ET 1 to 50

Question 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 agents
C. improved product reliability

Answer 1

B. a reduced workload for the customer service agents

Question 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.

Answer 2

B. Randomly split the data into rows for training and rows for evaluation.

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 strati+ed sampling.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/split-data

Question 3

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 A.

Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic exhibit B.

Answer 3

Question 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.

Answer 4

B. Enable Explain best model.

Model 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 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.
Reference:
https://azure.microsoft.com/en-us/blog/new-automated-machine-learning-capabilities-in-azure-machine-learning-service/

Question 5

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 5

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.
Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/anomaly-detection

Question 6

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

A. Inclusiveness
B. privacy and security
C. reliability and safety
D. transparency

Answer 6

C. 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 to respond safely to new situations. Its inherent resilience should resist intended or unintended manipulation. Rigorous testing and validation should 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 reactively and proactively measure the model’s performance and retrain it, as necessary, to modernize it.
Reference:
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai

Question 7

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.

Answer 7

Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing

Question 8

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. fairness
B. inclusiveness
C. reliability and safety
D. accountability

Answer 8

B. inclusiveness

Inclusiveness: At Microsoft, we +rmly believe everyone should bene+t from intelligent technology, meaning it must incorporate and address a broad range of human needs and experiences. For the 1 billion people with disabilities around the world, AI technologies can be a game- changer.
Reference:
https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles

Question 9

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.

Answer 9

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: Accountability -
The people who design and deploy AI systems must be accountable for how their systems operate. Organizations should draw upon industry standards to develop accountability norms. These norms can ensure that AI systems are not the +nal authority on any decision that impacts people’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 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 Reference:
https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles

Question 10

When developing an AI system for self-driving cars, which MS principle for responsible AI should be applied to ensure consistent operation of the system during unexpected circumstances?

A. inclusiveness
B. accountability
C. reliability and safety
D. fairness

Answer 10

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

Question 11

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.

Answer 11

C. Provide documentation to help developers debug code.

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

Question 12

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.

Answer 12

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

Question 13

What is used to extract dates, quantities, and locations from text?

A. Key phrase extraction
B. Language detection
C. Name entity recognition (NER)
D. Sentiment Analysis

Answer 13

C. Name entity recognition (NER)

Question 14

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. knowledgeability B. decisiveness
C. inclusiveness
D. fairness
E. opinionatedness
F. reliability and safety

Answer 14

C. inclusiveness
D. fairness
F. reliability and safety

https://www.microsoft.com/en-us/ai/responsible-ai

Fairness
AI systems should treat all people fairly.

Reliability and safety
AI systems should perform reliably and safely.

Privacy and security
AI systems should be secure and respect privacy.

Inclusiveness
AI systems should empower everyone and engage people.

Transparency
AI systems should be understandable.

Accountability
People should be accountable for AI systems.

Question 15

Returning a bounding box that indicates the location of a vehicle is an example of what?

A. image classification
B. object detection
C. optical character recognition (OCR)
D. semantic segmentation

Answer 15

B. object detection

Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection

Question 16

What is used to generate additional features?

A. feature engineering
B. feature selection
C. Model evaluation
D. MOdel training

Answer 16

A. feature engineering

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/create-features

Question 17

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 service
B. the Detect operation in the Face service
C. the Describe Image operation in the Computer Vision service
D. the Analyze Image operation in the Computer Vision service

Answer 17

B. the Detect operation in the Face service

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

Question 18

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. transparency
B. inclusiveness
C. fairness
D. privacy and security

Answer 18

A. transparency

Achieving transparency helps the team to understand the data and algorithms used to train the model, what transformation logic was applied to the data, the +nal model generated, and its associated assets. This information offers insights about how the model was created, which allows it 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 to understand and address potential barriers that could unintentionally exclude people. Where possible, speech-to-text, text-to-speech, and visual recognition 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 are being 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 religion bias 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 to be secured, and it should be accessed in a way that doesn’t compromise an individual’s privacy.
Reference:
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai https://docs.microsoft.com/en- us/azure/cloud-adoption-framework/strategy/responsible-ai

Question 19

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 19

Box1:Yes-
Achieving transparency helps the team to understand the data and algorithms used to train the model, what transformation logic was applied to the data, the +nal model generated, and its associated assets. This information offers insights about how the model was created, which allows it to be reproduced in a transparent way.

Box2:No-
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 to be secured, and it should be accessed in a way that doesn’t compromise an individual’s privacy.

Box3:No-
Inclusiveness mandates that AI should consider all human races and experiences, and inclusive design practices can help developers to understand and address potential barriers that could unintentionally exclude people. Where possible, speech-to-text, text-to-speech, and visual recognition technology should be used to empower people with hearing, visual, and other impairments.
Reference:
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai

Question 20

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 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.

Answer 20

Reference:
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai https://docs.microsoft.com/en- us/learn/modules/responsible-ai-principles/4-guiding-principles

Question 21

DRAG 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 of processes to the answer area and arrange them in the correct order. Select and Place:

Answer 21

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

Question 22

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

Answer 22

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 privacy officer

Reference:
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai https://docs.microsoft.com/en- us/learn/modules/responsible-ai-principles/3-implications-responsible-ai-practical

Question 23

Which MS principle of responsible AI states that AI systems should NOT reflect biases from the data sets that are use to train the systems?

