Practice Free DP-100 Exam Online Questions
You use differential privacy to ensure your reports are private. The calculated value of the epsilon for your data is 1.8. You need to modify your data to ensure your reports are private.
Which epsilon value should you accept for your data?
- A . between 0 and 1
- B . between 2 and 3
- C . between 3 and 10
- D . more than 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
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
DRAG DROP
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.
You manage an Azure Machine Learning workspace. You have an environment for training jobs which uses an existing Docker image. A new version of the Docker image is available.
You need to use the latest version of the Docker image for the environment configuration by using the Azure Machine Learning SDK v2.
What should you do?
- A . Modify the conda. file to specify the new version of the Docker image.
- B . Use the Environment class to create a new version of the environment.
- C . Use the create.or. update method to change the tag of the image.
- D . Change the description parameter of the environment configuration.
DRAG DROP
You manage an Azure Machine Learning workspace named workspaces
You plan to create a registry named registry01 with the help of the following registry.yml (line numbers are used for reference only):
You need to use Azure Machine Learning Python SDK v2 with Python 3.10 in a notebook to interact with workspace1.
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.
HOTSPOT
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.
Explanation:
Box 1: Tensorflow
TensorFlow represents an estimator for training in TensorFlow experiments.
Box 2: 12 vCPU, 112 GB memory..,2 GPU,..
Use GPUs for the deep neural network.
Reference: https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.dnn
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:
Does the solution meet the goal?
- A . Yes
- B . No
A
Explanation:
The two steps are present: process_step and train_step
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-ml-py
You create an Azure Machine Learning workspace. You use Azure Machine Learning designer to create a pipeline within the workspace. You need to submit a pipeline run from the designer.
What should you do first?
- A . Create a compute cluster.
- B . Create an attached compute resource.
- C . Select a model.
- D . Create an experiment.
HOTSPOT
You create an experiment in Azure Machine Learning Studio- You add a training dataset that contains 10.000 rows. The first 9.000 rows represent class 0 (90 percent). The first 1.000 rows represent class 1 (10 percent).
The training set is unbalanced between two Classes. You must increase the number of training examples for class 1 to 4,000 by using data rows. You add the Synthetic Minority Oversampling Technique (SMOTE) module to the experiment.
You need to configure the module.
Which values should you use? To answer, select the appropriate options in the dialog box in the answer area. NOTE: Each correct selection is worth one point.
Explanation:
4000, 5
HOTSPOT
You create an Azure Machine Learning compute target named ComputeOne by using the STANDARD_D1 virtual machine image.
You define a Python variable named was that references the Azure Machine Learning workspace.
You run the following Python code:
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Explanation:
Box 1: Yes
ComputeTargetException class: An exception related to failures when creating, interacting with, or configuring a compute target. This exception is commonly raised for failures attaching a compute target, missing headers, and unsupported configuration values.
Create (workspace, name, provisioning_configuration)
Provision a Compute object by specifying a compute type and related configuration.
This method creates a new compute target rather than attaching an existing one.
Box 2: Yes
Box 3: No
The line before print(‘Step1’) will fail.
Reference: https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.computetarget