A team uses Kubeflow to orchestrate ML pipelines on Kubernetes. They need to add a monitoring step that checks for data drift after each pipeline run and alerts the team if drift is detected. What is the BEST way to implement this?

Use a Kubernetes CronJob to run drift detection separately

Add a Kubeflow pipeline component that runs a drift detection script and sends alerts

Manually check for drift after each run

Use a Kubernetes Service to monitor the pipeline

A retail company needs to generate personalized product recommendations for each customer. The model must score every customer's profile nightly, and the results are stored in a database for the next day's website. The scoring job takes about 20 minutes and must complete before the website opens. Which deployment strategy best fits this requirement?

Edge deployment on customer devices

Streaming deployment with a Kafka consumer

Real-time deployment behind a REST endpoint

Batch deployment, running the model on a schedule

A team uses MLflow to manage models. They have a model in the registry that is ready for production. They want to ensure that only approved models can be deployed. What MLflow feature should they use?

Model registry stages (e.g., Staging, Production)

CertNexusCertified Data Science Practitioner (CDSP)

Domain 6Objective 2

Deploy and Manage Models CERTIFIED-DATA-SCIENCE-PRACTITIONER Practice Questions (Page 5)

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 21–25

21application · medium
A data science team wants to track all their experiments, compare metrics, and register the best model for production. They also need to be able to serve that model later. Which tool should they use?
Select an answer first
22application · medium
A Kubernetes deployment for a model is experiencing increased traffic. The team wants to automatically adjust the number of replicas based on CPU utilization. What should they configure?
Select an answer first
23expert · hard
A team uses Kubeflow to orchestrate ML pipelines on Kubernetes. They need to add a monitoring step that checks for data drift after each pipeline run and alerts the team if drift is detected. What is the BEST way to implement this?
Select an answer first
24application · medium
A retail company needs to generate personalized product recommendations for each customer. The model must score every customer's profile nightly, and the results are stored in a database for the next day's website. The scoring job takes about 20 minutes and must complete before the website opens. Which deployment strategy best fits this requirement?
Select an answer first
25application · medium
A team uses MLflow to manage models. They have a model in the registry that is ready for production. They want to ensure that only approved models can be deployed. What MLflow feature should they use?
Select an answer first

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Deploy and Manage Models CERTIFIED-DATA-SCIENCE-PRACTITIONER Practice Questions (Page 5)

Questions 21–25

A data science team wants to track all their experiments, compare metrics, and register the best model for production. They also need to be able to serve that model later. Which tool should they use?

A Kubernetes deployment for a model is experiencing increased traffic. The team wants to automatically adjust the number of replicas based on CPU utilization. What should they configure?

A team uses Kubeflow to orchestrate ML pipelines on Kubernetes. They need to add a monitoring step that checks for data drift after each pipeline run and alerts the team if drift is detected. What is the BEST way to implement this?

A team uses MLflow to manage models. They have a model in the registry that is ready for production. They want to ensure that only approved models can be deployed. What MLflow feature should they use?

Keep studying — 6.0 Operationalizing the pipeline