A. accountability
B. fairness
C. inclusiveness
D. transparency

Answer 23

B. fairness

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

Fairness is a core ethical principle that all humans aim to understand and apply. This principle is even more important when AI systems are being 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 religion bias toward a group or individual.
Reference:
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai

Question 24

Q-24 was a repeat

Question 25

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.

Answer 25

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

Question 26

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 not at all.
NOTE: Each correct selection is worth one point.
Select and Place:

Answer 26

Box 1: Model evaluation -
The Model evaluation module outputs a confusion matrix showing the number of true positives, false negatives, false positives, and true negatives, 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 Azure Machine Learning, scaling and normalization techniques are applied to facilitate feature engineering. Collectively, these techniques and feature engineering 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 be useful for modeling until the information is transformed into units of days, months, or categories that are relevant to the problem, such as holiday 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 an analytical model. Feature selection helps narrow the +eld of data to the most valuable inputs. Narrowing the +eld of data helps reduce noise and improve training performance.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance https://docs.microsoft.com/en- us/azure/machine-learning/concept-automated-ml

Question 27

Data values that influence the prediction of a model are called ?

A. dependent variables
B. features
C. identifies
D. labels

Answer 27

B. features

Reference:
https://www.baeldung.com/cs/feature-vs-label https://machinelearningmastery.com/discover-feature-engineering-how-to-engineer-features-and-how-to-get-good-at-it/

Question 28

You have the Predicted vs. True chart shown in the following exhibit.
Which type of model is the chart used to evaluate?

A. classification
B. regression
C. clustering

Answer 28

B. regression

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 be used 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-m

Question 29

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

Answer 29

B. regression

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 dependent variable.
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 to make predictions.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression

Question 30

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 dataset
B. the trip distance of individual taxi journeys
C. the fare of individual taxi journeys
D. the trip ID of individual taxi journeys

Answer 30

B. the trip distance of individual taxi journeys

The label is the column you want to predict. The identi+ed Featuresare the inputs you give the model to predict the Label.
Example:
The provided data set contains the following columns:
vendor_id: The ID of the taxi vendor is a feature.
rate_code: The rate type of the taxi trip is a feature.
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 predict the 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 feature and you’ll exclude this column from the model. trip_distance: The distance of the trip is a feature. payment_type: The payment method (cash or credit card) is a feature. fare_amount: The total taxi fare paid is the label.
Reference:
https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/predict-prices

Question 31

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

A. classification
B. regression
C. clustering

Answer 31

Correct Answer: B regression

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 dependent variable.
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 to make predictions.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression

Question 32

For each of the following statements, select Yes if the statement is true. Otherwise, select No.

Answer 32

Box1:Yes-
Automated machine learning, also referred to as automated ML or AutoML, is the process of automating the time consuming, iterative tasks of machine learning model development. It allows data scientists, analysts, and developers to build ML models with high scale, ekciency, and productivity all while sustaining model quality.
Box2:No-
Box3:Yes-
During training, Azure Machine Learning creates a number of pipelines in parallel that try different algorithms and parameters for you. The service iterates through
ML algorithms paired with feature selections, where each iteration produces a model with a training score. The higher the score, the better the model is considered to “+t” your data. It will stop once it hits the exit criteria de+ned in the experiment.
Box4:No-
Apply automated ML when you want Azure Machine Learning to train and tune a model for you using the target metric you specify. The label is the column you want to predict.
Reference:
https://azure.microsoft.com/en-us/services/machine-learning/automatedml/#features

Question 33

A banking system that predicts whether a loan will be repaid is an example of that type of machine learning?

A. classification
B. regression
C. clustering

Answer 33

A. classification

Two-class classification provides the answer to simple two-choice questions such as Yes/No or True/False.

Question 34

For each of the following statements, select Yes if the statement is true. Otherwise, select No.

Answer 34

Box1: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, classi+cation, moderation, transcription, or processing.
Box2:No-
Box3:No-
Accuracy is simply the proportion of correctly classi+ed instances. It is usually the +rst metric you look at when evaluating a classi+er. 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 classi+er.
Reference:
https://www.cloudfactory.com/data-labeling-guide https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance

Question 35

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 Understanding
D. Custom Vision

Answer 35

A. Form Recognizer

Accelerate your business processes by automating information extraction. Form Recognizer applies advanced machine learning to accurately extract text, key/ value pairs, and tables from documents. With just a few samples, Form Recognizer tailors its understanding to your documents, both on-premises and in the cloud. Turn forms into usable data at a fraction of the time and cost, so you can focus more time acting on the information rather than compiling it.
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/form-recognizer/

Question 36

The ability to extract subtotals and totals from a receipt is a capability of what?

