Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 231:

  You need to implement a feature engineering strategy for the crowd sentiment local models.

  What should you do?

  A. Apply an analysis of variance (ANOVA).
  B. Apply a Pearson correlation coefficient.
  C. Apply a Spearman correlation coefficient.
  D. Apply a linear discriminant analysis.
  D. Apply a linear discriminant analysis.

  ---

  Explanation

  The linear discriminant analysis method works only on continuous variables, not categorical or ordinal variables.

  Linear discriminant analysis is similar to analysis of variance (ANOVA) in that it works by comparing the means of the variables.

  Scenario:

  Data scientists must build notebooks in a local environment using automatic feature engineering and model building in machine learning pipelines.

  Experiments for local crowd sentiment models must combine local penalty detection data.

  All shared features for local models are continuous variables.

  Incorrect Answers:

  B: The Pearson correlation coefficient, sometimes called Pearson's R test, is a statistical value that measures the linear relationship between two variables. By examining the coefficient values, you can infer something about the strength of the relationship between the two variables, and whether they are positively correlated or negatively correlated.

  C: Spearman's correlation coefficient is designed for use with non-parametric and non-normally distributed data. Spearman's coefficient is a nonparametric measure of statistical dependence between two variables, and is sometimes denoted by the Greek letter rho. The Spearman's coefficient expresses the degree to which two variables are monotonically related. It is also called Spearman rank correlation, because it can be used with ordinal variables.

  References:

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/fisher-linear-discriminant-analysis

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/compute-linear-correlation

• Question 232:

  DRAG DROP

  You have an Azure Machine Learning workspace that contains a training cluster and an inference cluster.

  You plan to create a classification model by using the Azure Machine Learning designer.

  You need to ensure that client applications can submit data as HTTP requests and receive predictions as responses.

  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.

  Select and Place:

  

  

• Question 233:

  You plan to use the Hyperdrive feature of Azure Machine Learning to determine the optimal hyperparameter values when training a model.

  You must use Hyperdrive to try combinations of the following hyperparameter values:

  1. learning_rate: any value between 0.001 and 0.1

  2. batch_size: 16, 32, or 64

  You need to configure the search space for the Hyperdrive experiment.

  Which two parameter expressions should you use? Each correct answer presents part of the solution.

  NOTE: Each correct selection is worth one point.

  A. a choice expression for learning_rate
  B. a uniform expression for learning_rate
  C. a normal expression for batch_size
  D. a choice expression for batch_size
  E. a uniform expression for batch_size
  B. a uniform expression for learning_rate
  D. a choice expression for batch_size

  ---

  Explanation

  B: Continuous hyperparameters are specified as a distribution over a continuous range of values.

  Supported distributions include:

  uniform(low, high) - Returns a value uniformly distributed between low and high

  D: Discrete hyperparameters are specified as a choice among discrete values. choice can be:

  1. one or more comma-separated values

  2. a range object

  3. any arbitrary list object

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters

• Question 234:

  HOTSPOT

  You plan to implement an Azure Machine Learning solution.

  You have the following requirements:

  1. Run a Jupyter notebook to interactively train a machine learning model.

  2. Deploy assets and workflows for machine learning proof of concept by using scripting rather than custom programming.

  You need to select a development technique for each requirement.

  Which development technique should you use? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Explanation:

  Box 1: Azure Machine Learning studio

  Run a Jupyter notebook to interactively train a machine learning model.

  Run Jupyter notebooks in your workspace

  You can run your Jupyter notebooks directly in your workspace in Azure Machine Learning studio. While you can launch Jupyter or JupyterLab, you can also edit and run your notebooks without leaving the workspace.

  Access notebooks from your workspace

  Use the Notebooks section of your workspace to edit and run Jupyter notebooks.

  Sign into Azure Machine Learning studio Select your workspace, if it isn't already open On the left, select Notebooks

  Box 2: Azure CLI

  Deploy assets and workflows for machine learning proof of concept by using scripting rather than custom programming.

  Deploy machine learning models to Azure

  You can deploy your machine learning or deep learning model as a web service in the Azure cloud.

  There are two options: Azure CLI, and Python SDK

  Incorrect:

  * Azure Machine Learning Python SDK

  Python: A clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java. Python is a general purpose programming language created by Guido Van Rossum. Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best.

