How Many Questions Of DP-100 Practice Question
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Online DP-100 free questions and answers of New Version:
NEW QUESTION 1
You are building a binary classification model by using a supplied training set. The training set is imbalanced between two classes.
You need to resolve the data imbalance.
What are three possible ways to achieve this goal? Each correct answer presents a complete solution NOTE: Each correct selection is worth one point.
- A. Penalize the classification
- B. Resample the data set using under sampling or oversampling
- C. Generate synthetic samples in the minority class.
- D. Use accuracy as the evaluation metric of the model.
- E. Normalize the training feature set.
Answer: ABD
Explanation:
References:
https://machinelearningmastery.com/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/
NEW QUESTION 2
You need to use the Python language to build a sampling strategy for the global penalty detection models. 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.
Solution:
Box 1: import pytorch as deeplearninglib Box 2: ..DistributedSampler(Sampler).. DistributedSampler(Sampler):
Sampler that restricts data loading to a subset of the dataset.
It is especially useful in conjunction with class:`torch.nn.parallel.DistributedDataParallel`. In such case, each process can pass a DistributedSampler instance as a DataLoader sampler, and load a subset of the original dataset that is exclusive to it.
Scenario: Sampling must guarantee mutual and collective exclusively between local and global segmentation models that share the same features.
Box 3: optimizer = deeplearninglib.train. GradientDescentOptimizer(learning_rate=0.10)
Does this meet the goal?
- A. Yes
- B. Not Mastered
Answer: A
NEW QUESTION 3
You are developing a machine learning, experiment by using Azure. The following images show the input and output of a machine learning experiment:
Use the drop-down menus to select the answer choice that answers each question based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.
Solution:
Does this meet the goal?
- A. Yes
- B. Not Mastered
Answer: A
NEW QUESTION 4
You plan to use a Deep Learning Virtual Machine (DLVM) to train deep learning models using Compute Unified Device Architecture (CUDA) computations.
You need to configure the DLVM to support CUDA. What should you implement?
- A. Intel Software Guard Extensions (Intel SGX) technology
- B. Solid State Drives (SSD)
- C. Graphic Processing Unit (GPU)
- D. Computer Processing Unit (CPU) speed increase by using overcloking
- E. High Random Access Memory (RAM) configuration
Answer: C
Explanation:
A Deep Learning Virtual Machine is a pre-configured environment for deep learning using GPU instances.
References:
https://azuremarketplace.microsoft.com/en-au/marketplace/apps/microsoft-ads.dsvm-deep-learning
NEW QUESTION 5
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 an Azure Machine Learning service datastore in a workspace. The datastore contains the following files:
• /data/2018/Q1.csv
• /data/2018/Q2.csv
• /data/2018/Q3.csv
• /data/2018/Q4.csv
• /data/2019/Q1.csv
All files store data in the following format: id,f1,f2i
1,1.2,0
2,1,1,
1 3,2.1,0
You run the following code:
You need to create a dataset named training_data and load the data from all files into a single data frame by using the following code:
Solution: Run the following code:
Does the solution meet the goal?
- A. Yes
- B. No
Answer: B
Explanation:
Use two file paths.
Use Dataset.Tabular_from_delimeted, instead of Dataset.File.from_files as the data isn't cleansed. Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-register-datasets
NEW QUESTION 6
You create a Python script that runs a training experiment in Azure Machine Learning. The script uses the Azure Machine Learning SDK for Python.
You must add a statement that retrieves the names of the logs and outputs generated by the script. You need to reference a Python class object from the SDK for the statement.
Which class object should you use?
- A. Run
- B. ScripcRunConfig
- C. Workspace
- D. Experiment
Answer: A
Explanation:
A run represents a single trial of an experiment. Runs are used to monitor the asynchronous execution of a trial, log metrics and store output of the trial, and to analyze results and access artifacts generated by the trial.
The run Class get_all_logs method downloads all logs for the run to a directory. Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.run(class)
NEW QUESTION 7
You publish a batch inferencing pipeline that will be used by a business application.
The application developers need to know which information should be submitted to and returned by the REST interface for the published pipeline.
You need to identify the information required in the REST request and returned as a response from the published pipeline.
Which values should you use in the REST request and to expect in the response? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Solution:
Box 1: JSON containing an OAuth bearer token Specify your authentication header in the request.
To run the pipeline from the REST endpoint, you need an OAuth2 Bearer-type authentication header. Box 2: JSON containing the experiment name
Add a JSON payload object that has the experiment name. Example:
rest_endpoint = published_pipeline.endpoint response = requests.post(rest_endpoint, headers=auth_header, json={"ExperimentName": "batch_scoring",
"ParameterAssignments": {"process_count_per_node": 6}}) run_id = response.json()["Id"]
Box 3: JSON containing the run ID
Make the request to trigger the run. Include code to access the Id key from the response dictionary to get the value of the run ID.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-pipeline-batch-scoring-classification
Does this meet the goal?
- A. Yes
- B. Not Mastered
Answer: A
NEW QUESTION 8
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 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:
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_list('Label Values', label_vals) Does the solution meet the goal?
- A. Yes
- B. No
Answer: A
Explanation:
run.log_list log a list of values to the run with the given name using log_list. Example: run.log_list("accuracies", [0.6, 0.7, 0.87])
Note:
Data= pd.read_csv('data.csv')
Data is read into a pandas.DataFrame, which is a two-dimensional, size-mutable, potentially heterogeneous tabular data.
label_vals =data['label'].unique
label_vals contains a list of unique label values. Reference:
https://www.element61.be/en/resource/azure-machine-learning-services-complete-toolbox-ai https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.run(class) https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html
NEW QUESTION 9
You are building recurrent neural network to perform a binary classification.
