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Online DP-100 Test Questions

QUESTION 1

You need to define a modeling strategy for ad response. Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Step 1: Implement a K-Means Clustering model
Step 2: Use the cluster as a feature in a Decision jungle model.
Decision jungles are non-parametric models, which can represent non-linear decision boundaries.
Step 3: Use the raw score as a feature in a Score Matchbox Recommender model

The goal of creating a recommendation system is to recommend one or more “items” to “users” of the system. Examples of an item could be a movie, restaurant, book, or song. A user could be a person, group of persons or other entity with item preferences.

Scenario:
Ad response rated declined.
Ad response models must be trained at the beginning of each event and applied during the sporting event.
Market segmentation models must optimize for similar ad response history.
Ad response models must support non-linear boundaries of features.

References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/multiclass-decision-jungle
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/score-matchbox-recommender

QUESTION 2

You are planning to make use of Azure Machine Learning designer to train models. You need to choose a suitable compute type. Recommendation: You choose the Inference cluster. Will the requirements be satisfied?

A. Yes
B. No

Correct Answer: B

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-studio

QUESTION 3

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.

After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear on the review screen. You create a model to forecast weather conditions based on historical data. You need to create a pipeline that runs a processing script to load data from a datastore and pass the processed data to a machine learning model training script.

Solution: Run the following code:

Does the solution meet the goal?

A. Yes
B. No

Correct Answer: B

Note: Data used in the pipeline can be produced by one step and consumed in another step by providing a PipelineData object as an output of one step and input of one or more subsequent steps.
Compare with this example, the pipeline train step depends on the process_step_output output of the pipeline process step:

from azure ml.pipeline.core import Pipeline, PipelineData from azure ml.pipeline.steps import PythonScriptStep datastore = was.get_default_datastore()
process_step_output = PipelineData(“processed_data”, datastore=datastore) process_step =
PythonScriptStep(script_name=”process.py”, arguments=[“–data_for_train”, process_step_output],
outputs=[process_step_output],
compute_target=aml_compute,
source_directory=process_directory)
train_step = PythonScriptStep(script_name=”train.py”,
arguments=[“–data_for_train”, process_step_output],
inputs=[process_step_output],
compute_target=aml_compute,
source_directory=train_directory)
pipeline = Pipeline(workspace=ws, steps=[process_step, train_step])

Reference:
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata? view=azure-mlpy

QUESTION 4

You are a data scientist working for a bank and have used Azure ML to train and register a machine learning model that predicts whether a customer is likely to repay a loan. You want to understand how your model is making selections and must be sure that the model does not violate government regulations such as denying loans based on where an applicant lives.

You need to determine the extent to which each feature in the customer data is influencing predictions.
What should you do?

A. Enable data drift monitoring for the model and its training dataset.
B. Score the model against some test data with known label values and use the results to calculate a confusion matrix.
C. Use the Hyperdrive library to test the model with multiple hyperparameter values.
D. Use the interpretability package to generate an explainer for the model.
E. Add tags to the model registration indicating the names of the features in the training dataset.

Correct Answer: D

When you compute model explanations and visualize them, you\\’re not limited to an existing model for an automated ML model. You can also get a for your model with different test data. The steps in this section show you how to compute and visualize engineered feature importance based on your test data.

Incorrect Answers:
A: In the context of machine learning, data drift is the change in model input data that leads to model performance degradation. It is one of the top reasons why model accuracy degrades over time, thus monitoring data drift helps detect model performance issues.
B: A confusion matrix is used to describe the performance of a classification model. Each row displays the instances of the true, or actual class in your dataset, and each column represents the instances of the class that was predicted by the model.
C: Hyperparameters are adjustable parameters you choose for model training that guide the training process. The HyperDrive package helps you automate choosing these parameters.

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability-automl

QUESTION 5

You plan to use a Python script to run an Azure Machine Learning experiment. The script creates a reference to the experiment run context, loads data from a file, identifies the set of unique values for the label column, and completes the experiment run:

from azure ml.core import Run import pandas as PD run = Run.get_context() data = PD.read_csv(\\’data.csv\\’) label_vals
= data[\\’label\\’].unique() # Add code to record metrics here run.complete()

The experiment must record the unique labels in the data as metrics for the run that can be reviewed later. You must add code to the script to record the unique label values as run metrics at the point indicated by the comment. Solution: Replace the comment with the following code:
run.log_table(\\’Label Values\\’, label_vals) Does the solution meet the goal?

