Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 271:

  DRAG DROP

  You design a project for interactive data wrangling with Apache Spark in an Azure Machine Learning workspace.

  The data pipeline must provide the following:

  1. Ingest and process a large amount of data from various sources and linked services, such as databases and APIs

  2. Visualize the results in Microsoft Power BI

  3. Quickly identify and address issues by observing a small amount of data using the fewest resources

  You need to select a compute option for project activities.

  Select and Place:

  

  

• Question 272:

  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: Replace each missing value using the Multiple Imputation by Chained Equations (MICE) method.

  Does the solution meet the goal?

  A. Yes
  B. No
  A. Yes

  ---

  Explanation

  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: Multivariate imputation by chained equations (MICE), sometimes called "fully conditional specification" or "sequential regression multiple imputation" has emerged in the statistical literature as one principled method of addressing missing data. Creating multiple imputations, as opposed to single imputations, accounts for the statistical uncertainty in the imputations. In addition, the chained equations approach is very flexible and can handle variables of varying types (e.g., continuous or binary) as well as complexities such as bounds or survey skip patterns.

  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

• Question 273:

  HOTSPOT

  You create an Azure Machine Learning workspace. You use the Azure Machine Learning SDK for Python.

  You must create a dataset from remote paths. The dataset must be reusable within the workspace.

  You need to create the dataset.

  How should you complete the following code segment? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  

  

• Question 274:

  You manage an Azure Machine Learning workspace.

  You must provide explanations for the behavior of the models with feature importance measures.

  You need to configure a Responsible AI dashboard in Azure Machine Learning.

  Which dashboard component should you configure?

  A. Counterfactual what-if
  B. Casual inference
  C. Fairness assessment
  D. Interpretability
  D. Interpretability

  ---

  Explanation

  Use interpretability when you need to:

  Determine how trustworthy your AI system's predictions are by understanding what features are most important for the predictions. Approach the debugging of your model by understanding it first and identifying whether the model is using healthy features or merely false correlations. Uncover potential sources of unfairness by understanding whether the model is basing predictions on sensitive features or on features that are highly correlated with them.

  Etc.

  Note: Responsible AI dashboard components

  The Responsible AI dashboard brings together, in a comprehensive view, various new and pre-existing tools. The dashboard integrates these tools with Azure Machine Learning CLI v2, Azure Machine Learning Python SDK v2, and Azure Machine Learning studio. The tools include:

  * Model interpretability (importance values for aggregate and individual features), to understand your model's predictions and how those overall and individual predictions are made.

  * Counterfactual what-if, to observe how feature perturbations would affect your model predictions while providing the closest data points with opposing or different model predictions.

  * Causal analysis, to use historical data to view the causal effects of treatment features on real-world outcomes.

  * Etc.

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

  https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai-dashboard

• Question 275:

  You manage an Azure Machine Learning Workspace named Workspase1 and an Azure Files share named Share1.

  You plan to create an Azure Files datastore in Workspace1 to target Share1.

  You need to configure permanent access to Share1 from the Azure Files datastore.

  Which authorization method should you use?

  A. Secondary access key
  B. Anonymous access
  C. Account SAS key
  D. Service SAS key
  A. Secondary access key

• Question 276:

  HOTSPOT

  You need to set up the Permutation Feature Importance module according to the model training requirements.

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

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Box 1: Accuracy

  Scenario: You want to configure hyperparameters in the model learning process to speed the learning phase by using hyperparameters. In addition, this configuration should cancel the lowest performing runs at each evaluation interval, thereby directing effort and resources towards models that are more likely to be successful.

  Box 2: R-Squared

• Question 277:

  You manage an Azure Machine Learning workspace.

  You build a custom model you must log with MLflow. The custom model includes the following:

  The model is not natively supported by MLflow.

  The model cannot be serialized in Pickle format.

  The model source code is complex.

  The Python library for the model must be packaged with the model.

  You need to create a custom model flavor to enable logging with MLflow.

  What should you use?

