Microsoft Azure Test 2

The Microsoft Azure Data Scientist DP-100 mock test is an excellent way to validate your knowledge of Azure Machine Learning. It demonstrates your ability to design and implement data science solutions in the cloud.

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On top of that, data science and AI expertise in TensorFlow, PyTorch, LLMs, and Generative AI are increasingly required. Modern roles blend data analysis, engineering, and AI, demanding versatility.

SynergisticIT’s Data Science JOPP program ensures you go beyond certification, equipping you with advanced skills, project work, and interview preparation to thrive in today’s competitive market.

Welcome to your Microsoft Azure Test-2

Name

Phone

Q1. You are a data scientist and you use Azure Machine Learning Studio for your experiments.

You are creating a new experiment in Azure Machine Learning Studio.
You have a small dataset that has missing values in many columns.
The data does not require the application of predictors for each column.
You plan to use the Clean Missing Data. You need to select a data cleaning method.

Which method should you use?

Select 1 option(s):

Normalization

Replace using MICE

Replace using Probabilistic PCA

Synthetic Minority Oversampling Technique (SMOTE)

Q2. You are analyzing a raw dataset that requires cleaning.

You must perform transformations and manipulations by using Azure Machine Learning Studio.
You need to identify the correct module to perform the below transformation.
Which module should you choose?

Scenario:
Replace missing values by removing rows and columns

Select 1 option(s):

SMOTE

Convert to Indicator Values

Remove Duplicate Rows

Clean Missing Data

Threshold Filter

Q3. Case study

This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section.

To start the case study
To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an
All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question.

Overview
You are a data scientist for Fabrikam Residences, a company specializing in quality private and commercial property in the United States. Fabrikam Residences is considering expanding into Europe and has asked you to investigate prices for private residences in major European cities.
You use Azure Machine Learning Studio to measure the median value of properties. You produce a regression model to predict property prices by using the
Linear Regression and Bayesian Linear Regression modules.

Datasets
There are two datasets in CSV format that contain property details for two cities, London and Paris. You add both files to Azure Machine Learning Studio as separate datasets to the starting point for an experiment. Both datasets contain the following columns:

An initial investigation shows that the datasets are identical in structure apart from the MedianValue column. The smaller Paris dataset contains the MedianValue in text format, whereas the larger London dataset contains the MedianValue in numerical format.

Data issues
Missing values
The AccessibilityToHighway column in both datasets contains missing values. The missing data must be replaced with new data so that it is modeled conditionally using the other variables in the data before filling in the missing values.
Columns in each dataset contain missing and null values. The datasets also contain many outliers. The Age column has a high proportion of outliers. You need to remove the rows that have outliers in the Age column. The MedianValue and AvgRoomsInHouse columns both hold data in numeric format. You need to select a feature selection algorithm to analyze the relationship between the two columns in more detail.

Model fit
The model shows signs of overfitting. You need to produce a more refined regression model that reduces the overfitting.

Experiment requirements
You must set up the experiment to cross-validate the Linear Regression and Bayesian Linear Regression modules to evaluate performance. In each case, the predictor of the dataset is the column named MedianValue. You must ensure that the datatype of the MedianValue column of the Paris dataset matches the structure of the London dataset.
You must prioritize the columns of data for predicting the outcome. You must use non-parametric statistics to measure relationships.
You must use a feature selection algorithm to analyze the relationship between the MedianValue and AvgRoomsInHouse columns.

Model training
Permutation Feature Importance
Given a trained model and a test dataset, you must compute the Permutation Feature Importance scores of feature variables. You must be determined the absolute fit for the model.

Hyperparameters
You must configure hyperparameters in the model learning process to speed the learning phase. In addition, this configuration should cancel the lowest performing runs at each evaluation interval, thereby directing effort and resources towards models that are more likely to be successful.
You are concerned that the model might not efficiently use compute resources in hyperparameter tuning. You also are concerned that the model might prevent an increase in the overall tuning time. Therefore, must implement an early stopping criterion on models that provides savings without terminating promising jobs.

Testing
You must produce multiple partitions of a dataset based on sampling using the Partition and Sample module in Azure Machine Learning Studio.

Cross-validation
You must create three equal partitions for cross-validation. You must also configure the cross-validation process so that the rows in the test and training datasets are divided evenly by properties that are near each city’s main river. You must complete this task before the data goes through the sampling process.

