What does this exam cover?

Azure data science solution design and implementation.

You are a data scientist working for a bank and have used Azure ML to train and register a machine learning model that predicts whether a customer is likely to repay a loan. You want to understand how your model is making selections and must be sure that the model does not violate government regulations such as denying loans based on where an applicant lives. You need to determine the extent to which each feature in the customer data is influencing predictions. What should you do?

Correct Answer: Use the interpretability package to generate an explainer for the model.

You create an Azure Machine Learning workspace. You must configure an event-driven workflow to automatically trigger upon completion of training runs in the workspace. The solution must minimize the administrative effort to configure the trigger. You need to configure an Azure service to automatically trigger the workflow. Which Azure service should you use?

Correct Answer: Event Grid subscription

Your team is building a data engineering and data science development environment. The environment must support the following requirements: ✑ support Python and Scala ✑ compose data storage, movement, and processing services into automated data pipelines ✑ the same tool should be used for the orchestration of both data engineering and data science ✑ support workload isolation and interactive workloads ✑ enable scaling across a cluster of machines You need to create the environment. What should you do?

Correct Answer: Build the environment in Azure Databricks and use Azure Data Factory for orchestration.

You create a deep learning model for image recognition on Azure Machine Learning service using GPU-based training. You must deploy the model to a context that allows for real-time GPU-based inferencing. You need to configure compute resources for model inferencing. Which compute type should you use?

Correct Answer: Azure Kubernetes Service

You need to implement a Data Science Virtual Machine (DSVM) that supports the Caffe2 deep learning framework. Which of the following DSVM should you create?

Correct Answer: Ubuntu 16.04 DSVM

You create a batch inference pipeline by using the Azure ML SDK. You run the pipeline by using the following code: from azureml.pipeline.core import Pipeline from azureml.core.experiment import Experiment pipeline = Pipeline(workspace=ws, steps=[parallelrun_step]) pipeline_run = Experiment(ws, 'batch_pipeline').submit(pipeline) You need to monitor the progress of the pipeline execution. What are two possible ways to achieve this goal? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

Correct Answer: Use the Activity log in the Azure portal for the Machine Learning workspace.

You have an Azure Machine Learning (ML) model deployed to an online endpoint. You need to review container logs from the endpoint by using Azure ML Python SDK v2. The logs must include the console log from the inference server, with print/log statements from the model’s scoring script. What should you do first?

Correct Answer: Create an instance of the MLCIient class.

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?

Correct Answer: Azure Machine Learning Service

You use an Azure Machine Learning workspace. You must monitor cost at the endpoint and deployment level. You have a trained model that must be deployed as an online endpoint. Users must authenticate by using Microsoft Entra ID. What should you do?

Correct Answer: Deploy the model to a managed online endpoint. During deployment, set the token_auth_mode parameter of the target configuration object to true.

You need to implement a feature engineering strategy for the crowd sentiment local models. What should you do?

Correct Answer: Apply a Pearson correlation coefficient.

You are implementing a machine learning model to predict stock prices. The model uses a PostgreSQL database and requires GPU processing. You need to create a virtual machine that is pre-configured with the required tools. What should you do?

Correct Answer: Create a Deep Learning Virtual Machine (DLVM) Linux edition.

You use the following code to run a script as an experiment in Azure Machine Learning: You must identify the output files that are generated by the experiment run. You need to add code to retrieve the output file names. Which code segment should you add to the script?

Correct Answer: files= run.get_file_names()

You deploy a model as an Azure Machine Learning real-time web service using the following code. The deployment fails. You need to troubleshoot the deployment failure by determining the actions that were performed during deployment and identifying the specific action that failed. Which code segment should you run?

Correct Answer: service.get_logs()

You are developing deep learning models to analyze semi-structured, unstructured, and structured data types. You have the following data available for model building: ✑ Video recordings of sporting events ✑ Transcripts of radio commentary about events ✑ Logs from related social media feeds captured during sporting events You need to select an environment for creating the model. Which environment should you use?

Correct Answer: Azure Machine Learning Studio

You are designing a training job in an Azure Machine Learning workspace by using Automated ML. During training, the compute resource must scale up to handle larger datasets. You need to select the compute resource that has a multi-node cluster that automatically scales. Which Azure Machine Learning compute target should you use?

Correct Answer: Serverless compute

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?

Correct Answer: Register the model, specifying the model_framework and model_framework_version properties.

You are determining if two sets of data are significantly different from one another by using Azure Machine Learning Studio. Estimated values in one set of data may be more than or less than reference values in the other set of data. You must produce a distribution that has a constant Type I error as a function of the correlation. You need to produce the distribution. Which type of distribution should you produce?

Correct Answer: Paired t-test with a two-tail option

You need to implement a model development strategy to determine a user's tendency to respond to an ad. Which technique should you use?

Correct Answer: Use a Relative Expression Split module to partition the data based on distance travelled to the event.

You need to select a feature extraction method. Which method should you use?

Correct Answer: Spearman correlation

You are moving a large dataset from Azure Machine Learning Studio to a Weka environment. You need to format the data for the Weka environment. Which module should you use?

Correct Answer: Convert to ARFF

You train a machine learning model. You must deploy the model as a real-time inference service for testing. The service requires low CPU utilization and less than 48 MB of RAM. The compute target for the deployed service must initialize automatically while minimizing cost and administrative overhead. Which compute target should you use?

Correct Answer: Azure Container Instance (ACI)

You need to implement a new cost factor scenario for the ad response models as illustrated in the performance curve exhibit. Which technique should you use?

Correct Answer: Set the threshold to 0.5 and retrain if weighted Kappa deviates +/- 5% from 0.45.

You have been tasked with designing a deep learning model, which accommodates the most recent edition of Python, to recognize language. You have to include a suitable deep learning framework in the Data Science Virtual Machine (DSVM). Which of the following actions should you take?

Correct Answer: You should consider including TensorFlow.

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?

Correct Answer: mini_batch_size= "6"

You are performing feature engineering on a dataset. You must add a feature named CityName and populate the column value with the text London. You need to add the new feature to the dataset. Which Azure Machine Learning Studio module should you use?

Correct Answer: Apply SQL Transformation

You plan to use a Deep Learning Virtual Machine (DLVM) to train deep learning models using Compute Unified Device Architecture (CUDA) computations. You need to configure the DLVM to support CUDA. What should you implement?

Correct Answer: Graphic Processing Unit (GPU)

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?

Correct Answer: Enable Azure Application Insights for the service endpoint and view logged data in the Azure portal.

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Designing and Implementing a Data Science Solution on Azure

Design and implement a data science solution on Azure.

4.8★★★★★(382 ratings)

20,624 studentsIntermediate

Learn to create data science workflows in Azure that cover data preparation, modeling, training, and deployment. The exam focuses on applying machine learning in a cloud environment for business-ready solutions. This certification supports data scientists working toward production-grade Azure solutions. It helps you move from experimentation to operational impact.

What you'll learn

Build data science workflows- Prepare training data- Train models in Azure- Deploy ML solutions- Support end-to-end analytics

DP-100

Lectures3

Quizzes0

Duration30 week

Students20,624

$20.00

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