Latest Lead4Pass Fundamentals AI-900 exam practice questions and AI-900 exam dumps

Lead4Pass Fundamentals AI-900 Exams "Microsoft Azure AI Fundamentals".


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QUESTION 1

DRAG DROP You plan to apply Text Analytics API features to a technical support ticketing system. Match the Text Analytics API features to the appropriate natural language processing scenarios. To answer, drag the appropriate feature from the column on the left to its scenario on the right. Each feature may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point.

Select and Place:

Box1: Sentiment analysis is the process of determining whether a piece of writing is positive, negative, or neutral.

Box 2: Broad entity extraction: Identify important concepts in the text, including key Keyphrase extraction/ Broad entity extraction: Identify important concepts in the text, including key phrases and named entities such as people, places, and organizations.

Box 3: Entity Recognition 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.

https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processinghttps://azure.microsoft.com/en-us/services/cognitive-services/text-analytics



QUESTION 2

DRAG DROP Match the Microsoft guiding principles for responsible AI to the appropriate descriptions. To answer, drag the appropriate principle from the column on the left to its description on the right. Each principle may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point.

Select and Place:

Correct Answer:

Box 1: Reliability and safety To build trust, it\\'s critical that AI systems operate reliably, safely, and consistently under normal circumstances and in unexpected conditions. These systems should be able to operate as they were originally designed, respond safely to unanticipated conditions, and resist harmful manipulation.

Box 2: Fairness Fairness: AI systems should treat everyone fairly and avoid affecting similarly situated groups of people in different ways. For example, when AI systems provide guidance on medical treatment, loan applications, or employment, they should make the same recommendations to everyone with similar symptoms, financial circumstances, or professional qualifications. We believe that mitigating bias starts with people understanding the implications and limitations of AI predictions and recommendations. Ultimately, people should supplement AI decisions with sound human judgment and be held accountable for consequential decisions that affect others.

Box 3: Privacy and security As AI becomes more prevalent, protecting privacy and securing important personal and business information are becoming more critical and complex. With AI, privacy and data security issues require especially close attention because access to data is essential for AI systems to make accurate and informed predictions and decisions about people. AI systems must comply with privacy laws that require transparency about the collection, use, and storage of data and mandate that consumers have appropriate controls to choose how their data is used

https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles



QUESTION 3

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.

Hot Area:

Correct Answer:

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. Box 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.

Box 2: No

Box 3: Yes

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



QUESTION 4

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

Correct Answer: C

Clustering, in machine learning, is a method of grouping data points into similar clusters. It is also called segmentation. Over the years, many clustering algorithms have been developed. Almost all clustering algorithms use the features of individual items to find similar items. For example, you might apply clustering to find similar people by demographics. You might use clustering with text analysis to group sentences with similar topics or sentiment.

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/machine-learninginitialize-model-clustering



QUESTION 5

You need to create a training dataset and validation dataset from an existing dataset. Which module in the Azure Machine Learning designer should you use?

A. Select Columns in Dataset

B. Add Rows

C. Split Data

D. Join Data

Correct Answer: C

A common way of evaluating a model is to divide the data into a training and test set by using Split Data and then validate the model on the training data. Use the Split Data module to divide a dataset into two distinct sets. The studio currently supports training/validation data splits

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-configure-cross-validation-data-splits2



QUESTION 6

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.

Hot Area:

Correct Answer:

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.

https://www.cloudfactory.com/data-labeling-guidehttps://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance



QUESTION 7

Your website has a chatbot to assist customers. You need to detect when a customer is upset based on what the customer types in the chatbot. Which type of AI workload should you use?

A. anomaly detection

B. semantic segmentation

C. regression

D. natural language processing

Correct Answer: D

Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization. Sentiment Analysis is the process of determining whether a piece of writing is positive, negative, or neutral.

https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing



QUESTION 8

A company employs a team of customer service agents to provide telephone and email support to customers. The company develops a web chatbot to provide automated answers to common customer queries. Which business benefit should the company expect as a result of creating the web chatbot solution?

