Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 171:

  You deploy a real-time inference service for a trained model.

  The deployed model supports a business-critical application, and it is important to be able to monitor the data submitted to the web service and the predictions the data generates.

  You need to implement a monitoring solution for the deployed model using minimal administrative effort.

  What should you do?

  A. View the explanations for the registered model in Azure ML studio.
  B. Enable Azure Application Insights for the service endpoint and view logged data in the Azure portal.
  C. View the log files generated by the experiment used to train the model.
  D. Create an ML Flow tracking URI that references the endpoint, and view the data logged by ML Flow.
  B. Enable Azure Application Insights for the service endpoint and view logged data in the Azure portal.

  ---

  Explanation

  Configure logging with Azure Machine Learning studio

  You can also enable Azure Application Insights from Azure Machine Learning studio. When you're ready to deploy your model as a web service, use the following steps to enable

  Application Insights:

  1. Sign in to the studio at

  https://ml.azure.com.

  2. Go to Models and select the model you want to deploy.

  3. Select +Deploy.

  4. Populate the Deploy model form.

  5. Expand the Advanced menu.

  6. Select Enable Application Insights diagnostics and data collection.

  

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-enable-app-insights

• Question 172:

  You are a data scientist building a deep convolutional neural network (CNN) for image classification.

  The CNN model you build shows signs of overfitting.

  You need to reduce overfitting and converge the model to an optimal fit.

  Which two actions should you perform? Each correct answer presents a complete solution.

  NOTE: Each correct selection is worth one point.

  A. Add an additional dense layer with 512 input units.
  B. Add L1/L2 regularization.
  C. Use training data augmentation.
  D. Reduce the amount of training data.
  E. Add an additional dense layer with 64 input units.
  B. Add L1/L2 regularization.
  C. Use 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 173:

  HOTSPOT

  You are running a training experiment on remote compute in Azure Machine Learning.

  The experiment is configured to use a conda environment that includes the mlflow and azureml-contrib-run packages.

  You must use MLflow as the logging package for tracking metrics generated in the experiment.

  You need to complete the script for the experiment.

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

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Box 1: import mlflow

  Import the mlflow and Workspace classes to access MLflow's tracking URI and configure your workspace.

  Box 2: mlflow.start_run()

  Set the MLflow experiment name with set_experiment() and start your training run with start_run().

  Box 3: mlflow.log_metric(' ..')

  Use log_metric() to activate the MLflow logging API and begin logging your training run metrics.

  Box 4: mlflow.end_run()

  Close the run:

  run.endRun() Reference:

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow

• Question 174:

  You create an Azure Machine Learning workspace.

  You must use the Python SDK v2 to implement an experiment from a Jupyter notebook in the workspace. The experiment must log a list of numeral metrics.

  You need to implement a method to log a list of numeral metrics.

  Which method should you use?

  A. mlflow.log_metric()
  B. mlflow.log.batch()
  C. mlflow.log_image()
  D. mlflow.log_artifact()
  B. mlflow.log.batch()

  ---

  Explanation

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

• Question 175:

  DRAG DROP

  You have an Azure Machine Learning workspace.

  You plan to use the terminal to configure a compute instance to run a notebook.

  You need to add a new R kernel to the compute instance.

  In which order should you perform the actions? To answer, move all actions from the list of actions to the answer area and arrange them in the correct order.

  Select and Place:

  

  

• Question 176:

  You create a batch inference pipeline by using the Azure ML SDK. You configure the pipeline parameters by executing the following code:

  

  You need to obtain the output from the pipeline execution.

  Where will you find the output?

  A. the digit_identification.py script
  B. the debug log
  C. the Activity Log in the Azure portal for the Machine Learning workspace
  D. the Inference Clusters tab in Machine Learning studio
  E. a file named parallel_run_step.txt located in the output folder
  E. a file named parallel_run_step.txt located in the output folder

  ---

  Explanation

  output_action (str): How the output is to be organized. Currently supported values are 'append_row' and 'summary_only'.

  1. 'append_row' ?All values output by run() method invocations will be aggregated into one unique file named parallel_run_step.txt that is created in the output location.

