Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 251:

  HOTSPOT

  You need to configure the Feature Based Feature Selection module based on the experiment requirements and datasets.

  How should you configure the module properties? To answer, select the appropriate options in the dialog box in the answer area.

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Box 1: Mutual Information.

  The mutual information score is particularly useful in feature selection because it maximizes the mutual information between the joint distribution and target variables in datasets with many dimensions.

  Box 2: MedianValue

  MedianValue is the feature column, , it is the predictor of the dataset.

  Scenario: The MedianValue and AvgRoomsinHouse columns both hold data in numeric format. You need to select a feature selection algorithm to analyze the relationship between the two columns in more detail.

  References:

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/filter-based-feature-selection

• Question 252:

  You have a dataset that contains salary information for users. You plan to generate an aggregate salary report that shows average salaries by city.

  Privacy of individuals must be preserved without impacting accuracy, completeness, or reliability of the data. The aggregation must be statistically consistent with the distribution of the original data. You must return an approximation of the data instead of the raw data.

  You need to apply a differential privacy approach.

  What should you do?

  A. Add noise to the salary data during the analysis
  B. Encrypt the salary data before analysis
  C. Remove the salary data
  D. Convert the salary data to the average column value
  A. Add noise to the salary data during the analysis

• Question 253:

  You create an Azure Machine Learning workspace. You are preparing a local Python environment on a laptop computer. You want to use the laptop to connect to the workspace and run experiments.

  You create the following config.json file.

  

  You must use the Azure Machine Learning SDK to interact with data and experiments in the workspace. You need to configure the config.json file to connect to the workspace from the Python environment.

  Which two additional parameters must you add to the config.json file in order to connect to the workspace? Each correct answer presents part of the solution.

  NOTE: Each correct selection is worth one point.

  A. login
  B. resource_group
  C. subscription_id
  D. key
  E. region
  B. resource_group
  C. subscription_id

  ---

  Explanation

  To use the same workspace in multiple environments, create a JSON configuration file. The configuration file saves your subscription (subscription_id), resource (resource_group), and workspace name so that it can be easily loaded.

  The following sample shows how to create a workspace. from azureml.core import Workspace ws = Workspace.create(name='myworkspace', subscription_id='<azure-subscription-id>', resource_group='myresourcegroup', create_resource_group=True, location='eastus2' )

  https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.workspace.workspace

• Question 254:

  HOTSPOT

  You create an Azure Machine Learning workspace and set up a development environment. You plan to train a deep neural network (DNN) by using the Tensorflow framework and by using estimators to submit training scripts.

  You must optimize computation speed for training runs.

  You need to choose the appropriate estimator to use as well as the appropriate training compute target configuration.

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

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Box 1: Tensorflow

  TensorFlow represents an estimator for training in TensorFlow experiments.

  Box 2: 12 vCPU, 112 GB memory..,2 GPU,..

  Use GPUs for the deep neural network.

  https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.dnn

• Question 255:

  You are building a recurrent neural network to perform a binary classification.

  You review the training loss, validation loss, training accuracy, and validation accuracy for each training epoch.

  You need to analyze model performance.

  You need to identify whether the classification model is overfitted.

  Which of the following is correct?

  A. The training loss stays constant and the validation loss stays on a constant value and close to the training loss value when training the model.
  B. The training loss decreases while the validation loss increases when training the model.
  C. The training loss stays constant and the validation loss decreases when training the model.
  D. The training loss increases while the validation loss decreases when training the model.
  B. The training loss decreases while the validation loss increases when training the model.

  ---

  Explanation

  An overfit model is one where performance on the train set is good and continues to improve, whereas performance on the validation set improves to a point and then begins to degrade.

  https://machinelearningmastery.com/diagnose-overfitting-underfitting-lstm-models/

• Question 256:

  DRAG DROP

  You create machine learning models by using Azure Machine Learning.

  You plan to train and score models by using a variety of compute contexts. You also plan to create a new compute resource in Azure Machine Learning studio.

  You need to select the appropriate compute types.

  Which compute types should you select? To answer, drag the appropriate compute types to the correct requirements. Each compute type may be used once, 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: Attached compute

  

  Box 2: Inference cluster

  Box 3: Training cluster

  Box 4: Attached compute

• Question 257:

  You are conducting feature engineering to prepuce data for further analysis.

  The data includes seasonal patterns on inventory requirements.

  You need to select the appropriate method to conduct feature engineering on the data.

  Which method should you use?

  A. Exponential Smoothing (ETS) function.
  B. One Class Support Vector Machine module.
  C. Time Series Anomaly Detection module.
  D. Finite Impulse Response (FIR) Filter module.
  D. Finite Impulse Response (FIR) Filter module.

• Question 258:

  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. Run the training script as an experiment on the aks-cluster compute target.

  Does the solution meet the goal?

  A. Yes
  B. No
  B. No

  ---

  Explanation

  Need to attach the mlvm virtual machine as a compute target in the Azure Machine Learning workspace.

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

• Question 259:

  HOTSPOT

  You are using an Azure Machine Learning workspace. You set up an environment for model testing and an environment for production.

  The compute target for testing must minimize cost and deployment efforts. The compute target for production must provide fast response time, autoscaling of the deployed service, and support real-time inferencing.

  You need to configure compute targets for model testing and production.

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

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Box 1: Local web service

  The Local web service compute target is used for testing/debugging. Use it for limited testing and troubleshooting. Hardware acceleration depends on use of libraries in the local system.

  Box 2: Azure Kubernetes Service (AKS)

  Azure Kubernetes Service (AKS) is used for Real-time inference.

  Recommended for production workloads.

  Use it for high-scale production deployments. Provides fast response time and autoscaling of the deployed service

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

• Question 260:

  You are using a ScriptRunConfig object to configure an experiment that uses a script to train a machine learning model.

  The script must apply a regularization rate hyperparameter to the algorithm that is used to train the model.

  You need to pass the regularization rate in a variable named reg_rate to the script.

  Which code segment should you use?

  

  A. Option A
  B. Option B
  C. Option C
  D. Option D
  B. Option B

  ---

  Explanation

  ScriptRunConfig Class

  Represents configuration information for submitting a training run in Azure Machine Learning.

  Constructor

  ScriptRunConfig(source_directory, script=None, arguments=None, run_config=None, _telemetry_values=None, compute_target=None, environment=None, distributed_job_config=None, resume_from=None, max_run_duration_seconds=2592000, command=None, docker_runtime_config=None)

  Parameters include:

  arguments

  list or str

  Required

  Optional command line arguments to pass to the training script. Arguments are passed in pairs, for example, ['rg1', arg1_val, 'rg2', arg2_val].

  Example:

  # configure and submit your training run config = ScriptRunConfig(source_directory='.', script='train.py', arguments=['--arg1', arg1_val, '--arg2', arg2_val], compute_target=cluster, environment=env)

  Incorrect:

  Not A:

  Parameter _telemetry_values

  dict

  Required

  Internal use only.

  https://learn.microsoft.com/en-us/python/api/azureml-core/azureml.core.script_run_config.scriptrunconfig