How Many Questions Of DP-100 Preparation Labs

NEW QUESTION 1

You deploy a real-time inference service for a trained model.
The deployed model supports a business-critical application, and it is important to be able to monitor the data submitted to the web service and the predictions the data generates.
You need to implement a monitoring solution for the deployed model using minimal administrative effort. What should you do?

• A. View the explanation for the registered model in Azure ML studio.
• B. Enable Azure Application Insights for the service endpoint and view logged data in the Azure portal.
• C. Create an ML Flow tracking URI that references the endpoint, and view the data logged by ML Flow.
• D. View the log files generated by the experiment used to train the model.

Answer: B

Explanation:
Configure logging with Azure Machine Learning studio
You can also enable Azure Application Insights from Azure Machine Learning studio. When you're ready to deploy your model as a web service, use the following steps to enable Application Insights:
* 1. Sign in to the studio at https://ml.azure.com.
* 2. Go to Models and select the model you want to deploy.
* 3. Select +Deploy.
* 4. Populate the Deploy model form.
* 5. Expand the Advanced menu.
* 6. Select Enable Application Insights diagnostics and data collection.

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-enable-app-insights

NEW QUESTION 2

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 3

You have a multi-class image classification deep learning model that uses a set of labeled photographs. You create the following code to select hyperparameter values when training the model.

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

• A. Yes
• B. Not Mastered

Answer: A

NEW QUESTION 4

You create a training pipeline using the Azure Machine Learning designer. You upload a CSV file that contains the data from which you want to train your model.
You need to use the designer to create a pipeline that includes steps to perform the following tasks:
Select the training features using the pandas filter method.
Train a model based on the naive_bayes.GaussianNB algorithm.
Return only the Scored Labels column by using the query SELECT [Scored Labels] FROM t1; Which modules should you use? To answer, drag the appropriate modules to the appropriate locations. Each
module name 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.

• A. Yes
• B. Not Mastered

Answer: A

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 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 MimicExplainer. Does the solution meet the goal?

• A. Yes
• B. No

Answer: B

Explanation:
Instead use Permutation Feature Importance Explainer (PFI).
Note 1: Mimic explainer is based on the idea of training global surrogate models to mimic blackbox models. A global surrogate model is an intrinsically interpretable model that is trained to approximate the predictions of any black box model as accurately as possible. Data scientists can interpret the surrogate model to draw conclusions about the black box model.
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 6

You train and register a model in your Azure Machine Learning workspace.
You must publish a pipeline that enables client applications to use the model for batch inferencing. You must use a pipeline with a single ParallelRunStep step that runs a Python inferencing script to get predictions from the input data.
You need to create the inferencing script for the ParallelRunStep pipeline step.
Which two functions should you include? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

• A. run(mini_batch) D
• B. main()
• C. batch()
• D. init()
• E. score(mini_batch)

Answer: AD

Explanation:
Reference:
https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/machine-learningpipelin

NEW QUESTION 7

You need to modify the inputs for the global penalty event model to address the bias and variance issue. 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.

• A. Yes
• B. Not Mastered

Answer: A

NEW QUESTION 8

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:

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?

• A. Option A
• B. Option B
• C. Option C
• D. Option D
• E. Option E

Answer: B

Explanation:
Besides passing the dataset through the inputs parameter in the estimator, you can also pass the dataset through script_params and get the data path (mounting point) in your training script via arguments. This way, you can keep your training script independent of azureml-sdk. In other words, you will be able use the same training script for local debugging and remote training on any cloud platform.
Example:
from azureml.train.sklearn import SKLearn script_params = {
# mount the dataset on the remote compute and pass the mounted path as an argument to the training script '--data-folder': mnist_ds.as_named_input('mnist').as_mount(),
'--regularization': 0.5
}
est = SKLearn(source_directory=script_folder, script_params=script_params, compute_target=compute_target, environment_definition=env, entry_script='train_mnist.py')
# Run the experiment
run = experiment.submit(est) run.wait_for_completion(show_output=True) Reference:
https://docs.microsoft.com/es-es/azure/machine-learning/how-to-train-with-datasets

NEW QUESTION 9

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 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?

