Microsoft Azure Data Scientist Associate- Practice Set 1

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?

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 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?

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?

You must store data in Azure Blob Storage to support Azure Machine Learning.

You need to transfer the data into Azure Blob Storage.

What are three possible ways to achieve the goal? Each correct answer presents a complete solution.

NOTE: Each correct selection is worth one point.

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?

You plan to create a speech recognition deep learning model.

The model must support the latest version of Python.

You need to recommend a deep learning framework for speech recognition to include in the Data Science Virtual Machine (DSVM).

What should you recommend?

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?

You plan to use a Data Science Virtual Machine (DSVM) with the open source deep learning frameworks Caffe2 and PyTorch.

You need to select a pre-configured DSVM to support the frameworks.

What should you create?

You are developing a data science workspace that uses an Azure Machine Learning service.

You need to select a compute target to deploy the workspace.

What should you use?

You are solving a classification task.

The dataset is imbalanced.

You need to select an Azure Machine Learning Studio module to improve the classification accuracy.

Which module should you use?

You are analyzing a dataset containing historical data from a local taxi company. You are developing a regression model.

You must predict the fare of a taxi trip.

You need to select performance metrics to correctly evaluate the regression model.

Which two metrics can you use? Each correct answer presents a complete solution?

NOTE: Each correct selection is worth one point.

You configure a Deep Learning Virtual Machine for Windows.

You need to recommend tools and frameworks to perform the following:

Build deep neural network (DNN) models

Perform interactive data exploration and visualization

Which tools and frameworks should you recommend? To answer, drag the appropriate tools to the correct tasks. Each tool 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 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?

You plan to provision an Azure Machine Learning Basic edition workspace for a data science project.

You need to identify the tasks you will be able to perform in the workspace.

Which three tasks will you be able to perform? Each correct answer presents a complete solution.

NOTE: Each correct selection is worth one point.

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?

You create an Azure Machine Learning compute resource to train models. The compute resource is configured as follows:

Minimum nodes: 2

Maximum nodes: 4

You must decrease the minimum number of nodes and increase the maximum number of nodes to the following values:

Minimum nodes: 0

Maximum nodes: 8

You need to reconfigure the compute resource.

What are three possible ways to achieve this goal? Each correct answer presents a complete solution.

NOTE: Each correct selection is worth one point.

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?

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.

OPTION:A

OPTION:B

OPTION:C

Use the Inference Clusters tab in Machine Learning Studio.

OPTION:D

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