Microsoft (DP-100) Exam Questions And Answers page 29
You register a model that you plan to use in a batch inference pipeline.
The batch inference pipeline must use a ParallelRunStep step to process files in a file dataset. The script has the ParallelRunStep step runs must process six input files each time the inferencing function is called.
You need to configure the pipeline.
Which configuration setting should you specify in the ParallelRunConfig object for the PrallelRunStep step?
The batch inference pipeline must use a ParallelRunStep step to process files in a file dataset. The script has the ParallelRunStep step runs must process six input files each time the inferencing function is called.
You need to configure the pipeline.
Which configuration setting should you specify in the ParallelRunConfig object for the PrallelRunStep step?
node_count= "6"
mini_batch_size= "6"
error_threshold= "6"
Data Preparation and Processing
Deployment and Monitoring
You create a multi-class image classification deep learning model.
You train the model by using PyTorch version 1.2.
You need to ensure that the correct version of PyTorch can be identified for the inferencing environment when the model is deployed.
What should you do?
You train the model by using PyTorch version 1.2.
You need to ensure that the correct version of PyTorch can be identified for the inferencing environment when the model is deployed.
What should you do?
Save the model locally as a.pt file, and deploy the model as a local web service.
Deploy the model on computer that is configured to use the default Azure Machine Learning conda environment.
Register the model with a .pt file extension and the default version property.
Register the model, specifying the model_framework and model_framework_version properties.
Data Preparation and Processing
Modeling
You have an Azure blob container that contains a set of TSV files. The Azure blob container is registered as a datastore for an Azure Machine Learning service workspace. Each TSV file uses the same data schema.
You plan to aggregate data for all of the TSV files together and then register the aggregated data as a dataset in an Azure Machine Learning workspace by using the Azure Machine Learning SDK for Python.
You run the following code.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
You plan to aggregate data for all of the TSV files together and then register the aggregated data as a dataset in an Azure Machine Learning workspace by using the Azure Machine Learning SDK for Python.
You run the following code.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Data Preparation and Processing
Deployment and Monitoring
You plan to build a team data science environment. Data for training models in machine learning pipelines will be over 20 GB in size.
You have the following requirements:
• Models must be built using Caffe2 or Chainer frameworks.
• Data scientists must be able to use a data science environment to build the machine learning pipelines and train models on their personal devices in both connected and disconnected network environments.
Personal devices must support updating machine learning pipelines when connected to a network.
You need to select a data science environment.
Which environment should you use?
You have the following requirements:
• Models must be built using Caffe2 or Chainer frameworks.
• Data scientists must be able to use a data science environment to build the machine learning pipelines and train models on their personal devices in both connected and disconnected network environments.
Personal devices must support updating machine learning pipelines when connected to a network.
You need to select a data science environment.
Which environment should you use?
Azure Machine Learning Service
Azure Machine Learning Studio
Azure Databricks
Azure Kubernetes Service (AKS)
Data Preparation and Processing
Modeling
This question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the recommendation satisfies the requirements.
You have been tasked with constructing a machine learning model that translates language text into a different language text.
The machine learning model must be constructed and trained to learn the sequence of the.
Recommendation: You make use of Recurrent Neural Networks (RNNs).
Will the requirements be satisfied?
You have been tasked with constructing a machine learning model that translates language text into a different language text.
The machine learning model must be constructed and trained to learn the sequence of the.
Recommendation: You make use of Recurrent Neural Networks (RNNs).
Will the requirements be satisfied?
Yes
No
Data Preparation and Processing
Modeling
You need to visually identify whether outliers exist in the Age column and quantify the outliers before the outliers are removed.
Which three Azure Machine Learning Studio modules should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Which three Azure Machine Learning Studio modules should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Create Scatterplot
Summarize Data
Clip Values
Replace Discrete Values
Build Counting Transform
Data Preparation and Processing
Modeling
You use Azure Machine Learning to deploy a model as a real-time web service.
You need to create an entry script for the service that ensures that the model is loaded when the service starts and is used to score new data as it is received.
Which functions should you include in the script? To answer, drag the appropriate functions to the correct actions. Each function 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.
You need to create an entry script for the service that ensures that the model is loaded when the service starts and is used to score new data as it is received.
Which functions should you include in the script? To answer, drag the appropriate functions to the correct actions. Each function 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.
Modeling
Deployment and Monitoring
You create an Azure Machine Learning workspace named ML-workspace. You also create an Azure Databricks workspace named DB-workspace. DB-workspace contains a cluster named DB-cluster.
You must use DB-cluster to run experiments from notebooks that you import into DB-workspace.
You need to use ML-workspace to track MLflow metrics and artifacts generated by experiments running on DB-cluster. The solution must minimize the need for custom code.
What should you do?
You must use DB-cluster to run experiments from notebooks that you import into DB-workspace.
You need to use ML-workspace to track MLflow metrics and artifacts generated by experiments running on DB-cluster. The solution must minimize the need for custom code.
What should you do?
From DB-cluster, configure the Advanced Logging option.
From DB-workspace, configure the Link Azure ML workspace option.
From ML-workspace, create an attached compute.
From ML-workspace, create a compute cluster.
Designing and Implementing Data Science Solutions
Deployment and Monitoring
You use the designer to create a training pipeline for a classification model. The pipeline uses a dataset that includes the features and labels required for model training.
You create a real-time inference pipeline from the training pipeline. You observe that the schema for the generated web service input is based on the dataset and includes the label column that the model predicts. Client applications that use the service must not be required to submit this value.
You need to modify the inference pipeline to meet the requirement.
What should you do?
You create a real-time inference pipeline from the training pipeline. You observe that the schema for the generated web service input is based on the dataset and includes the label column that the model predicts. Client applications that use the service must not be required to submit this value.
You need to modify the inference pipeline to meet the requirement.
What should you do?
Add a Select Columns in Dataset module to the inference pipeline after the dataset and use it to select all columns other than the label.
Delete the dataset from the training pipeline and recreate the real-time inference pipeline.
Delete the Web Service Input module from the inference pipeline.
Replace the dataset in the inference pipeline with an Enter Data Manually module that includes data for the feature columns but not the label column.
Data Preparation and Processing
Deployment and Monitoring
You have a dataset that includes confidential data. You use the dataset to train a model.
You must use a differential privacy parameter to keep the data of individuals safe and private.
You need to reduce the effect of user data on aggregated results.
What should you do?
You must use a differential privacy parameter to keep the data of individuals safe and private.
You need to reduce the effect of user data on aggregated results.
What should you do?
Decrease the value of the epsilon parameter to reduce the amount of noise added to the data
Increase the value of the epsilon parameter to decrease privacy and increase accuracy
Decrease the value of the epsilon parameter to increase privacy and reduce accuracy
Set the value of the epsilon parameter to 1 to ensure maximum privacy
Data Preparation and Processing
Modeling
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