Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 301:

  You are in the process of constructing a deep convolutional neural network (CNN). The CNN will be used for image classification.

  You notice that the CNN model you constructed displays hints of overfitting.

  You want to make sure that overfitting is minimized, and that the model is converged to an optimal fit.

  Which of the following is TRUE with regards to achieving your goal?

  A. You have to add an additional dense layer with 512 input units, and reduce the amount of training data.
  B. You have to add L1/L2 regularization, and reduce the amount of training data.
  C. You have to reduce the amount of training data and make use of training data augmentation.
  D. You have to add L1/L2 regularization, and make use of training data augmentation.
  E. You have to add an additional dense layer with 512 input units, and add L1/L2 regularization.
  D. You have to add L1/L2 regularization, and make use of training data augmentation.

  ---

  Explanation

  https://machinelearningmastery.com/how-to-reduce-overfitting-in-deep-learning-with-weight-regularization/

  https://en.wikipedia.org/wiki/Convolutional_neural_network

• Question 302:

  You are evaluating a completed binary classification machine learning model.

  You need to use the precision as the evaluation metric.

  Which visualization should you use?

  A. violin plot
  B. Gradient descent
  C. Scatter plot
  D. Receiver Operating Characteristic (ROC) curve
  D. Receiver Operating Characteristic (ROC) curve

  ---

  Explanation

  Receiver operating characteristic (or ROC) is a plot of the correctly classified labels vs. the incorrectly classified labels for a particular model.

  Incorrect Answers:

  A: A violin plot is a visual that traditionally combines a box plot and a kernel density plot.

  B: Gradient descent is a first-order iterative optimization algorithm for finding the minimum of a function. To find a local minimum of a function using gradient descent, one takes steps proportional to the negative of the gradient (or approximate gradient) of the function at the current point.

  C: A scatter plot graphs the actual values in your data against the values predicted by the model. The scatter plot displays the actual values along the X-axis, and displays the predicted values along the Y-axis. It also displays a line that illustrates the perfect prediction, where the predicted value exactly matches the actual value.

  References:

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

  matrix

• Question 303:

  DRAG DROP

  You are building an experiment using the Azure Machine Learning designer.

  You split a dataset into training and testing sets. You select the Two-Class Boosted Decision Tree as the algorithm.

  You need to determine the Area Under the Curve (AUC) of the model.

  Which three modules should you use in sequence? To answer, move the appropriate modules from the list of modules to the answer area and arrange them in the correct order.

  Select and Place:

  

  

  ---

  Step 1: Train Model

  Two-Class Boosted Decision Tree

  First, set up the boosted decision tree model.

  1. Find the Two-Class Boosted Decision Tree module in the module palette and drag it onto the canvas.

  2. Find the Train Model module, drag it onto the canvas, and then connect the output of the Two-Class Boosted Decision Tree module to the left input port of the Train Model module.

  The Two-Class Boosted Decision Tree module initializes the generic model, and Train Model uses training data to train the model.

  3. Connect the left output of the left Execute R Script module to the right input port of the Train Model module (in this tutorial you used the data coming from the left side of the Split Data module for training).

  This portion of the experiment now looks something like this:

  

  Step 2: Score Model

  Score and evaluate the models

  You use the testing data that was separated out by the Split Data module to score our trained models. You can then compare the results of the two models to see which generated better results.

  Add the Score Model modules 1. Find the Score Model module and drag it onto the canvas.

  2. Connect the Train Model module that's connected to the Two-Class Boosted Decision Tree module to the left input port of the Score Model module.

  3. Connect the right Execute R Script module (our testing data) to the right input port of the Score Model module.

  

  Step 3: Evaluate Model

  To evaluate the two scoring results and compare them, you use an Evaluate Model module.

  1. Find the Evaluate Model module and drag it onto the canvas.

  2. Connect the output port of the Score Model module associated with the boosted decision tree model to the left input port of the Evaluate Model module.

  3. Connect the other Score Model module to the right input port.

  

• Question 304:

  You are solving a classification task.

  You must evaluate your model on a limited data sample by using k-fold cross-validation. You start by configuring a k parameter as the number of splits.

  You need to configure the k parameter for the cross-validation.

  Which value should you use?

  A. k=1
  B. k=10
  C. k=0.5
  D. k=0.9
  B. k=10

  ---

  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 305:

  HOTSPOT

  You manage an Azure Machine Learning workspace.

  You train a model interactively with a Jupyter Notebook in the workspace. During training, a dataset is created with accuracy and loss metrics for each epoch.

  You need to log the dataset using MLflow.

  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.

  

  

• Question 306:

  You are reviewing model benchmarks in Azure AI Foundry.

  You must use a large language model benchmark that measures linguistic correctness.

  Which benchmark metric should you focus on?

  A. Fluency
  B. Coherence
  C. Precision
  D. Accuracy
  A. Fluency

• Question 307:

  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=1.

  Will the requirements be satisfied?

  A. Yes
  B. No
  B. No

• Question 308:

  HOTSPOT

  You publish a batch inferencing pipeline that will be used by a business application. The application developers need to know which information should be submitted to and returned by the REST interface for the published pipeline.

  You need to identify the information required in the REST request and returned as a response from the published pipeline.

  Which values should you use in the REST request and to expect in the response? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Box 1: JSON containing an OAuth bearer token

  Specify your authentication header in the request. To run the pipeline from the REST endpoint, you need an OAuth2 Bearer-type authentication header.

  Box 2: JSON containing the experiment name

  Add a JSON payload object that has the experiment name.

  Example:

  rest_endpoint = published_pipeline.endpoint response = requests.post(rest_endpoint, headers=auth_header, json={"ExperimentName": "batch_scoring", "ParameterAssignments": {"process_count_per_node": 6}}) run_id = response.json()["Id"]

  Box 3: JSON containing the run ID

  Make the request to trigger the run. Include code to access the Id key from the response dictionary to get the value of the run ID.

• Question 309:

  You manage an Azure Machine Learning workspace.

  The development environment tor managing the workspace is configured to use Python SDK v2 in Azure Machine Learning Notebooks.

  A Synapse Spark Compute is currently attached and uses system-assigned identity.

  You need to use Python code to update the Synapse Spark Compute 10 use a user-assigned identity.

  Solution: Configure the IdentityConfiguration class with the appropriate identity type.

  Does the solution meet the goal?

  A. Yes
  B. No
  A. Yes

• Question 310:

  HOTSPOT

  You create an Azure Machine Learning workspace and install the MLflow library.

  You need to log different types of data by using the MLflow library.

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

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Explanation:

  Box 1: log_figure

  Log matlotlib plot

  Logging images MLflow supports two ways of logging images. Both of them persists the given image as an artifact inside of the run.

  * Log matlotlib plot or image file

  Example code: mlflow.log_figure(fig, "figure.png") figure.png is the name of the artifact that will be generated inside of the run. It doesn't have to be an existing file.

  * Etc.

  Box 2: log_metric

  Log boolean value

  Logging metrics

  Metrics, as opposite to parameters, are always numeric. The following table describes how to log specific numeric types:

  * Log a boolean value

  Example code: mlflow.log_metric("my_metric", 0) 0 = True, 1 = False

  * Etc.

  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-log-view-metrics