A. Yes
B. No

Correct Answer: B

Instead, use the run_log function to log the contents in label_vals:
for label_val in label_vals: run.log(\\’Label Values\\’, label_val)

Reference: https://www.element61.be/en/resource/azure-machine-learning-services-complete-toolbox-ai

QUESTION 6

DRAG DROP
An organization uses Azure Machine Learning services and wants to expand its use of machine learning.
You have the following compute environments. The organization does not want to create another computing environment.

You need to determine which compute environment to use for the following scenarios. Which compute types should you use? To answer, drag the appropriate compute environments to the correct scenarios.
Each computing environment 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.

Box 1: nb_server Box 2: mlc_cluster With Azure Machine Learning, you can train your model on a variety of resources or environments, collectively referred to as compute targets. A compute target can be a local machine or a cloud resource, such as an Azure Machine Learning Compute, Azure HDInsight, or a remote virtual machine.

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-target
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-set-up-training-targets

QUESTION 7

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

A. Mutual information
B. Pearson\’s correlation
C. Spearman correlation
D. Fisher Linear Discriminant Analysis

Correct Answer: C

Spearman\’s rank correlation coefficient assesses how well the relationship between two variables can be described using a monotonic function.

Note: Both Spearman\’s and Kendall\’s can be formulated as special cases of a more general correlation coefficient, and they are both appropriate in this scenario.

Scenario: 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.

Incorrect Answers:
B: The Spearman correlation between two variables is equal to the Pearson correlation between the rank values of those two variables; while Pearson\’s correlation assesses linear relationships, Spearman\’s correlation assesses monotonic relationships (whether linear or not).

References: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/feature-selectionmodules

QUESTION 8

HOTSPOT
You need to identify the methods for dividing the data according to the testing requirements. Which properties should you select? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Hot Area:

Correct Answer:

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

Box 1: Assign to folds
Use Assign to folds option when you want to divide the dataset into subsets of the data. This option is also useful when you want to create a custom number of folds for cross-validation, or to split rows into several groups.

Not Head: Use Head mode to get only the first n rows. This option is useful if you want to test a pipeline on a small number of rows, and don\\’t need the data to be balanced or sampled in any way.
Not Sampling: The Sampling option supports simple random sampling or stratified random sampling. This is useful if you want to create a smaller representative sample dataset for testing.

Box 2: Partition evenly
Specify the partitioner method: Indicate how you want data to be apportioned to each partition, using these options: Partition evenly: Use this option to place an equal number of rows in each partition. To specify the number of output partitions, type a whole number in the Specify number of folds to split evenly into the text box.

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/partition-and-sample

QUESTION 9

You create a binary classification model. You need to evaluate the model performance. Which two metrics can you use? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

A. relative absolute error
B. precision
C. accuracy
D. mean absolute error
E. coefficient of determination

Correct Answer: BC

The evaluation metrics available for binary classification models are Accuracy, Precision, Recall, F1 Score, and AUC.
Note: A very natural question is: `Out of the individuals whom the model, how many were classified correctly (TP)?\\’ This question can be answered by looking at the Precision of the model, which is the proportion of positives that are classified correctly

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

QUESTION 10

HOTSPOT
You deploy a model in Azure Container Instance. You must use the Azure Machine Learning SDK to call the model API. You need to invoke the deployed model using native SDK classes and methods. How should you complete the command? To answer, select the appropriate options in the answer areas.

NOTE: Each correct selection is worth one point.
Hot Area:

Correct Answer:

QUESTION 11

HOTSPOT
You have a dataset that contains 2,000 rows. You are building a machine learning classification model by using Azure Learning Studio. You add a Partition and Sample module to the experiment.

You need to configure the module. You must meet the following requirements:
1. Divide the data into subsets
2. Assign the rows into folds using a round-robin method
3. Allow rows in the dataset to be reused

How should you configure the module? To answer, select the appropriate options in the dialog box in the answer area. NOTE: Each correct selection is worth one point.
Hot Area:

Correct Answer:

Use the Split data into partitions option when you want to divide the dataset into subsets of the data. This option is also useful when you want to create a custom number of folds for cross-validation, or to split rows into several groups.

Add the Partition and Sample module to your experiment in Studio (classic), and connect the dataset.
For Partition or sample mode, select Assign to Folds. Use replacement in the partitioning: Select this option if you want the sampled row to be put back into the pool of rows for potential reuse. As a result, the same row might be assigned to several folds.