Linear regression module
When you train a Linear Regression module, you must determine the best features to use in a model. You can choose standard metrics provided to measure performance before and after the feature importance process completes. The distribution of features across multiple training models must be consistent.

Data visualization
You need to provide the test results to the Fabrikam Residences team. You create data visualizations to aid in presenting the results.
You must produce a Receiver Operating Characteristic (ROC) curve to conduct a diagnostic test evaluation of the model. You need to select appropriate methods for producing the ROC curve in Azure Machine Learning Studio to compare the Two-Class Decision Forest and the Two-Class Decision Jungle modules with one another.
Question
HOTSPOT
You need to identify the methods for dividing the data according to the testing requirements.
Which properties should you select?

Select 1 option(s):

Box1: Assign to Folds Box2: False

Box1: Assign to Folds Box2: True

Box1: Head Box2: Partition evenly

Box1: Assign to Folds Box2: Partition with custom partitions

Box1: Assign to Folds Box2: Partition evenly

Box1: Sampling Box2: Partition with custom partitions

Q4. Case study

Model fit
The model shows signs of overfitting. You need to produce a more refined regression model that reduces the overfitting.

Testing
You must produce multiple partitions of a dataset based on sampling using the Partition and Sample module in Azure Machine Learning Studio.

Data visualization
You need to provide the test results to the Fabrikam Residences team. You create data visualizations to aid in presenting the results.
You must produce a Receiver Operating Characteristic (ROC) curve to conduct a diagnostic test evaluation of the model. You need to select appropriate methods for producing the ROC curve in Azure Machine Learning Studio to compare the Two-Class Decision Forest and the Two-Class Decision Jungle modules with one another.
Question
HOTSPOT
You need to configure the Feature Based Feature Selection module based on the experiment requirements and datasets.
How should you configure the module properties?

Select 1 option(s):

Box 1: Mutual Information Box 2: MedianValue

Box 1: Fisher Score Box 2: MedianValue

Box 1: Mutual Information Box 2: AvgRoomInHouse

Box 1: Chi-squared Box 2: AvgRoomInHouse

Box 1: Chi-squared Box 2: MedianValue

Q5. Your manager asked you to analyze a numerical dataset which contains missing values in several columns.

You must clean the missing values using an appropriate operation without affecting the dimensionality of the feature set.
You need to analyze a full dataset to include all values.
Solution:
Use the Last Observation Carried Forward (LOCF) method to impute the missing data points.

Does the solution meet the goal?

Select 1 option(s):

Yes

Q6. Your manager asked you to create a new experiment in Azure Machine Learning Studio.

One class has a much smaller number of observations than the other classes in the training set.
You need to select an appropriate data sampling strategy to compensate for the class imbalance.
Solution:
You use the Synthetic Minority Oversampling Technique (SMOTE) sampling mode.

Does the solution meet the goal?

Select 1 option(s):

Yes

Q7. Your manager have asked you to analyze a numerical dataset which contains missing values in several columns.

You must clean the missing values using an appropriate operation without affecting the dimensionality of the feature set.
You need to analyze a full dataset to include all values.

Solution:
Remove the entire column that contains the missing data point.
Does the solution meet the goal?

Select 1 option(s):

Yes

Q8. You are asked to solve a classification task.

You must evaluate your model on a limited data sample by using k-fold cross-validation. You start by configuring a k parameter as the number of splits.
You need to configure the k parameter for the cross-validation.
Which value should you use?

Select 1 option(s):

k=1

k=0.9

k=0.5

k=10

Q9. You are a data scientist and you use Azure Machine Learning Studio for your experiments.

You are creating a new experiment in Azure Machine Learning Studio.
One class has a much smaller number of observations than the other classes in the training set.
You need to select an appropriate data sampling strategy to compensate for the class imbalance.

Solution:
You use the Principal Components Analysis (PCA) sampling mode.

Does the solution meet the goal?

Select 1 option(s):

Yes

Q10. You are a data scientist using Azure Machine Learning Studio.

You are evaluating a completed binary classification machine learning model.
You need to use the precision as the evaluation metric.
Which visualization should you use?

Select 1 option(s):

Binary classification confusion matrix

Box plot

Gradient descent

Violin plot

Q11. Case study

Model fit
The model shows signs of overfitting. You need to produce a more refined regression model that reduces the overfitting.