• A. Yes
• B. No

Answer: B

Explanation:
Those are metrics for evaluating classification models, instead use: Mean Absolute Error, Root Mean Absolute Error, Relative Absolute Error, Relative Squared Error, and the Coefficient of Determination.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model

NEW QUESTION 10

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 have a Python script named train.py in a local folder named scripts. The script trains a regression model by using scikit-learn. The script includes code to load a training data file which is also located in the scripts folder.
You must run the script as an Azure ML experiment on a compute cluster named aml-compute.
You need to configure the run to ensure that the environment includes the required packages for model training. You have instantiated a variable named aml-compute that references the target compute cluster.
Solution: Run the following code:

Does the solution meet the goal?

• A. Yes
• B. No

Answer: B

Explanation:
The scikit-learn estimator provides a simple way of launching a scikit-learn training job on a compute target. It is implemented through the SKLearn class, which can be used to support single-node CPU training.
Example:
from azureml.train.sklearn import SKLearn
}
estimator = SKLearn(source_directory=project_folder, compute_target=compute_target, entry_script='train_iris.py'
)
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-scikit-learn

NEW QUESTION 11

You create an Azure Machine Learning workspace named workspace1. You assign a custom role to a user of workspace1.
The custom role has the following JSON definition:

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

• A.

Answer:

Explanation:
Graphical user interface, text, application, email Description automatically generated
Box 1: No
The actions listed in NotActions are prohibited.
If the roles include Actions that have a wildcard (*), the effective permissions are computed by subtracting the NotActions from the allowed Actions.
Box 2: No
Deleting compute resources in the workspace is in the NotActions list. Box 3: Yes
Writing metrics is not listed in NotActions. Reference:
https://docs.microsoft.com/en-us/azure/role-based-access-control/overview#how-azure-rbac-determines-if-a-use

NEW QUESTION 12

A set of CSV files contains sales records. All the CSV files have the same data schema.
Each CSV file contains the sales record for a particular month and has the filename sales.csv. Each file in stored in a folder that indicates the month and year when the data was recorded. The folders are in an Azure blob container for which a datastore has been defined in an Azure Machine Learning workspace. The folders are organized in a parent folder named sales to create the following hierarchical structure:

At the end of each month, a new folder with that month’s sales file is added to the sales folder.
You plan to use the sales data to train a machine learning model based on the following requirements:
You must define a dataset that loads all of the sales data to date into a structure that can be easily converted to a dataframe.
You must be able to create experiments that use only data that was created before a specific previous month, ignoring any data that was added after that month.
You must register the minimum number of datasets possible.
You need to register the sales data as a dataset in Azure Machine Learning service workspace. What should you do?

• A. Create a tabular dataset that references the datastore and explicitly specifies each 'sales/mm-yyyy/ sales.csv' file every mont
• B. Register the dataset with the name sales_dataset each month, replacing the existing dataset and specifying a tag named month indicating the month and year it was registere
• C. Use this dataset for all experiments.
• D. Create a tabular dataset that references the datastore and specifies the path 'sales/*/sales.csv', register the dataset with the name sales_dataset and a tag named month indicating the month and year it was registered, and use this dataset for all experiments.
• E. Create a new tabular dataset that references the datastore and explicitly specifies each 'sales/mm-yyyy/ sales.csv' file every mont
• F. Register the dataset with the name sales_dataset_MM-YYYY each month with appropriate MM and YYYY values for the month and yea
• G. Use the appropriate month-specific dataset for experiments.
• H. Create a tabular dataset that references the datastore and explicitly specifies each 'sales/mm-yyyy/ sales.csv' fil
• I. Register the dataset with the name sales_dataset each month as a new version and with a tag named month indicating the month and year it was registere
• J. Use this dataset for all experiments, identifying the version to be used based on the month tag as necessary.

