A team has containerized their model and wants to deploy it on Kubernetes. They need to ensure that the model is always running and can be accessed by other services via a stable network name. Which Kubernetes resources should they create?

A StatefulSet and a PersistentVolume

What is Kubeflow primarily used for?

Orchestrating machine learning pipelines on Kubernetes

Creating interactive data visualizations

CertNexusCertified Data Science Practitioner (CDSP)

Domain 6Objective 2

Deploy and Manage Models CERTIFIED-DATA-SCIENCE-PRACTITIONER Practice Questions (Page 4)

Part of the 6.0 Operationalizing the pipeline domain, which accounts for 5-8% of the CERTIFIED-DATA-SCIENCE-PRACTITIONER exam.

36questions here

8free pages

8concepts

5-8%of the exam

Questions 16–20

16foundation · easy
After writing a Dockerfile for a machine learning model, which command is used to build the Docker image?
Select an answer first
17foundation · easy
A team deploys a new version of a recommendation model. After a few days, they notice the model's performance has degraded. They want to quickly revert to the previous version. Which production environment consideration is most relevant to this need?
Select an answer first
18application · medium
A team has containerized their model and wants to deploy it on Kubernetes. They need to ensure that the model is always running and can be accessed by other services via a stable network name. Which Kubernetes resources should they create?
Select an answer first
19application · medium
An organization wants to standardize its ML workflows on Kubernetes. They need a platform that provides pipeline components for training, serving, and monitoring, all integrated with Kubernetes. Which tool should they adopt?
Select an answer first
20foundation · easy
What is Kubeflow primarily used for?
Select an answer first

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Deploy and Manage Models CERTIFIED-DATA-SCIENCE-PRACTITIONER Practice Questions (Page 4)

Questions 16–20

After writing a Dockerfile for a machine learning model, which command is used to build the Docker image?

A team deploys a new version of a recommendation model. After a few days, they notice the model's performance has degraded. They want to quickly revert to the previous version. Which production environment consideration is most relevant to this need?

A team has containerized their model and wants to deploy it on Kubernetes. They need to ensure that the model is always running and can be accessed by other services via a stable network name. Which Kubernetes resources should they create?

An organization wants to standardize its ML workflows on Kubernetes. They need a platform that provides pipeline components for training, serving, and monitoring, all integrated with Kubernetes. Which tool should they adopt?

What is Kubeflow primarily used for?

Keep studying — 6.0 Operationalizing the pipeline