If you do not use a replacement (the default option), the sampled row is not put back into the pool of rows for potential reuse. As a result, each row can be assigned to only one fold. Randomized split: Select this option if you want rows to be randomly assigned to folds. If you do not select this option, rows are assigned to folds using the round-robin method.

References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/partition-and-sample

QUESTION 12

HOTSPOT
You need to configure the Edit Metadata module so that the structure of the datasets matches.
Which configuration options should you select? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
Hot Area:

Correct Answer:

Box 1: Floating point
Need floating point for Median values.
Scenario: 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.

Box 2: Unchanged Note: Select the Categorical option to specify that the values in the selected columns should be treated as categories. For example, you might have a column that contains the numbers 0,1, and 2, but know that the numbers actually mean “Smoker”, “Nonsmoker” and “Unknown”. In that case, by flagging the column as categorical you can ensure that the values are not used in numeric calculations, only to group data.

QUESTION 13

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.

After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear on the review screen.

You are a data scientist using Azure Machine Learning Studio.
You need to normalize values to produce an output column into bins to predict a target column.
Solution: Apply a Quantiles binning mode with a PQuantile normalization. Does the solution meet the goal?

A. Yes
B. No

Correct Answer: B

Use the Entropy MDL binning mode which has a target column.

References: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/group-data-into-bins

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Vendor: Microsoft
Certifications: Role-based
Exam Code: DP-100
Exam Name: Designing and Implementing a Data Science Solution on Azure

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You need to invest time in these practice tests, pass all the questions, and read explanations. This will give you the advantage to pass the exam.

QUESTION 1
DRAG DROP
An organization uses Azure Machine Learning service and wants to expand its use of machine learning.
You have the following compute environments. The organization does not want to create another compute
environment.

examprepwebinar dp-100 exam questions-q1

You need to determine which compute environment to use for the following scenarios.
Which compute types should you use? To answer, drag the appropriate compute environments to the correct scenarios.
Each computing environment 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.
Select and Place:

examprepwebinar dp-100 exam questions-q1-2

Box 1: nb_server Box 2: mlc_cluster With Azure Machine Learning, you can train your model on a variety of resources
or environments, collectively referred to as compute targets. A computing target can be a local machine or a cloud
resource, such as an Azure Machine Learning Compute, Azure HDInsight, or a remote virtual machine.

examprepwebinar dp-100 exam questions-q1-3

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-target
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-set-up-training-targets

QUESTION 2
HOTSPOT
You are tuning a hyperparameter for an algorithm. The following table shows a data set with different hyperparameter,
training error, and validation errors.

examprepwebinar dp-100 exam questions-q2

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

examprepwebinar dp-100 exam questions-q2-2

Correct Answer:

examprepwebinar dp-100 exam questions-q2-3

Box 1: 4
Choose the one which has lower training and validation error and also the closest match.
Minimize variance (the difference between validation error and train error).
Box 2: 5
Minimize variance (the difference between validation error and train error).
Reference:
https://medium.com/comet-ml/organizing-machine-learning-projects-project-management-guidelines-2d2b85651bbd

QUESTION 3
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. NOTE: Each correct selection is worth one point.
A. Create Scatterplot
B. Summarize Data
C. Clip Values
D. Replace Discrete Values
E. Build Counting Transform
Correct Answer: ABC
B: To have a global view, the summarize data module can be used. Add the module and connect it to the data set that
needs to be visualized.
A: One way to quickly identify Outliers visually is to create scatter plots.
C: The easiest way to treat the outliers in Azure ML is to use the Clip Values module. It can identify and optionally
replace data values that are above or below a specified threshold.
You can use the Clip Values module in Azure Machine Learning Studio, to identify and optionally replace data values
that are above or below a specified threshold. This is useful when you want to remove outliers or replace them with a
mean, a constant, or other substitute value.
References: https://blogs.msdn.microsoft.com/azuredev/2017/05/27/data-cleansing-tools-in-azure-machine-learning/
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clip-values Question Set 3


