Adaface Sample MLOps Questions

Here are some sample MLOps questions from our premium questions library (10273 non-googleable questions).

Skills

🧐 Question
Medium Model Registry Configuration Model Registry CI/CD Pipeline Version Control	Solve
A data science team is using a CI/CD pipeline for their machine learning models. They need to set up a model registry that satisfies the following requirements: 1. Automatically track model lineage 2. Perform A/B testing between model versions 3. Support automated rollback if performance degrades 4. Enable easy model deployment to production Which of the following configurations would best meet these requirements? A: Use MLflow for model registry with Git for versioning, Jenkins for CI/CD, and Kubernetes for deployment B: Use DVC for model versioning, GitLab CI for pipelines, and manual deployment scripts C: Use MLflow for tracking experiments only, store models in S3, and use Airflow for orchestration D: Use Git LFS for model storage, GitHub Actions for CI/CD, and Docker containers for deployment E: Use TensorFlow Serving for deployment, MongoDB for storing model metadata, and manual versioning

🧐 Question

Medium

Model Registry Configuration

Model Registry

CI/CD Pipeline

Version Control

Solve

A data science team is using a CI/CD pipeline for their machine learning models. They need to set up a model registry that satisfies the following requirements:

1. Automatically track model lineage
2. Perform A/B testing between model versions
3. Support automated rollback if performance degrades
4. Enable easy model deployment to production

Which of the following configurations would best meet these requirements?

A: Use MLflow for model registry with Git for versioning, Jenkins for CI/CD, and Kubernetes for deployment
B: Use DVC for model versioning, GitLab CI for pipelines, and manual deployment scripts
C: Use MLflow for tracking experiments only, store models in S3, and use Airflow for orchestration
D: Use Git LFS for model storage, GitHub Actions for CI/CD, and Docker containers for deployment
E: Use TensorFlow Serving for deployment, MongoDB for storing model metadata, and manual versioning

	🧐 Question	🔧 Skill
	Medium Model Registry Configuration Model Registry CI/CD Pipeline Version Control	2 mins MLOps	Solve
A data science team is using a CI/CD pipeline for their machine learning models. They need to set up a model registry that satisfies the following requirements: 1. Automatically track model lineage 2. Perform A/B testing between model versions 3. Support automated rollback if performance degrades 4. Enable easy model deployment to production Which of the following configurations would best meet these requirements? A: Use MLflow for model registry with Git for versioning, Jenkins for CI/CD, and Kubernetes for deployment B: Use DVC for model versioning, GitLab CI for pipelines, and manual deployment scripts C: Use MLflow for tracking experiments only, store models in S3, and use Airflow for orchestration D: Use Git LFS for model storage, GitHub Actions for CI/CD, and Docker containers for deployment E: Use TensorFlow Serving for deployment, MongoDB for storing model metadata, and manual versioning

🧐 Question

🔧 Skill

Medium

Model Registry Configuration

Model Registry

CI/CD Pipeline

Version Control

2 mins

MLOps

Solve

	🧐 Question	🔧 Skill	💪 Difficulty	⌛ Time
	Model Registry Configuration Model Registry CI/CD Pipeline Version Control	MLOps	Medium	2 mins	Solve
A data science team is using a CI/CD pipeline for their machine learning models. They need to set up a model registry that satisfies the following requirements: 1. Automatically track model lineage 2. Perform A/B testing between model versions 3. Support automated rollback if performance degrades 4. Enable easy model deployment to production Which of the following configurations would best meet these requirements? A: Use MLflow for model registry with Git for versioning, Jenkins for CI/CD, and Kubernetes for deployment B: Use DVC for model versioning, GitLab CI for pipelines, and manual deployment scripts C: Use MLflow for tracking experiments only, store models in S3, and use Airflow for orchestration D: Use Git LFS for model storage, GitHub Actions for CI/CD, and Docker containers for deployment E: Use TensorFlow Serving for deployment, MongoDB for storing model metadata, and manual versioning

🧐 Question

🔧 Skill

💪 Difficulty

⌛ Time

Model Registry Configuration

Model Registry

CI/CD Pipeline

Version Control

MLOps

Medium

2 mins

Solve

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Adaface Sample MLOps Questions

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