  Note: Workflow for deploying a model

  The workflow is similar no matter where you deploy your model:

  Register the model.

  Prepare an entry script.

  Prepare an inference configuration.

  Deploy the model locally to ensure everything works.

  Choose a compute target.

  Deploy the model to the cloud.

  Test the resulting web service.

  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-run-jupyter-notebooks

  https://stackshare.io/stackups/azure-machine-learning-vs-python

• Question 235:

  You create an Azure Machine Learning workspace. You are implementing hyperparameter tuning for a model training from a notebook.

  You must configure a Bandit termination policy that provides the following outcome:

  If the value of the primary metric of AUC is 0.8 at the point of evaluation intervals, any run with the primary metric value below 0.66 will be terminated.

  You need to identify which Bandit termination policy configuration to use.

  What should you identify?

  A. Set slack_amount to 0.2.
  B. Set slack_factor to 0.1.
  C. Set slack_factor to 0.2.
  D. Set slack_amount to 0.1.
  C. Set slack_factor to 0.2.

  ---

  Explanation

  Bandit policy

  Bandit policy is based on slack factor/slack amount and evaluation interval. Bandit policy ends a job when the primary metric isn't within the specified slack factor/slack amount of the most successful job.

  Specify the following configuration parameters:

  slack_factor or slack_amount: the slack allowed with respect to the best performing training job. slack_factor specifies the allowable slack as a ratio. slack_amount specifies the allowable slack as an absolute amount, instead of a ratio.

  For example, consider a Bandit policy applied at interval 10. Assume that the best performing job at interval 10 reported a primary metric is 0.8 with a goal to maximize the primary metric. If the policy specifies a slack_factor of 0.2, any training jobs whose best metric at interval 10 is less than 0.66 (0.8/(1+slack_factor)) will be terminated.

  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters

• Question 236:

  HOTSPOT

  You manage an Azure Machine Learning workspace named Workspace1 and an Azure Blob Storage accessed by using the URL https://storage1.blob.core.wmdows.net/data1.

  You plan to create an Azure Blob datastore in Workspace1.

  The datastore must target the Blob Storage by using Azure Machine Learning Python SDK v2.

  Access authorization to the datastore must be limited to a specific amount of time.

  You need to select the parameters of the Azure Blob Datastore class that will point to the target datastore and authorize access to it.

  Which parameters should you use? To answer, select the appropriate options in the answer area

  NOTE: Each correct selection is worth one point.

  

  

• Question 237:

  You have been tasked with evaluating your model on a partial data sample via k-fold cross-validation.

  You have already configured a k parameter as the number of splits. You now have to configure the k parameter for the cross-validation with the usual value choice.

  Recommendation: You configure the use of the value k=10.

  Will the requirements be satisfied?

  A. Yes
  B. No
  A. Yes

  ---

  Explanation

  Leave One Out (LOO) cross-validation

  Setting K = n (the number of observations) yields n-fold and is called leave-one out cross-validation (LOO), a special case of the K-fold approach.

  LOO CV is sometimes useful but typically doesn't shake up the data enough. The estimates from each fold are highly correlated and hence their average can have high variance.

  This is why the usual choice is K=5 or 10. It provides a good compromise for the bias-variance tradeoff.

• Question 238:

  You have an Azure Machine Learning workspace. You connect to a terminal session from the Notebooks page in Azure Machine Learning Studio.

  You plan to add a new Jupyter kernel that will be accessible from the same terminal session.

  You need to perform a task that must be completed before you can add the new kernel.

  Solution: Create a compute instance.

  Does the solution meet the goal?

  A. Yes
  B. No
  B. No

• Question 239:

  DRAG DROP

  You manage an Azure AI Foundry project.

  You fine-tune a base model.

  During evaluation, you observe that the model is overfitting and that responses vary significantly.

  You need to improve the fine-tuned model.

  Which hyperparameters should you use? To answer, move the appropriate hyperparameters to the correct requirements.

  NOTE: Each correct selection is worth one point.

  Select and Place:

  

  

• Question 240:

  You have an Azure Machine Learning workspace named WS1.

  You plan to use the Responsible Al dashboard to assess MLflow models that you will register in WS1.

  You need to identify the library you should use to register the MLflow models.

  Which library should you use?

  A. PyTorch
  B. mlpy
  C. TensorFlow
  D. scikit-learn
  A. PyTorch