  A. model loader
  B. artifacts
  C. model wrapper
  D. custom signatures
  A. model loader

  ---

  Explanation

  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-convert-custom-model-to-mlflow

• Question 278:

  HOTSPOT

  You are creating data wrangling and model training solutions in an Azure Machine Learning workspace.

  You must use the same Python notebook to perform both data wrangling and model training.

  You need to use the Azure Machine Learning Python SDK v2 to define and configure the Synapse Spark pool asynchronously in the workspace as dedicated compute.

  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.

  

  

  ---

  Explanation:

  Box 1: SynapseSparkCompute

  Sample code:

  synapse_comp = SynapseSparkCompute(name=synapse_name, resource_id=synapse_resource) ml_client.begin_create_or_update(synapse_comp)

  Box 2: MLClient.begin_create_or_update

  Note: To attach a Synapse Compute using Python SDK, first create an instance of azure.ai.ml.MLClient class. This provides convenient functions for interaction with Azure Machine Learning services. The following code sample uses azure.identity.DefaultAzureCredential for connecting to a workspace in resource group of a specified Azure subscription. In the following code sample, define the SynapseSparkCompute with the parameters:

  name - user-defined name of the new attached Synapse Spark pool. resource_id - resource ID of the Synapse Spark pool created earlier in the Azure Synapse Analytics workspace.

  An azure.ai.ml.MLClient.begin_create_or_update() function call attaches the defined Synapse Spark pool to the Azure Machine Learning workspace.

  from azure.ai.ml import MLClient from azure.ai.ml.entities import SynapseSparkCompute from azure.identity import DefaultAzureCredential subscription_id = "<SUBSCRIPTION_ID>" resource_group = "<RESOURCE_GROUP>" workspace_name = "<AML_WORKSPACE_NAME>"

  ml_client = MLClient( DefaultAzureCredential(), subscription_id, resource_group, workspace_name ) synapse_name = "<ATTACHED_SPARK_POOL_NAME>" synapse_resource = "/subscriptions/<SUBSCRIPTION_ID>/resourceGroups/<RESOURCE_GROUP>/providers/Microsoft.Synapse/workspaces/<SYNAPSE_WORKSPACE_NAME>/bigDataPools/<SPARK_POOL_NAME>"

  synapse_comp = SynapseSparkCompute(name=synapse_name, resource_id=synapse_resource) ml_client.begin_create_or_update(synapse_comp)

  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-synapse-spark-pool

• Question 279:

  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 new experiment in Azure Machine Learning Studio.

  One class has a much smaller number of observations than the other classes in the training set.

  You need to select an appropriate data sampling strategy to compensate for the class imbalance.

  Solution: You use the Scale and Reduce sampling mode.

  Does the solution meet the goal?

  A. Yes
  B. No
  B. No

  ---

  Explanation

  Instead use the Synthetic Minority Oversampling Technique (SMOTE) sampling mode.

  Note: SMOTE is used to increase the number of underepresented cases in a dataset used for machine learning. SMOTE is a better way of increasing the number of rare cases than simply duplicating existing cases.

  References:

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

• Question 280:

  You are creating a classification model for a banking company to identify possible instances of credit card fraud. You plan to create the model in Azure Machine Learning by using automated machine learning.

  The training dataset that you are using is highly unbalanced.

  You need to evaluate the classification model.

  Which primary metric should you use?

  A. normalized_mean_absolute_error
  B. AUC_weighted
  C. accuracy
  D. normalized_root_mean_squared_error
  E. spearman_correlation
  B. AUC_weighted

  ---

  Explanation

  AUC_weighted is a Classification metric.

  Note: AUC is the Area under the Receiver Operating Characteristic Curve. Weighted is the arithmetic mean of the score for each class, weighted by the number of true instances in each class.

  Incorrect Answers:

  A: normalized_mean_absolute_error is a regression metric, not a classification metric.

  C: When comparing approaches to imbalanced classification problems, consider using metrics beyond accuracy such as recall, precision, and AUROC. It may be that switching the metric you optimize for during parameter selection or model selection is enough to provide desirable performance detecting the minority class.

  D: normalized_root_mean_squared_error is a regression metric, not a classification metric.

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml