Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 331:

  You manage an Azure Machine Learning workspace named workspace1.

  You must develop Python SDK v2 code to add a compute instance to workspace1. The code must import all required modules and call the constructor of the ComputeInstance class.

  You need to add the instantiated compute instance to workspace1.

  What should you use?

  A. constructor of the azure.ai.ml.ComputeSchedule class
  B. constructor of the azure.ai.ml.ComputePowerAction enum
  C. begin_create_or_update method of an instance of the azure.ai.ml.MLCIient class
  D. set_resources method of an instance of the azure.ai.ml.Command class
  C. begin_create_or_update method of an instance of the azure.ai.ml.MLCIient class

  ---

  Explanation

  Creating a compute instance is a one time process for your workspace. You can reuse the compute as a development workstation or as a compute target for training. You can have multiple compute instances attached to your workspace.

  Example:

  # Compute Instances need to have a unique name across the region.

  # Here we create a unique name with current datetime from azure.ai.ml.entities import ComputeInstance, AmlCompute

  import datetime ci_basic_name = "basic-ci" + datetime.datetime.now().strftime("%Y%m%d%H%M") ci_basic = ComputeInstance(name=ci_basic_name, size="STANDARD_DS3_v2") ml_client.begin_create_or_update(ci_basic).result()

  For more information on the classes, methods, and parameters used in this example, see the following reference documents:

  AmlCompute class

  ComputeInstance class Reference:

  https://learn.microsoft.com/en-us/python/api/azure-ai-ml/azure.ai.ml.entities.computeinstance

• Question 332:

  HOTSPOT

  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.

  

  

• Question 333:

  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. No

  ---

  Explanation

  There is a missing line: conda_packages=['scikit-learn'], which is needed.

  Correct example:

  sk_est = Estimator(source_directory='./my-sklearn-proj', script_params=script_params, compute_target=compute_target, entry_script='train.py', conda_packages=['scikit-learn'])

  Note:

  The Estimator class represents a generic estimator to train data using any supplied framework.

  This class is designed for use with machine learning frameworks that do not already have an Azure Machine Learning pre-configured estimator. Pre-configured estimators exist for Chainer, PyTorch, TensorFlow, and SKLearn.

  Example:

  from azureml.train.estimator import Estimator

  script_params = {

  # to mount files referenced by mnist dataset

  '--data-folder': ds.as_named_input('mnist').as_mount(),

  '--regularization': 0.8

  } Reference:

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

• Question 334:

  You plan to use a Data Science Virtual Machine (DSVM) with the open source deep learning frameworks Caffe2 and Theano. You need to select a pre configured DSVM to support the framework.

  Which of following should you create?

  A. Data Science Virtual Machine for Linux (Ubuntu)
  B. Data Science Virtual Machine for Windows 2012
  C. Data Science Virtual Machine for Windows 2016
  D. Data Science Virtual Machine for Linux (CentOS)
  A. Data Science Virtual Machine for Linux (Ubuntu)

• Question 335:

  DRAG DROP

  You have an existing GitHub repository containing Azure Machine Learning project files.

  You need to clone the repository to your Azure Machine Learning shared workspace file system.

  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.

  NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.

  Select and Place:

  

  

• Question 336:

  You train a model and register it in your Azure Machine Learning workspace. You are ready to deploy the model as a real-time web service.

  You deploy the model to an Azure Kubernetes Service (AKS) inference cluster, but the deployment fails because an error occurs when the service runs the entry script that is associated with the model deployment.

  You need to debug the error by iteratively modifying the code and reloading the service, without requiring a re-deployment of the service for each code update.

  What should you do?

  A. Modify the AKS service deployment configuration to enable application insights and re-deploy to AKS.
  B. Create an Azure Container Instances (ACI) web service deployment configuration and deploy the model on ACI.
  C. Add a breakpoint to the first line of the entry script and redeploy the service to AKS.
  D. Create a local web service deployment configuration and deploy the model to a local Docker container.
  E. Register a new version of the model and update the entry script to load the new version of the model from its registered path.
  D. Create a local web service deployment configuration and deploy the model to a local Docker container.

• Question 337:

  HOTSPOT

  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.

  

  

  ---

  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)

  Incorrect Answers: ..SGD..

  Scenario: All penalty detection models show inference phases using a Stochastic Gradient Descent (SGD) are running too slow.

  Box 4: .. nn.parallel.DistributedDataParallel..

  DistributedSampler(Sampler): The sampler that restricts data loading to a subset of the dataset.

  It is especially useful in conjunction with :class:`torch.nn.parallel.DistributedDataParallel`.

  References:

  https://github.com/pytorch/pytorch/blob/master/torch/utils/data/distributed.py

• Question 338:

  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. Yes

  ---

  Explanation

  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])

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

• Question 339:

  You have an Azure Machine Learning workspace. You plan to tune model hyperparameters by using a sweep job.

  You need to find a sampling method that supports early termination of low-performance jobs and continuous hyperpara meters.

  Solution: Use the Bayesian sampling method over the hyperparameter space.

  Does the solution meet the goal?

  A. Yes
  B. No
  A. Yes

• Question 340:

  HOTSPOT

  You are preparing to use the Azure ML SDK to run an experiment and need to create compute. You run the following 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.

  

  

  ---

  Box 1: No

  If a training cluster already exists it will be used.

  Box 2: Yes

  The wait_for_completion method waits for the current provisioning operation to finish on the cluster.

  Box 3: Yes

  Low Priority VMs use Azure's excess capacity and are thus cheaper but risk your run being pre-empted.

  Box 4: No

  Need to use training_compute.delete() to deprovision and delete the AmlCompute target.

  https://notebooks.azure.com/azureml/projects/azureml-getting-started/html/how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb

  https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.computetarget