A. custom vision
B. form recognizer
C. ink reconizer
D. text analytics

Answer 36

B. form recognizer

Accelerate your business processes by automating information extraction. Form Recognizer applies advanced machine learning to accurately extract text, key/ value pairs, and tables from documents. With just a few samples, Form Recognizer tailors its understanding to your documents, both on-premises and in the cloud. Turn forms into usable data at a fraction of the time and cost, so you can focus more time acting on the information rather than compiling it.
Reference: https://azure.microsoft.com/en-us/services/cognitive-services/form-recognizer/

Question 37

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 name
B. the training endpoint
C. the authentication key
D. the REST endpoint

Answer 37

C. the authentication key
D. the REST endpoint

You can consume a published pipeline in the Published pipelines page. Select a published pipeline and +nd the REST endpoint of it. To consume the pipeline, you need:
✑ The REST endpoint for your service
✑ The Primary Key for your service
Reference: https://docs.microsoft.com/en-in/learn/modules/create-regression-model-azure-machine-learning-designer/deploy-service

Question 38

From Azure Machine Learning Designer, where do you deploy to when deploying to a real-time inference pipeline as a service for others to consume?

A. a local web service
B. Azure Container Instances
C. Azure Kubernetes Service (AKS)
D. Azure Machine Learning Compute

Answer 38

C. Azure Kubernetes Service (AKS)

To perform real-time inferencing, you must deploy a pipeline as a real-time endpoint. Real-time endpoints must be deployed to an Azure Kubernetes Service cluster. Reference: https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer#deploy

Question 39

Predicting how many hours of overtime a delivery person will work based on the number of orders received is an example of what?

A. classification
B. clustering
C. regression

Answer 39

C. regression

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 dependent variable.
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 to make predictions.
Incorrect Answers:
✑ Classification is a machine learning method that uses data to determine the category, type, or class of an item or row of data.
✑ 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 +nd similar items. For example, you might apply clustering to +nd similar people by demographics. You might use clustering with text analysis to group sentences with similar topics or sentiment.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/linear-regression https://docs.microsoft.com/en- us/azure/machine-learning/studio-module-reference/machine-learning-initialize-model-clustering

Question 40

For each of the following statements, select Yes if the statement is true. Otherwise, select No.

Answer 40

Box1:Yes-
Azure Machine Learning designer lets you visually connect datasets and modules on an interactive canvas to create machine learning models.
Box2: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.
Box3:No-
Reference: https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer

Question 41

You have the following dataset exhibit A.

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. exhibit B

Answer 41

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

Question 42

Azure Machine Learning Designer lets you create machine learning models by which of the following?

A. adding and connecting models on a visual canvas
B. automatically performing common data preparation tasks
C. automating selecting an algorithm to build the most accurate model
D. using a code-first notebook experience

Answer 42

A. adding and connecting models on a visual canvas

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

Question 43

For each of the following statements, select Yes if the statement is true. Otherwise, select No.

Answer 43

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-designer-python https://docs.microsoft.com/en-us/azure/machine- learning/concept-automated-ml

Question 44

A medical research project uses a large anonymized dataset of brain scan images that are categorized into predefined brain hemorrhage types. You need to use machine learning to support early detection of the different brain hemorrhage types in the images before the images are reviewed by a person.
This is an example of which type of machine learning?

A. clustering
B. regression
C. classification

Answer 44

C. classification

Reference: https://docs.microsoft.com/en-us/learn/modules/create-classi+cation-model-azure-machine-learning-designer/introduction

Question 45

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 performance
C. to test the model by using data that was not used to train the model

Answer 45

C. to test the model by using data that was not used to train the model

Question 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 define and run machine learning experiments from Azure Machine Learning designer.
D. Create a dataset from a comma-separated value (CSV) +le.

Answer 46

A. Use a graphical user interface (GUI) to run automated machine learning experiments.
C. Use a graphical user interface (GUI) to define and run machine learning experiments from Azure Machine Learning designer.

Note: Enterprise workspaces are no longer available as of September 2020. The basic workspace now has all the functionality of the enterprise workspace.
Reference:
https://www.azure.cn/en-us/pricing/details/machine-learning/
https://docs.microsoft.com/en-us/azure/machine-learning/concept-workspace

Question 47

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

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

A. Education Level
B. Last Name
C. Age
D. Income Range
E. First Name

Answer 47

A. Education Level
C. Age

First Name, Last Name, Age and Education Level are features. Income range is a label (what you want to predict). First Name and Last Name are irrelevant in that they have no bearing on income. Age and Education level are the features you should use.

Question 48

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 Recognizer
C. Face

Answer 48

A. Custom Vision

Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/overview

Question 49

For each of the following statements, select Yes if the statement is true. Otherwise, select No.

Answer 49

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

Question 50

For each of the following statements, select Yes if the statement is true. Otherwise, select No.

Answer 50

Box1:No-
The validation dataset is different from the test dataset that is held back from the training of the model.
Box2:Yes-
A validation dataset is a sample of data that is used to give an estimate of model skill while tuning model’s hyperparameters.
Box3:No-
The Test Dataset, not the validation set, used for this. The Test Dataset is a sample of data used to provide an unbiased evaluation of a +nal model +t on the training dataset.
Reference:
https://machinelearningmastery.com/difference-test-validation-datasets/