Testing
You must produce multiple partitions of a dataset based on sampling using the Partition and Sample module in Azure Machine Learning Studio.

Data visualization
You need to provide the test results to the Fabrikam Residences team. You create data visualizations to aid in presenting the results.
You must produce a Receiver Operating Characteristic (ROC) curve to conduct a diagnostic test evaluation of the model. You need to select appropriate methods for producing the ROC curve in Azure Machine Learning Studio to compare the Two-Class Decision Forest and the Two-Class Decision Jungle modules with one another.
Question
HOTSPOT
You need to set up the Permutation Feature Importance module according to the model training requirements.
Which properties should you select?

Select 1 option(s):

Box 1: F-score Box 2: R-squared

Box 1: Accuracy Box 2: R-squared

Box 1: Precision Box 2: R-squared

Box 1: Recall Box 2: Root of mean squared error

Box 1: F-score Box 2: Mean absolute error

Box 1: Accuracy Box 2: Mean zero one error

Q12. Case study

Model fit
The model shows signs of overfitting. You need to produce a more refined regression model that reduces the overfitting.

Testing
You must produce multiple partitions of a dataset based on sampling using the Partition and Sample module in Azure Machine Learning Studio.

Data visualization
You need to provide the test results to the Fabrikam Residences team. You create data visualizations to aid in presenting the results.
You must produce a Receiver Operating Characteristic (ROC) curve to conduct a diagnostic test evaluation of the model. You need to select appropriate methods for producing the ROC curve in Azure Machine Learning Studio to compare the Two-Class Decision Forest and the Two-Class Decision Jungle modules with one another.
Question
HOTSPOT
You need to configure the Edit Metadata module so that the structure of the datasets match.
Which configuration options should you select?

Select 1 option(s):

Box 1: Integer Box 2: Make Uncategorical

Box 1: Floating point Box 2: Make Uncategorical

Box 1: Floating point Box 2: Unchanged

Box 1: TimeSpan Box 2: Make Uncategorical

Box 1: DateTime Box 2: Unchanged

Box 1: Integer Box 2: Make Categorical

Q13. You are a data scientist using Azure Machine Learning Studio.

You are creating a machine learning model.
You need to identify outliers in the data.
Which two visualizations can you use?

Select 2 option(s):

Venn diagram

Scatter plot

Box plot

Random forest diagram

ROC curve

Q14. HOTSPOT

You are a data scientist of your company
You are working on a classification task.
You have a dataset indicating whether a student would like to play soccer and associated attributes.
The dataset includes the following columns:

You need to classify variables by type.
Which variable should you add to below category?

Select 1 option(s):

PrevExamMarks, Height, Weight

IsPLaySoccer

Gender, IsPLaySoccer

Gender, PrevExamMarks, Height, Weight

Q15. HOTSPOT

You are a data scientist using Azure Machine Learning Studio.
You are using the Azure Machine Learning Service to automate hyperparameter exploration of your neural network classification model.
You must define the hyperparameter space to automatically tune hyperparameters using random sampling according to following requirements:
§ The learning rate must be selected from a normal distribution with a mean value of 10 and a standard deviation of 3.
§ Batch size must be 16, 32 and 64.
§ Keep probability must be a value selected from a uniform distribution between the range of 0.05 and 0.1.

You need to use the param_sampling method of the Python API for the Azure Machine Learning Service.
How should you complete the code segment?

Select 1 option(s):

learning_rate = choice(10,3) batch_size = choice(range(16,64) keep_probability = normal(0.05,0.1)

learning_rate = Loguniform(10,3) batch_size = normal(range(16,64) keep_probability = lognormal(0.05,0.1)

learning_rate = uniform(10,3) batch_size = normal(16,32,64) keep_probability = choice(range(0.05,0.1))

learning_rate = normal(10,3) batch_size = choice(16,32,64) keep_probability = uniform(0.05,0.1)

Q16. DRAG DROP

You have a dataset that contains over 150 features.
You use the dataset to train a Support Vector Machine (SVM) binary classifier.
You need to use the Permutation Feature Importance module in Azure Machine Learning Studio to compute a set of feature importance scores for the dataset.
In which order should you perform the actions?

