Pass Microsoft AI-900 Exam Quickly With ValidBraindumps [Q41-Q58]

Pass Microsoft AI-900 Exam Quickly With ValidBraindumps

Prepare AI-900 Question Answers - AI-900 Exam Dumps

Microsoft AI-900, also known as Microsoft Azure AI Fundamentals, is a certification exam that validates an individual's knowledge and understanding of artificial intelligence and machine learning concepts and their application in cloud-based solutions using Microsoft Azure services. AI-900 exam is designed to help individuals demonstrate their foundational knowledge of AI and its potential uses in business settings. Microsoft Azure AI Fundamentals certification is ideal for individuals who are new to AI and machine learning or want to gain expertise in the latest AI technologies.

NEW QUESTION # 41
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/overview

NEW QUESTION # 42
You need to make the press releases of your company available in a range of languages.
Which service should you use?

A. Speech
B. Translator Text
C. Language Understanding (LUIS)
D. Text Analytics

Answer: B

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.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/translator/

NEW QUESTION # 43
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Reference:
https://machinelearningmastery.com/difference-test-validation-datasets/

NEW QUESTION # 44
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 45
Select the answer that correctly completes the sentence.

Answer:

Explanation:

NEW QUESTION # 46
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/azure/bot-service/bot-builder-tutorial-add-qna

NEW QUESTION # 47
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:

NEW QUESTION # 48
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:

NEW QUESTION # 49
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.

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:

Explanation:
Explanation

Box 1: 11

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).
Box 2: 1,033
FN = False Negative
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance Finding TP is easy. It basically means the value where Predicted and True value is 1 and that is 11 in this case.
False Negative means where true value was 1 but predicted value was 0 and that is 1033 in this case The confusion matrix shows cases where both the predicted and actual values were 1 (known as true positives) at the top left, and cases where both the predicted and the actual values were 0 (true negatives) at the bottom right. The other cells show cases where the predicted and actual values differ (false positives and false negatives).
https://docs.microsoft.com/en-us/learn/modules/create-classification-model-azure-machine-learning-designer/eva

NEW QUESTION # 50
Match the services to the appropriate descriptions.
To answer, drag the appropriate service from the column on the left to its description on the right. Each service may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point

Answer:

Explanation:

NEW QUESTION # 51
What are two metrics that you can use to evaluate a regression model? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A. coefficient of determination (R2)
B. F1 score
C. balanced accuracy
D. area under curve (AUC)
E. root mean squared error (RMSE)

Answer: A,E

Explanation:
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.
Reference:
https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/metrics

NEW QUESTION # 52
To complete the sentence, select the appropriate option in the answer area.

Answer:

Explanation:

Explanation:

Regression is a machine learning task that is used to predict the value of the label from a set of related features.
Reference:
https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/tasks

NEW QUESTION # 53
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 54
Extracting relationships between data from large volumes of unstructured data is an example of which type of Al workload?

A. computer vision
B. anomaly detection
C. natural language processing (NLP)
D. knowledge mining

Answer: D

NEW QUESTION # 55
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Graphical user interface, text, application Description automatically generated

Clustering is a machine learning task that is used to group instances of data into clusters that contain similar characteristics. Clustering can also be used to identify relationships in a dataset Regression is a machine learning task that is used to predict the value of the label from a set of related features.
Reference:
https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/tasks

NEW QUESTION # 56
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation

Box 1: Yes
Azure Machine Learning designer lets you visually connect datasets and modules on an interactive canvas to create machine learning models.
Box 2: Yes
With the designer you can connect the modules to create a pipeline draft.
As you edit a pipeline in the designer, your progress is saved as a pipeline draft.
Box 3: No
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer