has on the predicted value helps better understand and explain the model. With model explain ability, we enable you to understand feature importance as part of automated ML runs.
Question 690
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: Forecasting housing prices based on historical data is an example of anomaly detection.
Statement 2: Identifying suspicious sign-ins by looking for deviations from usual patterns is an example of anomaly detection.
Statement 3: Predicting whether a patient will develop diabetes based on the patient’s medical history is an example of anomaly detection.
Answer
Statement 1: Forecasting housing prices based on historical data is an example of anomaly detection. No
Statement 2: Identifying suspicious sign-ins by looking for deviations from usual patterns is an example of anomaly detection. Yes
Statement 3: Predicting whether a patient will develop diabetes based on the patient’s medical history is an example of anomaly detection. No
Explanation
Anomaly detection encompasses many important tasks in machine learning: Identifying transactions that are potentially fraudulent. Learning patterns that indicate that a network intrusion has occurred. Finding abnormal clusters of patients. Checking values entered into a system.