IBM AI Engineering Model Deployment Fundamentals

1. Which tool is used for building and storing Docker container images?

Docker Registry or Docker Hub

Python IDE

SQL database

Text editor

2. In the context of model deployment, what does scalability mean?

The ability to handle increased load by adding resources without degrading performance

Making models smaller in file size

Reducing the number of features

Eliminating model updates

3. What is the role of logging in deployed AI systems?

To track predictions, errors, and system behavior for debugging and auditing

To increase model accuracy automatically

To reduce storage requirements

To eliminate data preprocessing

4. Which serialization format is commonly used to save trained machine learning models?

Pickle, ONNX, or SavedModel formats

JPEG images

HTML documents

Video files

5. What is the purpose of A/B testing in model deployment?

To compare performance of two model versions and determine which performs better

To train models faster

To eliminate all user feedback

To increase model size

6. Which of the following is a best practice for securing deployed AI models?

Implementing authentication, encryption, and access control

Sharing API keys publicly

Disabling all monitoring

Storing credentials in code files

7. In model deployment, what does latency refer to?

The time it takes for a model to return a prediction after receiving input

The total amount of data processed

The number of users accessing the model

The cost of cloud infrastructure

8. What is the advantage of using microservices architecture for AI model deployment?

Enables independent scaling, updates, and deployment of models

Requires all models in one application

Eliminates the need for APIs

Reduces system complexity

9. Which IBM service provides model governance and monitoring capabilities?

IBM Watson OpenScale

IBM Cloud Storage

IBM Data Catalog

IBM Security Service

10. What is the primary purpose of feature scaling in model deployment?

To normalize input data to the same range as training data

To increase model file size

To eliminate outliers completely

To reduce the number of features

11. What is the primary advantage of containerizing a machine learning model using Docker?

Ensures consistency across development, testing, and production environments

Automatically improves model accuracy

Eliminates the need for version control

Reduces the size of the training dataset

12. In Kubernetes, what is a pod?

The smallest deployable unit that can contain one or more containers

A storage device for data

A type of machine learning algorithm

A monitoring dashboard

13. What does REST API provide in the context of model deployment?

A standard interface for client applications to request model predictions

Real-time storage of raw data

Automatic model retraining

Direct access to training code

14. Which metric is most important for monitoring a deployed classification model in production?

Accuracy, precision, recall, and F1-score

Only model file size

Training time duration

Number of features used

15. What is the purpose of model versioning in production environments?

To track changes, enable rollback, and manage multiple model iterations

To increase model size

To eliminate historical data

To reduce inference speed

16. Which framework is commonly used for serving machine learning models at scale?

TensorFlow Serving or KServe

Jupyter Notebook

Git version control

Excel spreadsheets

17. What is model drift in the context of deployed AI systems?

A gradual decline in model performance due to changes in data distribution

The random movement of data points

An increase in model complexity

A decrease in training dataset size

18. Which of the following is a key component of the model lifecycle in production?

Model monitoring and retraining

Data deletion after training

Eliminating all model versions

Disabling user feedback mechanisms

19. In model deployment, what does CI/CD stand for?

Continuous Integration / Continuous Deployment

Code Integration / Code Delivery

Continuous Improvement / Continuous Development

Cloud Infrastructure / Cloud Distribution

20. Which IBM cloud service is most commonly used for deploying and managing containerized applications?

IBM Cloud Kubernetes Service (IKS)

IBM Cloud Object Storage

IBM Watson Assistant

IBM DataStax

IBM AI Engineering Model Deployment Fundamentals Quiz