QUESTION 4
You have a dataset created for multiclass classification tasks that contains a normalized numerical feature set with
10,000 data points and 150 features.
You use 75 percent of the data points for training and 25 percent for testing. You are using the scikit-learn machine
learning library in Python. You use X to denote the feature set and Y to denote class labels.
You create the following Python data frames:

examprepwebinar dp-100 exam questions-q4

You need to apply the Principal Component Analysis (PCA) method to reduce the dimensionality of the feature set to 10
features in both training and testing sets.
How should you complete the code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Hot Area:

examprepwebinar dp-100 exam questions-q4-2

Box 1: PCA(n_components = 10)
Need to reduce the dimensionality of the feature set to 10 features in both training and testing sets.
Example:
from sklearn.decomposition import PCA
pca = PCA(n_components=2) ;2 dimensions
principalComponents = pca.fit_transform(x)
Box 2: pca
fit_transform(X[, y])fits the model with X and apply the dimensionality reduction on X.
Box 3: transform(x_test)
transform(X) applies dimensionality reduction to X.
References:
https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html

QUESTION 5
HOTSPOT
You deploy a model in Azure Container Instance.
You must use the Azure Machine Learning SDK to call the model API.
You need to invoke the deployed model using native SDK classes and methods.
How should you complete the command? To answer, select the appropriate options in the answer areas.
NOTE: Each correct selection is worth one point.
Hot Area:

examprepwebinar dp-100 exam questions-q5

Box 1: from azureml.core.webservice import Webservice
The following code shows how to use the SDK to update the model, environment, and entry script for a web service to Azure Container Instances:
from azureml.core import Environment
from azureml.core.webservice import Webservice
from azureml.core.model import Model, InferenceConfig
Box 2: predictions = service.run(input_json)
Example: The following code demonstrates sending data to the service:
import json
test_sample = json.dumps({\\’data\\’: [ [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]
]})
test_sample = bytes(test_sample, encoding=\\’utf8\\’)
prediction = service.run(input_data=test_sample) print(prediction)
Reference: https://docs.microsoft.com/bs-latn-ba/azure/machine-learning/how-to-deploy-azure-container-instance
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-deployment

QUESTION 6
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains
a unique solution that might meet the stated goals. Some question sets might have more than one correct solution,
while
others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not
appear in the review screen.
You create a model to forecast weather conditions based on historical data.
You need to create a pipeline that runs a processing script to load data from a datastore and pass the processed data to
a machine learning model training script.
Solution: Run the following code:

examprepwebinar dp-100 exam questions-q6

Does the solution meet the goal?
A. Yes
B. No
Correct Answer: A
The two steps are present: process_step and train_step
Data_input correctly references the data in the data store.
Note:
Data used in pipeline can be produced by one step and consumed in another step by providing a PipelineData object as
an output of one step and an input of one or more subsequent steps.
PipelineData objects are also used when constructing Pipelines to describe step dependencies. To specify that a step
requires the output of another step as input, use a PipelineData object in the constructor of both steps.
For example, the pipeline train step depends on the process_step_output output of the pipeline process step:
from azureml.pipeline.core import Pipeline, PipelineData from azureml.pipeline.steps import PythonScriptStep
datastore = ws.get_default_datastore()
process_step_output = PipelineData(“processed_data”, datastore=datastore) process_step =
PythonScriptStep(script_name=”process.py”, arguments=[“–data_for_train”, process_step_output],
outputs=[process_step_output],
compute_target=aml_compute,
source_directory=process_directory)
train_step = PythonScriptStep(script_name=”train.py”,
arguments=[“–data_for_train”, process_step_output],
inputs=[process_step_output],
compute_target=aml_compute,
source_directory=train_directory)
pipeline = Pipeline(workspace=ws, steps=[process_step, train_step])
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azure-mlpy


QUESTION 7
HOTSPOT
You are using Azure Machine Learning to train machine learning models. You need to compute the target on which to
remotely run the training script.
You run the following Python code:

examprepwebinar dp-100 exam questions-q7

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

examprepwebinar dp-100 exam questions-q7-2

Correct Answer:

examprepwebinar dp-100 exam questions-q7-3

Box 1: Yes
The compute is created within your workspace region as a resource that can be shared with other users.
Box 2: Yes
It is displayed as a compute cluster.
View compute targets
1.
To see all compute targets for your workspace, use the following steps:
2.
Navigate to Azure Machine Learning studio.
3.
Under Manage, select Compute.
4.
Select tabs at the top to show each type of computing target.

examprepwebinar dp-100 exam questions-q7-4

Box 3: Yes
min_nodes is not specified, so it defaults to 0.
Reference:
https://docs.microsoft.com/en-us/python/api/azuremlcore/azureml.core.compute.amlcompute.amlcomputeprovisioningconfiguration
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-studio