  2. 'summary_only' Reference:

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

• Question 177:

  HOTSPOT

  You train classification and regression models by using automated machine learning.

  You must evaluate automated machine learning experiment results. The results include how a classification model is making systematic errors in its predictions and the relationship between the target feature and the regression model's predictions. You must use charts generated by automated machine learning.

  You need to choose a chart type for each model type.

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

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Explanation:

  Box 1: Confusion matrix

  The results include how a classification model is making systematic errors in its predictions.

  Classification, Confusion matrix

  Confusion matrices provide a visual for how a machine learning model is making systematic errors in its predictions for classification models.

  Note: A confusion matrix is a summary of prediction results on a classification problem. The number of correct and incorrect predictions are summarized with count values and broken down by each class. This is the key to the confusion matrix.

  Incorrect:

  * Classification, Calibration curve

  The calibration curve plots a model's confidence in its predictions against the proportion of positive samples at each confidence level. A well-calibrated model will correctly classify 100% of the predictions to which it assigns 100% confidence, 50% of the predictions it assigns 50% confidence, 20% of the predictions it assigns a 20% confidence, and so on. A perfectly calibrated model will have a calibration curve following the y = x line where the model perfectly predicts the probability that samples belong to each class.

  Box 2: Predicted vs. true

  The results include the relationship between the target feature and the regression model's predictions.

  Regression, Predicted vs. true

  For regression and forecasting experiment the predicted vs. true chart plots the relationship between the target feature (true/actual values) and the model's predictions. The true values are binned along the x-axis and for each bin the mean predicted value is plotted with error bars. This allows you to see if a model is biased toward predicting certain values. The line displays the average prediction and the shaded area indicates the variance of predictions around that mean.

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

• Question 178:

  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.

  An IT department creates the following Azure resource groups and resources:

  

  The IT department creates an Azure Kubernetes Service (AKS)-based inference compute target named aks-cluster in the Azure Machine Learning workspace.

  You have a Microsoft Surface Book computer with a GPU. Python 3.6 and Visual Studio Code are installed.

  You need to run a script that trains a deep neural network (DNN) model and logs the loss and accuracy metrics.

  Solution: Install the Azure ML SDK on the Surface Book. Run Python code to connect to the workspace and then run the training script as an experiment on local compute.

  Does the solution meet the goal?

  A. Yes
  B. No
  A. Yes

  ---

  Explanation

  https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-target

• Question 179:

  You manage an Azure Machine Learning workspace.

  You build an image recognition training pipeline that includes hyperparameter tuning.

  For each epoch run, you plan to log the following metrics:

  1. The transformed images used for training in an existing folder

  2. A description explaining the hyperparameter changes

  You need to configure logging for the experiment.

  Which two functions should you use? (Choose two.) Each correct answer presents part of the solution.

  NOTE: Each correct selection is worth one point.

  A. mlflow.log_artifact()
  B. mlflow.log_artifacts()
  C. mlflow.log_metric()
  D. mlflow.log_param()
  B. mlflow.log_artifacts()
  D. mlflow.log_param()

  ---

  
  

• Question 180:

  HOTSPOT

  You are retrieving data from a large datastore by using Azure Machine Learning Studio.

  You must create a subset of the data for testing purposes using a random sampling seed based on the system clock.

  You add the Partition and Sample module to your experiment.

  You need to select the properties for the module.

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

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Box 1: Sampling

  Create a sample of data

  This option supports simple random sampling or stratified random sampling. This is useful if you want to create a smaller representative sample dataset for testing.

  1. Add the Partition and Sample module to your experiment in Studio, and connect the dataset.

  2. Partition or sample mode: Set this to Sampling.

  3. Rate of sampling. See box 2 below.

  Box 2: 0

  3. Rate of sampling. Random seed for sampling: Optionally, type an integer to use as a seed value.

  This option is important if you want the rows to be divided the same way every time. The default value is 0, meaning that a starting seed is generated based on the system clock. This can lead to slightly different results each time you run the experiment.

  References:

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