Select 1 option(s):

Action 1 Action 5 Action 3 Action 4 Action 2

Action 2 Action 5 Action 1 Action 3 Action 4

Action 1 Action 4 Action 5 Action 3 Action 2

Action 4 Action 5 Action 1 Action 3 Action 2

Q17. HOTSPOT

Your manager have asked you to create a binary classification model to predict whether a person has a disease.
You need to detect possible classification errors.
Which error type should you choose for below description?

Select 1 option(s):

False Positives

True Negatives

True Positives

False Negatives

Q18. You are analyzing a raw dataset that requires cleaning.

You must perform transformations and manipulations by using Azure Machine Learning Studio.
You need to identify the correct module to perform the below transformation.
Which module should you choose?

Scenario:
Remove potential duplicates from a dataset

Select 1 option(s):

SMOTE

Threshold Filter

Convert to Indicator Values

Clean Missing Data

Remove Duplicate Rows

Q19. HOTSPOT

True Positives

False Negatives

True Negative

False Positives

Q20. Case study

Model fit
The model shows signs of overfitting. You need to produce a more refined regression model that reduces the overfitting.

Testing
You must produce multiple partitions of a dataset based on sampling using the Partition and Sample module in Azure Machine Learning Studio.

Data visualization
You need to provide the test results to the Fabrikam Residences team. You create data visualizations to aid in presenting the results.
You must produce a Receiver Operating Characteristic (ROC) curve to conduct a diagnostic test evaluation of the model. You need to select appropriate methods for producing the ROC curve in Azure Machine Learning Studio to compare the Two-Class Decision Forest and the Two-Class Decision Jungle modules with one another.
Question
You need to visually identify whether outliers exist in the Age column and quantify the outliers before the outliers are removed.
Which three Azure Machine Learning Studio modules should you use? Each correct answer presents part of the solution.

Select 3 option(s):

Clip Values

Replace Discrete Values

Build Counting Transform

Create Scatterplot

Summarize Data

Q21. Your manager asked you to create a new experiment in Azure Machine Learning Studio.

Does the solution meet the goal?

Select 1 option(s):

Yes

Q22. HOTSPOT

You need to classify variables by type.
Which variable should you add to below category?

Select 1 option(s):

Gender, IsPLaySoccer

IsPLaySoccer

PrevExamMarks, Height, Weight

Gender, PrevExamMarks, Height, Weight

Q23. You are a data scientist using Azure Machine Learning Studio.

You are using Azure Machine Learning Studio to perform feature engineering on a dataset.
You need to normalize values to produce a feature column grouped into bins.
Solution:
Apply an Entropy Minimum Description Length (MDL) binning mode.

Does the solution meet the goal?

Select 1 option(s):

Yes

Q24. You are a data scientist using Azure Machine Learning Studio.

You are analyzing a dataset by using Azure Machine Learning Studio.
You need to generate a statistical summary that contains the p-value and the unique count for each feature column.
Which two modules can you use?

Select 2 option(s):

Export Count Table

Summarize Data

Computer Linear Correlation

Execute Python Script

Convert to Indicator Values

Q25. HOTSPOT

You are a junior data scientist of your company.
You have a Python dataframe named salesData in the following format:

The data frame must be unpivoted to a long data format as below:

You need to use the pandas.melt() function in Python to perform this transformation.
How should you complete the code segment? To answer, select the appropriate options in the answer area.

Select 1 option(s):

Box1: salesdata Box2: Shop X, Shop Y, Shop Box3: ['year']

Box1: pandas Box2: value Box3: 'shop'

Box1: dataframe Box2: 'shop' Box3: ['2017','2018']

Box1: salesData Box2: shop Box3: ['2017','2018']

Q26. You are analyzing a raw dataset that requires cleaning.

You must perform transformations and manipulations by using Azure Machine Learning Studio.
You need to identify the correct module to perform the below transformation.
Which module should you choose?

Scenario:
Increase the number of low-incidence examples in the dataset

Select 1 option(s):

Threshold Filter

Remove Duplicate Rows

Clean Missing Data

Convert to Indicator Values

SMOTE

Q27. Case study

Data issues

Missing values
The AccessibilityToHighway column in both datasets contains missing values. The missing data must be replaced with new data so that it is modeled conditionally using the other variables in the data before filling in the missing values.
Columns in each dataset contain missing and null values. The datasets also contain many outliers. The Age column has a high proportion of outliers. You need to remove the rows that have outliers in the Age column. The MedianValue and AvgRoomsInHouse columns both hold data in numeric format. You need to select a feature selection algorithm to analyze the relationship between the two columns in more detail.

