Bộ đề luyện thi Microsoft AI-900 Exam từ Examtopics - Đã thi đậu (2024)

Bộ đề luyện thi Microsoft AI-900 Exam từ Examtopics - Đã thi đậu.Skills at a glanceDescribe Artificial Intelligence workloads and considerations (15–20%)Describe fundamental principles of machine learning on Azure (20–25%)Describe features of computer vision workloads on Azure (15–20%)Describe features of Natural Language Processing (NLP) workloads on Azure (15–20%)Describe features of generative AI workloads on Azure (15–20%)

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Topic 1 - Single Topic

Topic 1Question #1

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 bene t should the company expect as a result of creating the webchat bot solution?A increased sales

B a reduced workload for the customer service agentsC improved product reliability

Correct Answer: B

Community vote distribution

B (100%)

Topic 1Question #2

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.

Correct Answer: B

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 wantto 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 canalso randomize the selection of rows in each group, and use strati ed sampling.

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/split-dataCommunity vote distribution

B (100%)

HOTSPOT

-You are developing a model to predict events by using classi cation.

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.

Hot Area:

Correct Answer:

Box 1: 11

-negative instances that a classi er predicts correctly are called true positives (TP) and true -negatives (TN), respectively Similarly, theincorrectly classi ed instances are called false positives (FP) and false negatives (FN).

Box 2: 1,033

FN = False Negative Reference:

Topic 1Question #4

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.

Correct Answer: BModel Explain Ability.

Most businesses run on trust and being able to open the ML ג€black boxג€ helps build transparency and trust In heavily regulated industrieslike healthcare and banking, it is critical to comply with regulations and best practices One key aspect of this is understanding the relationshipbetween 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 understandfeature importance as part of automated ML runs.

https://azure.microsoft.com/en-us/blog/new-automated-machine-learning-capabilities-in-azure-machine-learning-service/Community vote distribution

B (100%)

HOTSPOT

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

Correct Answer:

Reliability and safety:

AI systems need to be reliable and safe in order to be trusted It is important for a system to perform as it was originally designed and for it torespond safely to new situations Its inherent resilience should resist intended or unintended manipulation Rigorous testing and validationshould be established for operating conditions to ensure that the system responds safely to edge cases, and A/B testing and

champion/challenger methods should be integrated into the evaluation process.

An AI system's performance can degrade over time, so a robust monitoring and model tracking process needs to be established to reactivelyand proactively measure the model's performance and retrain it, as necessary, to modernize it.

https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai

DRAG DROP

-Match the types of AI workloads to the appropriate scenarios.

To answer, drag the appropriate workload type from the column on the left to its scenario on the right Each workload 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 3: Natural language processing

Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, anddocument categorization.

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

You are designing an AI system that empowers everyone, including people who have hearing, visual, and other impairments.This is an example of which Microsoft guiding principle for responsible AI?

A fairnessB inclusivenessC reliability and safetyD accountability

Correct Answer: B

Inclusiveness: At Microsoft, we rmly believe everyone should bene t from intelligent technology, meaning it must incorporate and address abroad range of human needs and experiences For the 1 billion people with disabilities around the world, AI technologies can be a game-changer.

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

B (100%)

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 thanonce, 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 circ*mstances and in unexpected conditions.These systems should be able to operate as they were originally designed, respond safely to unanticipated conditions, and resist harmfulmanipulation.

Box 2: Accountability

-The people who design and deploy AI systems must be accountable for how their systems operate Organizations should draw upon industrystandards to develop accountability norms These norms can ensure that AI systems are not the nal authority on any decision that impactspeople's lives and that humans maintain meaningful control over otherwise highly autonomous AI systems.

Box 3: Privacy and security

-As AI becomes more prevalent, protecting privacy and securing important personal and business information is becoming more critical andcomplex With AI, privacy and data security issues require especially close attention because access to data is essential for AI systems to makeaccurate and informed predictions and decisions about people AI systems must comply with privacy laws that require transparency about thecollection, 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

You are building an AI system.

Which task should you include to ensure that the service meets the Microsoft transparency principle for responsible AI?A Ensure that all visuals have an associated text that can be read by a screen reader.

B Enable autoscaling to ensure that a service scales based on demand.C Provide documentation to help developers debug code.

D Ensure that a training dataset is representative of the population.

Correct Answer: CReference:

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

C (100%)

DRAG DROP

-Match the types of AI workloads to the appropriate scenarios.

To answer, drag the appropriate workload type from the column on the left to its scenario on the right Each workload 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:

https://docs.microsoft.com/en-us/learn/paths/get-started-with-arti cial-intelligence-on-azure/

Topic 1Question #13

Your company is exploring the use of voice recognition technologies in its smart home devices The company wants to identify any barriers thatmight unintentionally leave out speci c user groups.

This an example of which Microsoft guiding principle for responsible AI?A accountability

B fairnessC inclusivenessD privacy and security

Correct Answer: CReference:

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

C (100%)

What are three Microsoft guiding principles for responsible AI? Each correct answer presents a complete solution.NOTE: Each correct selection is worth one point.

