[Jan 03, 2025] Fully Updated Microsoft Azure (DP-100) Certification Sample Questions [Q14-Q31]

[Jan 03, 2025] Fully Updated Microsoft Azure (DP-100) Certification Sample Questions

Latest Microsoft DP-100 Real Exam Dumps PDF

NEW QUESTION # 14
space and set up a development environment. You plan to train a deep neural network (DNN) by using the Tensorflow framework and by using estimators to submit training scripts.
You must optimize computation speed for training runs.
You need to choose the appropriate estimator to use as well as the appropriate training compute target configuration.
Which values should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.dnn

NEW QUESTION # 15

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:

Explanation:

NEW QUESTION # 16
You manage an Azure Machine Learning workspace.
You must define the execution environments for your jobs and encapsulate the dependencies for your code.
You need to configure the environment from a Docker build context.
How should you complete the rode segment? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 17
You have an Azure Machine learning workspace. The workspace contains a dataset with data in a tabular form.
You plan to use the Azure Machine Learning SDK for Python vl to create a control script that will load the dataset into a pandas dataframe in preparation for model training The script will accept a parameter designating the dataset You need to complete the script.
How should you complete the script? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

NEW QUESTION # 18
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:

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.

Answer:

Explanation:

Explanation:
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

NEW QUESTION # 19
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 train a classification model by using a logistic regression algorithm.
You must be able to explain the model's predictions by calculating the importance of each feature, both as an overall global relative importance value and as a measure of local importance for a specific set of predictions.
You need to create an explainer that you can use to retrieve the required global and local feature importance values.
Solution: Create a TabularExplainer.
Does the solution meet the goal?

A. No
B. Yes

Answer: A

Explanation:
Explanation
Instead use Permutation Feature Importance Explainer (PFI).
Note 1:

Note 2: Permutation Feature Importance Explainer (PFI): Permutation Feature Importance is a technique used to explain classification and regression models. At a high level, the way it works is by randomly shuffling data one feature at a time for the entire dataset and calculating how much the performance metric of interest changes. The larger the change, the more important that feature is. PFI can explain the overall behavior of any underlying model but does not explain individual predictions.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability

NEW QUESTION # 20
You create an Azure Machine Learning workspace.
You must implement dedicated compute for model training in the workspace by using Azure Synapse compute resources. The solution must attach the dedicated compute and start an Azure Synapse session.
You need to implement the compute resources.
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.

Answer:

Explanation:

1 - Create a linked service by using Azure Machine Learning studio.
2 - Create an Azure Synapse workspace by using the Azures portal.
3 - Create an Apache Spark pool by using the Azure portal.

NEW QUESTION # 21
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 are creating a new experiment in Azure Learning learning Studio.
One class has a much smaller number of observations than the other classes in the training 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?

A. No
B. Yes

Answer: B

Explanation:
SMOTE is used to increase the number of underepresented cases in a dataset used for machine learning. SMOTE is a better way of increasing the number of rare cases than simply duplicating existing cases.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote

NEW QUESTION # 22
You deploy a model as an Azure Machine Learning real-time web service using the following code.

The deployment fails.
You need to troubleshoot the deployment failure by determining the actions that were performed during deployment and identifying the specific action that failed.
Which code segment should you run?

A. service.serialize()
B. service.get_logs()
C. service.update_deployment_state()
D. service.state

Answer: B

Explanation:
Explanation
You can print out detailed Docker engine log messages from the service object. You can view the log for ACI, AKS, and Local deployments. The following example demonstrates how to print the logs.
# if you already have the service object handy
print(service.get_logs())
# if you only know the name of the service (note there might be multiple services with the same name but different version number) print(ws.webservices['mysvc'].get_logs()) Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-deployment

NEW QUESTION # 23
You create a multi-class image classification deep learning experiment by using the PyTorch framework. You plan to run the experiment on an Azure Compute cluster that has nodes 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? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Answer:

Explanation:

Explanation:

Step 1: Configure a DataTransferStep() to fetch new image data...
Step 2: Configure a PythonScriptStep() to run image_resize.y on the cpu-compute compute target.
Step 3: Configure the EstimatorStep() to run training script on the gpu_compute computer target.
The PyTorch estimator provides a simple way of launching a PyTorch training job on a compute target.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-pytorch

NEW QUESTION # 24
You train a machine learning model by using Aunt Machine Learning.
You use the following training script m Python to log an accuracy value.