Model fit
The model shows signs of overfitting. You need to produce a more refined regression model that reduces the overfitting.

Model training

Permutation Feature Importance
Given a trained model and a test dataset, you must compute the Permutation Feature Importance scores of feature variables. You must be determined the absolute fit for the model.

Testing
You must produce multiple partitions of a dataset based on sampling using the Partition and Sample module in Azure Machine Learning Studio.

Question
You need to select a feature extraction method.
Which method should you use?

Select 1 option(s):

Fisher Linear Discriminant Analysis

Pearson's correlation

Spearman correlation

Mutual information

Q28. You have a model with a large difference between the training and validation error values. You must create a new model and perform cross-validation. You need to identify a parameter set for the new model using Azure Machine Learning Studio.

Which module you should use for below step?
Step:
Define the parameter scope

Select 1 option(s):

Partition and Sample

Two-Class Boosted Decision Tree

Tune Model Hyperparameters

Split Data

Q29. HOTSPOT

You are employee of digiTechClouds and you are analyzing the asymmetry in a statistical distribution.
The following image contains two density curves that show the probability distribution of two datasets.

Use the drop-down menus to select the answer choice that answers each question based on the information presented in the graphic above.

Select 1 option(s):

Graph-1--> Negative skew Graph-2--> Positive skew

Graph-1--> Positive skew Graph-2--> Negative skew

Graph-1--> Positive skew Graph-2--> Positive skew

Graph-1--> Negative skew Graph-2--> Negative skew

Q30. You are data scientist of your company

You are creating a binary classification by using a two-class logistic regression model.
You need to evaluate the model results for imbalance.
Which evaluation metric should you use?

Select 1 option(s):

Accuracy

Relative Squared Error

Root Mean Square Error

Relative Absolute Error

AUC Curve

Mean Absolute Error

Q31. You have a model with a large difference between the training and validation error values. You must create a new model and perform cross-validation. You need to identify a parameter set for the new model using Azure Machine Learning Studio.

Which module you should use for below step?
Step:
Define the cross-validation settings

Select 1 option(s):

Split Data

Partition and Sample

Tune Model Hyperparameters

Two-Class Boosted Decision Tree

Q32. You are creating a model to predict the price of a student’s artwork depending on the following variables: the student’s length of education, degree type, and art form.

You start by creating a linear regression model.
You need to evaluate the linear regression model.
Solution:
Use the following metrics: Mean Absolute Error, Root Mean Absolute Error, Relative Absolute Error, Relative Squared Error, and the Coefficient of Determination.
Does the solution meet the goal?

Select 1 option(s):

Yes

Q33. You are a lead data scientist for a project that tracks the health and migration of birds. You create a multi-image classification deep learning model that uses a set of labeled bird photos collected by experts. You plan to use the model to develop a cross-platform mobile app that predicts the species of bird captured by app users.

You must test and deploy the trained model as a web service. The deployed model must meet the following requirements:
• An authenticated connection must not be required for testing.
• The deployed model must perform with low latency during inferencing.
• The REST endpoints must be scalable and should have a capacity to handle large number of requests when multiple end users are using the mobile application.
You need to verify that the web service returns predictions in the expected JSON format when a valid REST request is submitted.
Which compute resources should you use?

Select 1 option(s):

ds-workstation notebook VM

gpu-compute cluster

aks-compute cluster

cpu-compute cluster

Q34. You are working as a ML professional for your company.

Your manager have asked you to build a recurrent neural network to perform a binary classification.
You review the training loss, validation loss, training accuracy, and validation accuracy for each training epoch.
You need to analyze model performance.
You need to identify whether the classification model is overfitted.
Which of the following is correct?

Select 1 option(s):

The training loss stays constant and the validation loss stays on a constant value and close to the training loss value when training the model.

The training loss increases while the validation loss decreases when training the model.

The training loss decreases while the validation loss increases when training the model.

The training loss stays constant and the validation loss decreases when training the model.

Q35. Case study

Data issues

Model fit
The model shows signs of overfitting. You need to produce a more refined regression model that reduces the overfitting.

Model training

Testing
You must produce multiple partitions of a dataset based on sampling using the Partition and Sample module in Azure Machine Learning Studio.

