DTSC 3300(D501) Machine Learning DevOps Latest OA Guide 2025

D501 DTSC 3300

Machine Learning DevOps

2.0 Credits

Objective Assessment Review (Qns &
Ans)

2025




©2025

, Question 1:
In a modern MLOps pipeline, which component is responsible for
managing version control of both data and model artifacts to ensure
reproducibility?
- A. Model Registry
- B. Data Version Control (DVC)
- C. Continuous Integration Server
- D. Container Orchestration Platform

Correct ANS: B. Data Version Control (DVC)
Rationale:
DVC (Data Version Control) is specifically designed to track versions of
datasets, models, and code in machine learning projects. It enables
reproducibility by linking model artifacts with specific data versions,
whereas a model registry focuses primarily on models and a CI server
automates tests.

---

Question 2:
Which of the following best describes the primary goal of implementing
Continuous Integration/Continuous Deployment (CI/CD) practices in
machine learning workflows?
- A. To increase model complexity
- B. To automate model retraining and streamline deployment
- C. To maximize training dataset size
- D. To replace human decision-making entirely

Correct ANS: B. To automate model retraining and streamline
deployment
Rationale:
CI/CD practices in machine learning (MLOps) focus on automating the
model building, testing, and deployment processes so that model
updates can be delivered rapidly, reliably, and reproducibly to production
©2025

,environments.

---

Question 3:
In the context of containerized machine learning deployment, which
technology offers orchestration capabilities to manage deployments,
scaling, and monitoring of containerized ML models?
- A. Docker Compose
- B. Kubernetes
- C. Apache Spark
- D. Jenkins

Correct ANS: B. Kubernetes
Rationale:
Kubernetes is widely used for orchestrating containerized applications,
including machine learning models. It helps automatically manage
scaling, failure recovery, and rolling updates, making it ideal for deploying
and managing ML services.

---

Question 4:
Which tool is designed explicitly for tracking experiments,
hyperparameters, and performance metrics across multiple ML training
runs in an MLOps workflow?
- A. Jenkins
- B. MLflow
- C. Terraform
- D. Grafana

Correct ANS: B. MLflow
Rationale:
MLflow provides an experiment tracking component that enables data
scientists to record parameters, code versions, metrics, and artifacts
©2025

, from multiple model runs, supporting reproducibility and performance
comparison.

---

Question 5:
A machine learning platform implements automated retraining of models
when data drift is detected. Which technique is used to monitor changes
in the distribution of input data over time?
- A. Model ensembling
- B. Drift detection algorithms
- C. Model compression
- D. Batch normalization

Correct ANS: B. Drift detection algorithms
Rationale:
Drift detection algorithms continuously monitor the statistical properties
of input data and flag significant changes. When a drift is detected, it
suggests that retraining may be necessary to maintain model accuracy in
production.

---

Question 6:
Which approach in ML DevOps emphasizes provisioning infrastructure
and resources through code, ensuring version control of the entire
deployment environment?
- A. Configuration Management
- B. Infrastructure as Code (IaC)
- C. Virtualization
- D. Serverless Computing

Correct ANS: B. Infrastructure as Code (IaC)
Rationale:
IaC refers to managing infrastructure through code and version control,
©2025