A knowledgeabilityB decisivenessC inclusivenessD fairness

E opinionatednessF reliability and safety

Correct Answer: CDFReference:

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

CDF (100%)

Topic 1Question #15

You run a charity event that involves posting photos of people wearing sunglasses on Twitter.You need to ensure that you only retweet photos that meet the following requirements:✑ Include one or more faces.

✑ Contain at least one person wearing sunglasses.What should you use to analyze the images?

A the Verify operation in the Face serviceB the Detect operation in the Face service

C the Describe Image operation in the Computer Vision serviceD the Analyze Image operation in the Computer Vision service

Correct Answer: BReference:

https://docs.microsoft.com/en-us/azure/cognitive-services/face/overviewCommunity vote distribution

B (100%)

When you design an AI system to assess whether loans should be approved, the factors used to make the decision should be explainable.This is an example of which Microsoft guiding principle for responsible AI?

A transparencyB inclusivenessC fairness

D privacy and security

Correct Answer: A

Achieving transparency helps the team to understand the data and algorithms used to train the model, what transformation logic was applied tothe data, the nal model generated, and its associated assets This information offers insights about how the model was created, which allowsit to be reproduced in a transparent way.

Incorrect Answers:

B: Inclusiveness mandates that AI should consider all human races and experiences, and inclusive design practices can help developers tounderstand and address potential barriers that could unintentionally exclude people Where possible, speech-to-text, text-to-speech, and visualrecognition technology should be used to empower people with hearing, visual, and other impairments.

C: Fairness is a core ethical principle that all humans aim to understand and apply This principle is even more important when AI systems arebeing developed.

Key checks and balances need to make sure that the system's decisions don't discriminate or run a gender, race, sexual orientation, or religionbias toward a group or individual.

D: A data holder is obligated to protect the data in an AI system, and privacy and security are an integral part of this system Personal needs tobe secured, and it should be accessed in a way that doesn't compromise an individual's privacy.

us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai us/azure/cloud-adoption-framework/strategy/responsible-ai

https://docs.microsoft.com/en-Community vote distribution

A (100%)

https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai

DRAG DROP

-Match the principles of responsible AI to appropriate requirements.

To answer, drag the appropriate principles from the column on the left to its requirement on the right Each principle may be used once, more thanonce, or not at all You may need to drag the split bar between panes or scroll to view content.

NOTE: Each correct selection is worth one point.Select and Place:

Correct Answer:

us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai us/learn/modules/responsible-ai-principles/4-guiding-principles

https://docs.microsoft.com/enDRAG DROP

-You plan to deploy an Azure Machine Learning model as a service that will be used by client applications.

Which three processes should you perform in sequence before you deploy the model? To answer, move the appropriate processes from the list ofprocesses to the answer area and arrange them in the correct order.

Select and Place:

Correct Answer:

https://docs.microsoft.com/en-us/azure/machine-learning/concept-ml-pipelines

You are building an AI-based app.

You need to ensure that the app uses the principles for responsible AI.

Which two principles should you follow? Each correct answer presents part of the solution.NOTE: Each correct selection is worth one point.

A Implement an Agile software development methodology

B Implement a process of AI model validation as part of the software review process

C Establish a risk governance committee that includes members of the legal team, members of the risk management team, and a privacyo cer

D Prevent the disclosure of the use of AI-based algorithms for automated decision making

Correct Answer: BCReference:

us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai us/learn/modules/responsible-ai-principles/3-implications-responsible-ai-practical

https://docs.microsoft.com/en-Community vote distribution

BC (100%)

Topic 1Question #23

https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai

DRAG DROP

-Match the types of AI workloads to the appropriate scenarios.

To answer, drag the appropriate workload type from the column on the left to its scenario on the right Each workload 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: Knowledge mining

-You can use Azure Cognitive Search's knowledge mining results and populate your knowledge base of your chatbot.Box 2: Computer vision -

Box 3: Natural language processing

Natural language processing (NLP) is used for tasks such as sentiment analysis.Reference:

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

DRAG DROP

-Match the machine learning tasks to the appropriate scenarios.

To answer, drag the appropriate task from the column on the left to its scenario on the right Each task may be used once, more than once, or notat all.

NOTE: Each correct selection is worth one point.Select and Place:

Correct Answer:

Box 1: Model evaluation

-The Model evaluation module outputs a confusion matrix showing the number of true positives, false negatives, false positives, and truenegatives, as well as

ROC, Precision/Recall, and Lift curves.Box 2: Feature engineering -

Feature engineering is the process of using domain knowledge of the data to create features that help ML algorithms learn better In AzureMachine Learning, scaling and normalization techniques are applied to facilitate feature engineering Collectively, these techniques and featureengineering are referred to as featurization.