You must use a Python script to define a sweep job.
You need to provide the primary metric and goal you want hyper parameter tuning to optimize.
How should you complete the Python script? To answer select the appropriate options in the answer area NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 25
You perform hyper parameter tuning with Azure Machine Learning.
You create the following Python code:

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

Answer:

Explanation:

Explanation

NEW QUESTION # 26
The finance team asks you to train a model using data in an Azure Storage blob container named finance-data.
You need to register the container as a datastore in an Azure Machine Learning workspace and ensure that an error will be raised if the container does not exist.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.datastore.datastore

NEW QUESTION # 27
You are hired as a data scientist at a winery. The previous data scientist used Azure Machine Learning.
You need to review the models and explain how each model makes decisions.
Which explainer modules should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Reference:
https://medium.com/microsoftazure/automated-and-interpretable-machine-learning-d07975741298

NEW QUESTION # 28
You are building an experiment using the Azure Machine Learning designer.
You split a dataset into training and testing sets. You select the Two-Class Boosted Decision Tree as the algorithm.
You need to determine the Area Under the Curve (AUC) of the model.
Which three modules should you use in sequence? To answer, move the appropriate modules from the list of modules to the answer area and arrange them in the correct order.

Answer:

Explanation:

Explanation:

Step 1: Train Model
Two-Class Boosted Decision Tree
First, set up the boosted decision tree model.
1. Find the Two-Class Boosted Decision Tree module in the module palette and drag it onto the canvas.
2. Find the Train Model module, drag it onto the canvas, and then connect the output of the Two-Class Boosted Decision Tree module to the left input port of the Train Model module.
The Two-Class Boosted Decision Tree module initializes the generic model, and Train Model uses training data to train the model.
3. Connect the left output of the left Execute R Script module to the right input port of the Train Model module (in this tutorial you used the data coming from the left side of the Split Data module for training).
This portion of the experiment now looks something like this:

Step 2: Score Model
Score and evaluate the models
You use the testing data that was separated out by the Split Data module to score our trained models. You can then compare the results of the two models to see which generated better results.
Add the Score Model modules
1. Find the Score Model module and drag it onto the canvas.
2. Connect the Train Model module that's connected to the Two-Class Boosted Decision Tree module to the left input port of the Score Model module.
3. Connect the right Execute R Script module (our testing data) to the right input port of the Score Model module.

Step 3: Evaluate Model
To evaluate the two scoring results and compare them, you use an Evaluate Model module.
1. Find the Evaluate Model module and drag it onto the canvas.
2. Connect the output port of the Score Model module associated with the boosted decision tree model to the left input port of the Evaluate Model module.
3. Connect the other Score Model module to the right input port.

NEW QUESTION # 29
You are a data scientist building a deep convolutional neural network (CNN) for image classification.
The CNN model you built 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.
NOTE: Each correct selection is worth one point.

A. Add an additional dense layer with 64 input units
B. Use training data augmentation
C. Add L1/L2 regularization.
D. Add an additional dense layer with 512 input units.
E. Reduce the amount of training data.

Answer: A,D

NEW QUESTION # 30
You configure a Deep Learning Virtual Machine for Windows.
You need to recommend tools and frameworks to perform the following:
Build deep neural network (DNN) models
Perform interactive data exploration and visualization
Which tools and frameworks should you recommend? To answer, drag the appropriate tools to the correct tasks. Each tool 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:

Explanation:

Explanation

Box 1: Vowpal Wabbit
Use the Train Vowpal Wabbit Version 8 module in Azure Machine Learning Studio (classic), to create a machine learning model by using Vowpal Wabbit.
Box 2: PowerBI Desktop
Power BI Desktop is a powerful visual data exploration and interactive reporting tool BI is a name given to a modern approach to business decision making in which users are empowered to find, explore, and share insights from data across the enterprise.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/train-vowpal-wabbit-version-8
https://docs.microsoft.com/en-us/azure/architecture/data-guide/scenarios/interactive-data-exploration