Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 611:

  You have fine-tuned an Azure OpenAI Service model by using the Azure AI Foundry portal.

  The fine-tuned model is overfitting.

  You plan to correct the overfitting by fine-tuning the model again.

  You need to modify the default value of a fine-tuning task parameter to minimize the possibility of overfitting.

  Which modification should you apply?

  A. Increase the learning_rate_multiplier.
  B. Increase the batch_size.
  C. Decrease the batch_size.
  D. Decrease the learning_rate_multiplier.
  D. Decrease the learning_rate_multiplier.

• Question 612:

  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: Use the Last Observation Carried Forward (LOCF) method to impute the missing data points.

  Does the solution meet the goal?

  A. Yes
  B. No
  B. No

  ---

  Explanation

  Instead use the Multiple Imputation by Chained Equations (MICE) method. Replace using MICE: For each missing value, this option assigns a new value, which is calculated by using a method described in the statistical literature as "Multivariate Imputation using Chained Equations" or "Multiple Imputation by Chained Equations". With a multiple imputation method, each variable with missing data is modeled conditionally using the other variables in the data before filling in the missing values.

  Note: Last observation carried forward (LOCF) is a method of imputing missing data in longitudinal studies. If a person drops out of a study before it ends, then his or her last observed score on the dependent variable is used for all subsequent (i.e., missing) observation points. LOCF is used to maintain the sample size and to reduce the bias caused by the attrition of participants in a study.

  References:

  https://methods.sagepub.com/reference/encyc-of-research-design/n211.xml

  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3074241/

• Question 613:

  HOTSPOT

  You create an Azure Machine Learning dataset containing automobile price data. The dataset includes 10,000 rows and 10 columns. You use the Azure Machine Learning designer to transform the dataset by using an Execute Python Script component and custom code.

  The code must combine three columns to create a new column.

  You need to configure the code function.

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

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Explanation:

  Box 1: azureml_main

  The entry point script must contain the function azureml_main. The function has two function parameters that map to the two input ports for the Execute Python Script component.

  Box 2: dataframe

  The return value must be a Pandas Dataframe. You can return at most two dataframes as component outputs.

  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-designer-python

• Question 614:

  You create and register a model in an Azure Machine Learning workspace.

  You must use the Azure Machine Learning SDK to implement a batch inference pipeline that uses a ParallelRunStep to score input data using the model. You must specify a value for the ParallelRunConfig compute_target setting of the pipeline step.

  You need to create the compute target.

  Which class should you use?

  A. BatchCompute
  B. AdlaCompute
  C. AmlCompute
  D. AksCompute
  C. AmlCompute

  ---

  Explanation

  Compute target to use for ParallelRunStep. This parameter may be specified as a compute target object or the string name of a compute target in the workspace.

  The compute_target target is of AmlCompute or string.

  Note: An Azure Machine Learning Compute (AmlCompute) is a managed-compute infrastructure that allows you to easily create a single or multi-node compute. The compute is created within your workspace region as a resource that can be shared with other users

  https://docs.microsoft.com/en-us/python/api/azureml-contrib-pipeline-steps/azureml.contrib.pipeline.steps.parallelrunconfig

  https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.amlcompute(class)

• Question 615:

  DRAG DROP

  You have a model with a large difference between the training and validation error values.

  You must create a new model and perform cross-validation.

  You need to identify a parameter set for the new model using Azure Machine Learning Studio.

  Which module you should use for each step? To answer, drag the appropriate modules to the correct steps. Each module may be used once or 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.

  Select and Place:

  

  

  ---

  Box 1: Split data

  Box 2: Partition and Sample

  Box 3: Two-Class Boosted Decision Tree

  Box 4: Tune Model Hyperparameters

  Integrated train and tune: You configure a set of parameters to use, and then let the module iterate over multiple combinations, measuring accuracy until it finds a "best" model. With most learner modules, you can choose which parameters should be changed during the training process, and which should remain fixed.

  We recommend that you use Cross-Validate Model to establish the goodness of the model given the specified parameters. Use Tune Model Hyperparameters to identify the optimal parameters.

  References:

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/partition-and-sample

• Question 616:

  An organization creates and deploys a multi-class image classification deep learning model that uses a set of labeled photographs.

  The software engineering team reports there is a heavy inferencing load for the prediction web services during the summer. The production web service for the model fails to meet demand despite having a fully- utilized compute cluster where the web service is deployed.

  You need to improve performance of the image classification web service with minimal downtime and minimal administrative effort.

  What should you advise the IT Operations team to do?

  A. Create a new compute cluster by using larger VM sizes for the nodes, redeploy the web service to that cluster, and update the DNS registration for the service endpoint to point to the new cluster.
  B. Increase the node count of the compute cluster where the web service is deployed.
  C. Increase the minimum node count of the compute cluster where the web service is deployed.
  D. Increase the VM size of nodes in the compute cluster where the web service is deployed.
  B. Increase the node count of the compute cluster where the web service is deployed.

  ---

  Explanation

  The Azure Machine Learning SDK does not provide support scaling an AKS cluster. To scale the nodes in the cluster, use the UI for your AKS cluster in the Azure Machine Learning studio. You can only change the node count, not the VM size of the cluster.

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-kubernetes

• Question 617:

  You have an Azure Machine Learning workspace named Workspace1. Workspace1 has a registered MLflow model named model1 with PyFunc flavor.

  You plan to deploy model1 to an online endpoint named endpoint1 without egress connectivity by using Azure Machine Learning Python SDK v2.

  You have the following code:

  

  You need to add a parameter to the ManagedOnlineDeployment object to ensure the model deploys successfully.

  Solution: Add the scoring_script parameter.

  Does the solution meet the goal?

  A. Yes
  B. No
  B. No