Question
You need to select a feature extraction method.
Which method should you use?

Select 1 option(s):

Mood's median test

Kendall correlation

Mutual information

Permutation Feature Importance

Q36. You create a multi-class image classification deep learning experiment by using me Pytorch framework. you plan to run the experiment on an Azure compute cluster that has noses with GPU’s.

You need to define an Azure Machine Learning service pipeline to perform the monthly retraining of the image classification model. The pipeline must run with minimal cost and minimize the time required to train the model.
Which three pipeline steps should you run in sequence?

Select 1 option(s):

Action 1 Action 6 Action 2

Action 3 Action 6 Action 4

Action 1 Action 7 Action 4

Action 1 Action 5 Action 4

Q37. You write five Python scripts that must be processed in the order specified in Exhibit A — which allows the same modules to run in parallel, but will wait for modules with dependencies.

You must create an Azure Machine Learning pipeline using the Python SDK, because you want to script to create the pipeline to be tracked in your version control system.
You have created five PythonScriptSteps and have named the variables to match the module names.

You need to create the pipeline shown. Assume all relevant imports have been done.
Which Python code segment should you use?

Option A:

Option B:

Option C:

Option D:

Select 1 option(s):

Option A

Option C

Option D

Option B

Q38. You are creating a model to predict the price of a student’s artwork depending on the following variables: the student’s length of education, degree type, and art form.

You start by creating a linear regression model.
You need to evaluate the linear regression model.

Solution:
Use the following metrics: Accuracy, Precision, Recall, F1 score, and AUC.
Does the solution meet the goal?

Select 1 option(s):

Yes

Q39. Case study

Data issues

Model fit
The model shows signs of overfitting. You need to produce a more refined regression model that reduces the overfitting.

Model training

Testing
You must produce multiple partitions of a dataset based on sampling using the Partition and Sample module in Azure Machine Learning Studio.

Question
DRAG DROP
You need to implement an early stopping criteria policy for model training.
Which three code segments should you use to develop the solution?

Select 1 option(s):

Action 3 Action 2 Action 5

Action 1 Action 2 Action 3

Action 2 Action 5 Action 4

Action 5 Action 2 Action 3

Action 3 Action 2 Action 1

Q40. HOTSPOT

You are working as a Machine Learning professional of your company.
You are evaluating a Python NumPy array that contains six data points defined as follows: data = [10, 20, 30, 40, 50, 60]
You must generate the following output by using the k-fold algorithm implantation in the Python Scikit-learn machine learning library:
train: [10 40 50 60], test: [20 30]
train: [20 30 40 60], test: [10 50]
train: [10 20 30 50], test: [40 60]

You need to implement a cross-validation to generate the output.
How should you complete the code segment?
To answer, select the appropriate code segment in the dialog box in the answer area.

Select 1 option(s):

import CrossValidation n_split-3 split(data)

import k-fold n_split-2 split(data)

import k-Means n_split-6 split(array)

import k-fold n_split-3 split(data)

Q41. You are an employee of digTechClouds.

You are performing feature engineering on a dataset.
You must add a feature named CityName and populate the column value with the text London.
You need to add the new feature to the dataset.
Which Azure Machine Learning Studio module should you use?

Select 1 option(s):

Execute Python Script

Edit Metadata

Latent Dirichlet Allocati

Filter Based Feature Selection

Q42. Case study

Data issues

Model fit
The model shows signs of overfitting. You need to produce a more refined regression model that reduces the overfitting.

Model training

Testing
You must produce multiple partitions of a dataset based on sampling using the Partition and Sample module in Azure Machine Learning Studio.

Question
HOTSPOT
You need to configure the Permutation Feature Importance module for the model training requirements.
What should you do?

Select 1 option(s):

Box 1: 500 Box 2: Regression- Mean Zero One Error

Box 1: 500 Box 2: Regression- Root Mean Square Error

Box 1: 500 Box 2: Regression- Mean Absolute Error

Box 1: 500 Box 2: Regression- R-squared

Q43. You are a data scientist of the company named digiTechClouds

You are building a deep convolutional neural network (CNN) for image classification.
The CNN model you build shows signs of overfitting.
You need to reduce overfitting and converge the model to an optimal fit.
Which two actions should you perform? Each correct answer presents a complete solution.