Answer: B

Explanation:
Specify the path. Example:
The following code gets the workspace existing workspace and the desired datastore by name. And then passes the datastore and file locations to the path parameter to create a new TabularDataset, weather_ds.
from azureml.core import Workspace, Datastore, Dataset datastore_name = 'your datastore name'
# get existing workspace
workspace = Workspace.from_config()
# retrieve an existing datastore in the workspace by name datastore = Datastore.get(workspace, datastore_name)
# create a TabularDataset from 3 file paths in datastore datastore_paths = [(datastore, 'weather/2018/11.csv'), (datastore, 'weather/2018/12.csv'),
(datastore, 'weather/2019/*.csv')]
weather_ds = Dataset.Tabular.from_delimited_files(path=datastore_paths)

NEW QUESTION 13

You use the following code to define the steps for a pipeline: from azureml.core import Workspace, Experiment, Run from azureml.pipeline.core import Pipeline
from azureml.pipeline.steps import PythonScriptStep ws = Workspace.from_config()
. . .
step1 = PythonScriptStep(name="step1", ...) step2 = PythonScriptsStep(name="step2", ...) pipeline_steps = [step1, step2]
You need to add code to run the steps.
Which two code segments can you use to achieve this goal? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

• A. experiment = Experiment(workspace=ws, name='pipeline-experiment')run = experiment.submit(config=pipeline_steps)
• B. run = Run(pipeline_steps)
• C. pipeline = Pipeline(workspace=ws, steps=pipeline_steps) experiment = Experiment(workspace=ws, name='pipeline-experiment')run = experiment.submit(pipeline)
• D. pipeline = Pipeline(workspace=ws, steps=pipeline_steps)run = pipeline.submit(experiment_name='pipeline-experiment')

Answer: CD

Explanation:
After you define your steps, you build the pipeline by using some or all of those steps.
# Build the pipeline. Example:
pipeline1 = Pipeline(workspace=ws, steps=[compare_models])
# Submit the pipeline to be run
pipeline_run1 = Experiment(ws, 'Compare_Models_Exp').submit(pipeline1) Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-machine-learning-pipelines

NEW QUESTION 14

You register a file dataset named csvjolder that references a folder. The folder includes multiple com
ma-separated values (CSV) files in an Azure storage blob container. You plan to use the following code to run a script that loads data from the file dataset. You create and instantiate the following variables:

You have the following code:


You need to pass the dataset to ensure that the script can read the files it references. Which code segment should you insert to replace the code comment?
A)

B)

C)

D)

• A. Option A
• B. Option B
• C. Option C
• D. Option D

Answer: D

Explanation:
Example:
from azureml.train.estimator import Estimator script_params = {
# to mount files referenced by mnist dataset
'--data-folder': mnist_file_dataset.as_named_input('mnist_opendataset').as_mount(),
'--regularization': 0.5
}
est = Estimator(source_directory=script_folder, script_params=script_params, compute_target=compute_target, environment_definition=env, entry_script='train.py')
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-train-models-with-aml

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 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.
You need to add logging to the script to allow Hyperdrive to optimize hyperparameters for the AUC metric. Solution: Run the following code:

Does the solution meet the goal?

• A. Yes
• B. No

Answer: B

Explanation:
Use a solution with logging.info(message) instead. Note: Python printing/logging example: logging.info(message)
Destination: Driver logs, Azure Machine Learning designer Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-debug-pipelines

NEW QUESTION 16

You 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 each description? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

• 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 analyzing 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: Calculate the column median value and use the median value as the replacement for any missing value in the column.
Does the solution meet the goal?

• A. Yes
• B. No

Answer: B

Explanation:
Use the Multiple Imputation by Chained Equations (MICE) method. References: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3074241/
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data

NEW QUESTION 18

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.

• A. Yes
• B. Not Mastered

Answer: A

NEW QUESTION 19

You need to define an evaluation strategy for the crowd sentiment models.
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.

• A. Yes
• B. Not Mastered

Answer: A

NEW QUESTION 20
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