Data Science Model Deployments and Cloud Computing on GCP - Lab - Code Walkthrough Using Kubeflow and Python

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Information Technology (IT), Architecture, Social Studies
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University
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Hard
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7 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the first step in setting up a Cueflow pipeline on Vertex AI?
Creating a custom folder in GCS bucket
Executing code blocks in the cloud
Installing Python 3.9
Opening the CC Fraud Model Cueflow notebook
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the purpose of the 'pipeline root' variable in the Cueflow pipeline?
To define the region for deployment
To store the final model
To specify the folder for output of each step
To set the base image for containers
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which algorithm is used for training the model in the pipeline?
K-Nearest Neighbors
Support Vector Machine
Linear Regression
Random Forest Classifier
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the output format of the trained model in the pipeline?
CSV file
Pickle file
JSON file
XML file
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which package is NOT required for deploying the model in the pipeline?
Google Cloud AI Platform
Psychic Learn
Pandas
Qflow
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the purpose of the 'create endpoint' function in the deployment process?
To fetch data from GCS
To create an endpoint for the model
To create a display name for the model
To compile the pipeline
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the final step in executing the pipeline?
Uploading the model to a registry
Defining the model name
Running the start pipeline command
Compiling the pipeline
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