A. increased sales

B. a reduced workload for the customer service agents

C. improved product reliability

Correct Answer: B



QUESTION 9

HOTSPOT To complete the sentence, select the appropriate option in the answer area. Hot Area:

Correct Answer:

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. Incorrect Answers: Not features: In machine learning and statistics, feature selection is the process of selecting a subset of relevant, useful features to use in building an analytical model. Feature selection helps narrow the field of data to the most valuable inputs. Narrowing the field of data helps reduce noise and improve training performance.

https://www.cloudfactory.com/data-labeling-guide



QUESTION 10

HOTSPOT You have the following dataset.

You plan to use the dataset to train a model that will predict the house price categories of houses. What are Household Income and House Price Category? To answer, select the appropriate option in the answer area. NOTE: Each correct selection is worth one point.

Hot Area:

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio/interpret-model-results



QUESTION 11

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 means squared error (RMSE)

Correct Answer: A

What does a good model look like? A ROC curve that approaches the top left corner with a 100% true positive rate and a 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.

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#classification



QUESTION 12

DRAG DROP Match the types of machine learning to the appropriate scenarios. To answer, drag the appropriate machine learning type from the column on the left to its scenario on the right. Each machine learning type may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point.

Select and Place:

Correct Answer:

Box 1: Regression In the most basic sense, regression refers to the 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.

Box 2: Classification Classification is a machine learning method that uses data to determine the category, type, or class of an item or row of data.

Box 3: Clustering Clustering, in machine learning, is a method of grouping data points into similar clusters. It is also called segmentation. Over the years, many clustering algorithms have been developed. Almost all clustering algorithms use the features of individual items to find similar items. For example, you might apply clustering to find similar people by demographics. You might use clustering with text analysis to group sentences with similar topics or sentiment.

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression



QUESTION 13

Which two scenarios are examples of a conversational AI workload? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

A. a telephone answering service that has a pre-recorded message

B. a chatbot that provides users with the ability to find answers on a website by themselves

C. telephone voice menus to reduce the load on human resources

D. a service that creates frequently asked questions (FAQ) documents by crawling public websites

Correct Answer: BC

B: A bot is an automated software program designed to perform a particular task. Think of it as a robot without a body.

C: Automated customer interaction is essential to a business of any size. In fact, 61% of consumers prefer to communicate via speech, and most of them prefer self-service. Because customer satisfaction is a priority for all businesses, self-service is a critical facet of any customer-facing communications strategy.

Incorrect Answers: D: Early bots were comparatively simple, handling repetitive and voluminous tasks with relatively straightforward algorithmic logic. An example would be web crawlers used by search engines to automatically explore and catalog web content.

https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/ai-overviewhttps://docs.microsoft.com/en-us/azure/architecture/solution-ideas/articles/interactive-voice-response-bot



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[p.s.3] Exam AI-900: Microsoft Azure AI Fundamentals: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900


Candidates for this exam should have the foundational knowledge of machine learning (ML) and artificial intelligence (AI) concepts and related Microsoft Azure services.


This exam is an opportunity to demonstrate knowledge of common ML and AI workloads and how to implement them on Azure.


This exam is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience is not required; however, some general programming knowledge or experience would be beneficial.


Azure AI Fundamentals can be used to prepare for other Azure role-based certifications like Azure Data Scientist Associate or Azure AI Engineer Associate, but it’s not a prerequisite for any of them.


Skills measured

1. The content of this exam was updated on October 22, 2020. Please download the exam skills outline below to see what changed.

2. Describe AI workloads and considerations (15-20%)

3. Describe fundamental principles of machine learning on Azure (30-35%)

4. Describe features of computer vision workloads on Azure (15-20%)

5. Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%)

6. Describe features of conversational AI workloads on Azure (15-20%)


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