The training loss, validation loss, training accuracy, and validation accuracy of each training epoch has been provided. You need to identify whether the classification model is over fitted.
Which of the following is correct?
- A. The training loss increases while the validation loss decreases when training the model.
- B. The training loss decreases while the validation loss increases when training the model.
- C. The training loss stays constant and the validation loss decreases when training the model.
- D. 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.
Answer: B
Explanation:
An overfit model is one where performance on the train set is good and continues to improve, whereas performance on the validation set improves to a point and then begins to degrade.
References:
https://machinelearningmastery.com/diagnose-overfitting-underfitting-lstm-models/
NEW QUESTION 10
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?
- A. Paired t-test with a two-tail option
- B. Unpaired t-test with a two tail option
- C. Paired t-test with a one-tail option
- D. Unpaired t-test with a one-tail option
Answer: A
Explanation:
Choose a one-tail or two-tail test. The default is a two-tailed test. This is the most common type of test, in which the expected distribution is symmetric around zero.
Example: Type I error of unpaired and paired two-sample t-tests as a function of the correlation. The simulated random numbers originate from a bivariate normal distribution with a variance of 1.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/test-hypothesis-using-t-test https://en.wikipedia.org/wiki/Student%27s_t-test
NEW QUESTION 11
You are developing a hands-on workshop to introduce Docker for Windows to attendees. You need to ensure that workshop attendees can install Docker on their devices.
Which two prerequisite components should attendees install on the devices? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
- A. Microsoft Hardware-Assisted Virtualization Detection Tool
- B. Kitematic
- C. BIOS-enabled virtualization
- D. VirtualBox
- E. Windows 10 64-bit Professional
Answer: CE
Explanation:
C: Make sure your Windows system supports Hardware Virtualization Technology and that virtualization is enabled.
Ensure that hardware virtualization support is turned on in the BIOS settings. For example:
E: To run Docker, your machine must have a 64-bit operating system running Windows 7 or higher. References:
https://docs.docker.com/toolbox/toolbox_install_windows/ https://blogs.technet.microsoft.com/canitpro/2015/09/08/step-by-step-enabling-hyper-v-for-use-on-windows-10/
NEW QUESTION 12
You need to define a process for penalty event detection.
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.
Solution:
Does this meet the goal?
- A. Yes
- B. Not Mastered
Answer: A
NEW QUESTION 13
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.
Solution:
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- https://docs.microsoft.com/en-us/azure/architecture/data-guide/scenarios/interactive-data-exploration
Does this meet the goal?
- A. Yes
- B. Not Mastered
Answer: A
NEW QUESTION 14
You are performing feature scaling by using the scikit-learn Python library for x.1 x2, and x3 features. Original and scaled data is shown in the following image.
Use the drop-down menus to select the answer choice that answers each question based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.
Solution:
Box 1: StandardScaler
The StandardScaler assumes your data is normally distributed within each feature and will scale them such that the distribution is now centred around 0, with a standard deviation of 1.
Example:
All features are now on the same scale relative to one another. Box 2: Min Max Scaler
Notice that the skewness of the distribution is maintained but the 3 distributions are brought into the same scale so that they overlap.
Box 3: Normalizer
References:
http://benalexkeen.com/feature-scaling-with-scikit-learn/
Does this meet the goal?
- A. Yes
- B. Not Mastered
Answer: A
NEW QUESTION 15
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 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 Scale and Reduce sampling mode.
Does the solution meet the goal?
- A. Yes
- B. No
Answer: B
Explanation:
Instead use the Synthetic Minority Oversampling Technique (SMOTE) sampling mode.
Note: 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.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote
NEW QUESTION 16
You create an Azure Machine Learning workspace and a new Azure DevOps organization. You register a
model in the workspace and deploy the model to the target environment.
All new versions of the model registered in the workspace must automatically be deployed to the target environment.
You need to configure Azure Pipelines to deploy the model.
Which four 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.
Solution:
Graphical user interface, text, application, email Description automatically generated
Step 1: Create an Azure DevOps project
Step 2: Create a release pipeline
Sign in to your Azure DevOps organization and navigate to your project. 
Go to Pipelines, and then select New pipeline.Step 3: Install the Machine Learning extension for Azure Pipelines
You must install and configure the Azure CLI and ML extension.
Step 4: Create a service connection
How to set up your service connection
Graphical user interface, text, application, email Description automatically generated
Select AzureMLWorkspace for the scope level, then fill in the following subsequent parameters. Graphical user interface, text, application Description automatically generated
Note: How to enable model triggering in a release pipeline
Go to your release pipeline and add a new artifact. Click on AzureML Model artifact then select the appropriate AzureML service connection and select from the available models in your workspace. Enable the deployment trigger on your model artifact as shown here. Every time a new version of that model is registered, a release pipeline will be triggered.Reference:
https://marketplace.visualstudio.com/items?itemName=ms-air-aiagility.vss-services-azureml https://docs.microsoft.com/en-us/azure/devops/pipelines/targets/azure-machine-learning
Does this meet the goal?
- A. Yes
- B. Not Mastered
Answer: A
NEW QUESTION 17
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 using Azure Machine Learning to run an experiment that trains a classification model.
You want to use Hyperdrive to find parameters that optimize the AUC metric for the model. You configure a HyperDriveConfig for the experiment by running the following code:
You plan to use this configuration to run a script that trains a random forest model and then tests it with validation data. The label values for the validation data are stored in a variable named y_test variable, and the predicted probabilities from the model are stored in a variable named y_predicted.
Solution: Run the following code:
Does the solution meet the goal?
- A. Yes
- B. No
Answer: A
NEW QUESTION 18
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