QUESTION 8
HOTSPOT
You are performing a classification task in Azure Machine Learning Studio.
You must prepare balanced testing and training samples based on a provided data set.
You need to split the data with a 0.75:0.25 ratio.
Which value should you use for each parameter? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Hot Area:

examprepwebinar dp-100 exam questions-q8

Correct Answer:

examprepwebinar dp-100 exam questions-q8-2

Box 1: Split rows
Use the Split Rows option if you just 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 stratified sampling. In stratified sampling, you
must select a single column of data for which you want values to be apportioned equally among the two result datasets.
Box 2: 0.75
If you specify a number as a percentage, or if you use a string that contains the “%” character, the value is interpreted
as a percentage. All percentage values must be within the range (0, 100), not including the values 0 and 100.
Box 3: Yes
To ensure splits are balanced.
Box 4: No
If you use the option for a stratified split, the output datasets can be further divided by subgroups, by selecting a strata
column.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/split-data

QUESTION 9
You use the Azure Machine Learning Studio to build a machine learning experiment.
You need to divide data into two distinct datasets.
Which module should you use?
A. Assign Data to Clusters
B. Load Trained Model
C. Partition and Sample
D. Tune Model-Hyperparameters
Correct Answer: C
Partition and Sample with the Stratified split option outputs multiple datasets, partitioned using the rules you specified.
References: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/partition-and-sample

QUESTION 10
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?
A. Relative Absolute Error
B. AUC Curve
C. Mean Absolute Error
D. Relative Squared Error
E. Accuracy
F. Root Mean Square Error
Correct Answer: B
One can inspect the true positive rate vs. the false positive rate in the Receiver Operating Characteristic (ROC) curve
and the corresponding Area Under the Curve (AUC) value. The closer this curve is to the upper left corner, the better
the
classifier\\’s performance is (that is maximizing the true positive rate while minimizing the false positive rate). Curves
that are close to the diagonal of the plot, result from classifiers that tend to make predictions that are close to random
guessing.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance#evaluating-a-binaryclassification-model

QUESTION 11
You are solving 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?
A. k=0.5
B. k=0.01
C. k=5
D. k=1
Correct Answer: C
Leave One Out (LOO) cross-validation
Setting K = n (the number of observations) yields n-fold and is called leave-one-out cross-validation (LOO), a special
case of the K-fold approach.
LOO CV is sometimes useful but typically doesn\\’t shake up the data enough. The estimates from each fold are highly
correlated and hence their average can have high variance. This is why the usual choice is K=5 or 10. It provides a
good
compromise for the bias-variance tradeoff.

QUESTION 12
You are creating a machine learning model. You have a dataset that contains null rows.
You need to use the Clean Missing Data module in Azure Machine Learning Studio to identify and resolve the null and
missing data in the dataset.
Which parameter should you use?
A. Replace with mean
B. Remove entire column
C. Remove entire row
D. Hot Deck
E. Custom substitution value
F. Replace with mode
Correct Answer: C
Remove entire row: Completely removes any row in the dataset that has one or more missing values. This is useful if
the missing value can be considered randomly missing.
References: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data

QUESTION 13
You are implementing a machine learning model to predict stock prices.
The model uses a PostgreSQL database and requires GPU processing.
You need to create a virtual machine that is pre-configured with the required tools.
What should you do?
A. Create a Data Science Virtual Machine (DSVM) Windows edition.
B. Create a Geo Al Data Science Virtual Machine (Geo-DSVM) Windows edition.
C. Create a Deep Learning Virtual Machine (DLVM) Linux edition.
D. Create a Deep Learning Virtual Machine (DLVM) Windows edition.
Correct Answer: A
In the DSVM, your training models can use deep learning algorithms on hardware that\\’s based on graphics processing
units (GPUs).
PostgreSQL is available for the following operating systems: Linux (all recent distributions), 64-bit installers available for
macOS (OS X) version 10.6 and newer? Windows (with installers available for 64-bit version; tested on latest versions
and back to Windows 2012 R2.
Incorrect Answers:
B: The Azure Geo AI Data Science VM (Geo-DSVM) delivers geospatial analytics capabilities from Microsoft\\’s Data Science VM. Specifically, this VM extends the AI and data science toolkits in the Data Science VM by adding ESRI\\’s
market-leading ArcGIS Pro Geographic Information System.
C, D: DLVM is a template on top of the DSVM image. In terms of the packages, GPU drivers, etc are all there in the DSVM
image. Mostly it is for convenience during creation where we only allow DLVM to be created on GPU VM instances on
Azure.
Reference: https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview

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