Select 2 option(s):

Add an additional dense layer with 512 input units.

Add L1/L2 regularization.

Add an additional dense layer with 64 input units.

Use training data augmentation.

Reduce the amount of training data.

Q44. You are junior data scientist of your company and you use Azure Machine Learning Studio

You use the Two-Class Neural Network module in Azure Machine Learning Studio to build a binary classification model.
You use the Tune Model Hyperparameters module to tune accuracy for the model.
You need to configure the Tune Model Hyperparameters module.
Which two values should you use? Each correct answer presents part of the solution.

Select 2 option(s):

Learning Rate

Number of learning iterations

Number of hidden nodes

Hidden layer specification

The type of the normalizer

Q45. You write code to retrieve an experiment that is run from your Azure Machine Learning workspace.

The run used the model interpretation support in Azure Machine Learning to generate and upload a model explanation.
Business managers in your organization want to see the importance of the features in the model.
You need to print out the model features and their relative importance in an output that looks similar to the following.

How should you complete the code? To answer, select the appropriate options in the answer area.

Select 1 option(s):

Box 1: download-model_explanation Box 2: list_model_explanations Box 3: get_feature_important_dict

Box 1: from_run Box 2: download-model_explanation Box 3: explanation

Box 1: from_run_id Box 2: upload_model_explanations Box 3: explanation

Box 1: from_run_id Box 2: list_model_explanations Box 3: explanation_client

Box 1: from_run_id Box 2: list_model_explanations Box 3: explanation

Q46. You are a data scientist of your company and you are creating a linear regression model.

You need to determine how closely the data fits the regression line.
Which metric should you review?

Select 1 option(s):

Mean absolute error

Root Mean Square Error

Coefficient of determination

Recall

Precision

Q47. You are a data scientist and you use Machine Learning Studio for your work.

You are with a time series dataset in Azure Machine Learning Studio.
You need to split your dataset into training and testing subsets by using the Split Data module.
Which splitting mode should you use?

Select 1 option(s):

Split Rows with the Randomized split parameter set to true

Relative Expression Split

Regular Expression Split

Recommender Split

Q48. You are junior data scientist of your company and you use Azure ML Studio for your work.

You create a binary classification model by using Azure Machine Learning Studio.
You must tune hyperparameters by performing a parameter sweep of the model.
The parameter sweep must meet the following requirements:
§ iterate all possible combinations of hyperparameters
§ minimize computing resources required to perform the sweep

You need to perform a parameter sweep of the model.
Which parameter sweep mode should you use?

Select 1 option(s):

Sweep clustering

Entire grid

Random grid

Random sweep

Q49. You create a binary classification model for you company.

You need to evaluate the model performance.
Which two metrics can you use?
Each correct answer presents a complete solution.

Select 2 option(s):

coefficient of determination

relative absolute error

accuracy

mean absolute error

precision

Q50. You are data scientist of your company and you use Azure Machine Learning Studio for your work

You are determining if two sets of data are significantly different from one another by using Azure Machine Learning Studio.
Estimated values in one set of data may be more than or less than reference values in the other set of data.
You must produce a distribution that has a constant Type I error as a function of the correlation.
You need to produce the distribution.
Which type of distribution should you produce?

Select 1 option(s):

Paired t-test with a two-tail option

Paired t-test with a one-tail option

Unpaired t-test with a one-tail option

Unpaired t-test with a two-tail option

Q51. Your manager have asked you to perform feature engineering on a dataset.

You must add a feature named CityName and populate the column value with the text London.
You need to add the new feature to the dataset.
Which Azure Machine Learning Studio module should you use?

Select 1 option(s):

Extract N-Gram Features from Text

Edit Metadata

Preprocess Text

Apply SQL Transformation

Q52. DRAG DROP

You are data scientist of your company and you use Azure Machine Learning Studio
You are producing a multiple linear regression model in Azure Machine Learning Studio.
Several independent variables are highly correlated.
You need to select appropriate methods for conducting effective feature engineering on all the data.
Which three actions should you perform in sequence?