Note: Often, features are created from raw data through a process of feature engineering For example, a time stamp in itself might not beuseful for modeling until the information is transformed into units of days, months, or categories that are relevant to the problem, such asholiday versus working day.

Box 3: Feature selection

-In machine learning and statistics, feature selection is the process of selecting a subset of relevant, useful features to use in building ananalytical model Feature selection helps narrow the eld of data to the most valuable inputs Narrowing the eld of data helps reduce noiseand improve training performance.

us/azure/machine-learning/studio/evaluate-model-performance us/azure/machine-learning/concept-automated-ml

You have the Predicted vs True chart shown in the following exhibit.

Which type of model is the chart used to evaluate?A classi cation

B regressionC clustering

Correct Answer: B

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 beused 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.Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-mCommunity vote distribution

B (100%)

Topic 1Question #29

Which type of machine learning should you use to predict the number of gift cards that will be sold next month?A classi cation

B regressionC clustering

Correct Answer: B

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 dependentvariable.

You use this module to de ne a linear regression method, and then train a model using a labeled dataset The trained model can then be used tomake predictions.

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

B (100%)

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 datasetB the trip distance of individual taxi journeysC the fare of individual taxi journeys

D the trip ID of individual taxi journeys

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 predictthe 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 featureand you'll exclude this column from the model trip_distance: The distance of the trip is a feature payment_type: The payment method (cash orcredit card) is a feature fare_amount: The total taxi fare paid is the label.

https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/predict-pricesCommunity vote distribution

B (100%)

Topic 1Question #31

You need to predict the sea level in meters for the next 10 years.Which type of machine learning should you use?

A classi cationB regressionC clustering

Correct Answer: B

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 dependentvariable.

You use this module to de ne a linear regression method, and then train a model using a labeled dataset The trained model can then be used tomake predictions.

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

B (100%)

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 Theservice iterates through

ML algorithms paired with feature selections, where each iteration produces a model with a training score The higher the score, the better themodel is considered to " t" your data It will stop once it hits the exit criteria de ned in the experiment.

https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance

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 UnderstandingD Custom Vision

A (100%)

Topic 1Question #36

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 nameB the training endpointC the authentication keyD the REST endpoint

https://docs.microsoft.com/en-in/learn/modules/create-regression-model-azure-machine-learning-designer/deploy-serviceCommunity vote distribution

CD (100%)

Topic 1Question #38

https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer#deploy

HOTSPOT

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

Correct Answer:

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 dependentvariable.

You use this module to de ne a linear regression method, and then train a model using a labeled dataset The trained model can then be used tomake predictions.

us/azure/machine-learning/algorithm-module-reference/linear-regression us/azure/machine-learning/studio-module-reference/machine-learning-initialize-model-clustering

https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer

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:

Correct Answer:

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

https://docs.microsoft.com/en-us/azure/machine-Topic 1Question #44

A medical research project uses a large anonymized dataset of brain scan images that are categorized into prede ned brain haemorrhage types.You need to use machine learning to support early detection of the different brain haemorrhage types in the images before the images arereviewed by a person.

This is an example of which type of machine learning?A clustering

B regressionC classi cation

Correct Answer: CReference:

https://docs.microsoft.com/en-us/learn/modules/create-classi cation-model-azure-machine-learning-designer/introductionCommunity vote distribution

C (100%)

When training a model, why should you randomly split the rows into separate subsets?A to train the model twice to attain better accuracy

B to train multiple models simultaneously to attain better performanceC to test the model by using data that was not used to train the model

Correct Answer: C

Community vote distribution

C (100%)

Topic 1Question #46

You are evaluating whether to use a basic workspace or an enterprise workspace in Azure Machine Learning.What are two tasks that require an enterprise workspace? Each correct answer presents a complete solution.NOTE: Each correct selection is worth one point.

A Use a graphical user interface (GUI) to run automated machine learning experiments.B Create a compute instance to use as a workstation.

C Use a graphical user interface (GUI) to de ne and run machine learning experiments from Azure Machine Learning designer.D Create a dataset from a comma-separated value (CSV) le.

You need to predict the income range of a given customer by using the following dataset.

Which two elds should you use as features? Each correct answer presents a complete solution.NOTE: Each correct selection is worth one point.

A Education LevelB Last NameC Age

D Income RangeE First Name

You are building a tool that will process images from retail stores and identify the products of competitors.The solution will use a custom model.

Which Azure Cognitive Services service should you use?A Custom Vision

B Form RecognizerC Face

D Computer Vision

Correct Answer: AReference:

https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/overviewCommunity vote distribution

A (100%)

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 coe cient of determination (R2)B F1 score

C root mean squared error (RMSE)D area under curve (AUC)

DRAG DROP

-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? To answer, drag the appropriate modules to the correct locations Each modulemay be used once, more than once, or not at all You may need to drag the split bar between panes or scroll to view content.

NOTE: Each correct selection is worth one point.Select and Place:

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 youmust use the same replacement method in all selected columns.

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 andthe other will test how well the model performed.