Select 1 option(s):

Step 1: Use the Filter Based Feature Selection module Step 2: Build a counting transform Step 3: Test the hypothesis using t-Test

Step 1: Remove duplicate rows Step 2: Evaluate the probability function Step 3: Compute linear correlation

Step 1: Use the Filter Based Feature Selection module Step 2: Remove duplicate rows Step 3: Compute linear correlation

Step 1: Remove duplicate rows Step 2: Evaluate the probability function Step 3: Build a counting transform

Step 1: Build a counting transform Step 2: Test the hypothesis using t-Test Step 3: Use the Filter Based Feature Selection module

Q53. You create a datastore named training_data that references a blob container in an Azure Storage account. The blob container contains a folder named csv_files in which multiple comma-separated values (CSV) files are stored.

You have a script named train.py in a local folder named ./script that you plan to run as an experiment using an estimator. The script includes the following code to read data from the csv_files folder:

# Code goes on to split the training data and train a logistic regression model

You have the following script.

You need to configure the estimator for the experiment so that the script can read the data from a data reference named data_ref that references the csv_files folder in the training_data datastore.
Which code should you use to configure the estimator?

Option 1:

Option 2:

Option 3:

Option 4:

Option 5:

Select 1 option(s):

Option 5

Option 1

Option 4

Option 2

Option 3

Q54. Case study

Data issues

Model fit
The model shows signs of overfitting. You need to produce a more refined regression model that reduces the overfitting.

Model training

Testing
You must produce multiple partitions of a dataset based on sampling using the Partition and Sample module in Azure Machine Learning Studio.

Question
DRAG DROP
You need to correct the model fit issue.
Which three actions should you perform in sequence?

Select 1 option(s):

Action 3 Action 4 Action 5

Action 7 Action 4 Action 3

Action 3 Action 4 Action 7

Action 2 Action 4 Action 6

Action 1 Action 3 Action 7

Q55. You are junior data scientist of your company.

You are building a machine learning model for translating English language textual content into French language textual content.
You need to build and train the machine learning model to learn the sequence of the textual content.
Which type of neural network should you use?

Select 1 option(s):

Multilayer Perceptions (MLPs)

Generative Adversarial Networks (GANs)

Recurrent Neural Networks (RNNs)

Convolutional Neural Networks (CNNs)

Q56. HOTSPOT

You are data scientist of your company and you use Azure Machine Learning Studio
You are developing a linear regression model in Azure Machine Learning Studio. You run an experiment to compare different algorithms.
The following image displays the results dataset output:

Choose appropriate answer from the following box

Select 1 option(s):

Increase the number of epochs

Set the Decrease learning rate option to True

Decrease the number of epochs

Set the Decrease learning rate option to False

Set the Create trainer mode option to Parameter Range

Q57. You are a lead data scientist for a project that tracks the health and migration of birds. You create a multi-image classification deep learning model that uses a set of labeled bird photos collected by experts. You plan to use the model to develop a cross-platform mobile app that predicts the species of bird captured by app users.

gpu-compute cluster

cpu-compute cluster

ds-workstation notebook VM

aks-compute cluster

Q58. You are preparing to use the Azure ML SDK to run an experiment and need to create compute. You run the following code:

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

Select 1 option(s):

Box 1: No Box 2: No Box 3: Yes Box 3: No

Box 1: No Box 2: No Box 3: No Box 4: No

Box 1: Yes Box 2: Yes Box 3: Yes Box 4: Yes

Box 1: No Box 2: Yes Box 3: Yes Box 4: No

Box 1: No Box 2: Yes Box 3: No Box 4: No

Q59. You are trainee of digiTechClouds company

Your instructor asked you to perform clustering by using the K-means algorithm.
You need to define the possible termination conditions.
Which three conditions can you use? Each correct answer presents a complete solution.

Select 3 option(s):

Centroids do not change between iterations.

The sum of distances between centroids reaches a maximum.

The residual sum of squares (RSS) falls below a threshold.

The residual sum of squares (RSS) rises above a threshold.

A fixed number of iterations is executed.

Q60. Case study

Data issues

Model fit
The model shows signs of overfitting. You need to produce a more refined regression model that reduces the overfitting.

Model training

Testing
You must produce multiple partitions of a dataset based on sampling using the Partition and Sample module in Azure Machine Learning Studio.

Question
DRAG DROP
You need to implement early stopping criteria as stated in the model training requirements.
Which three code segments should you use to develop the solution?

Select 1 option(s):

Action 1 Action 2 Action 3

Action 1 Action 3 Action 5

Action 3 Action 2 Action 5

Action 4 Action 2 Action 1

Action 5 Action 3 Action 1