Question 1

A financial institution is implementing an AI-driven fraud detection system that needs to process millions of transactions daily in real-time. The system must rapidly identify suspicious activity and trigger alerts, while also continuously learning from new data to improve accuracy. Which architecture is most appropriate for this scenario?

Options :

A : Single GPU server with local SSD storage for both training and inference

B : Edge-only deployment with ARM processors for both training and inference

C : Hybrid setup with multi-GPU servers for training and edge devices for inference

D : CPU-based servers with cloud storage for centralized processing

Answer: C

Question 2

A financial institution is implementing a real-time fraud detection system using deep learning models. The system needs to process large volumes of transactions with very low latency to identify fraudulent activities immediately. During testing, the team observes that the system occasionally misses fraudulent transactions under heavy load, and latency spikes occur. Which strategy would best improve the system's performance and reliability?

Options :

A : Increase the dataset size by including more historical transaction data.

B : Deploy the model on a CPU cluster instead of GPUs to handle the processing.

C : Reduce the complexity of the model to decrease the inference time.

D : Implement model parallelism to split the model across multiple GPUs.

Answer: D

Question 3

Which of the following has been the most critical factor enabling the recent rapid improvements and adoption of AI in various sectors?

Options :

A : The rise of user-friendly AI frameworks and libraries.

B : The availability of large, annotated datasets for training AI models.

C : Increased investment in AI research and development by large tech companies.

D : The development and adoption of AI-specific hardware like GPUs and TPUs.

Answer: D

Question 4

When implementing an MLOps pipeline, which component is crucial for managing version control and tracking changes in model experiments?

Options :

A : Orchestration Platform

B : Model Registry

C : Continuous Integration (CI) System

D : Artifact Repository

Answer: B

Question 5

You have completed a data mining project and have discovered several key insights from a large and complex dataset. You now need to present these insights to stakeholders in a way that clearly communicates the findings and supports data-driven decision-making. Which of the following approaches would be most effective for visualizing insights from large datasets to support decision-making in AI projects? (Select two)

Options :

A : Visualize all data in a single pie chart.

B : Use a heatmap to represent correlations between variables.

C : Generate a detailed text report with all the raw data.

D : Create interactive dashboards using tools like Tableau or Power BI.

E : Present a simple line chart showing one aspect of the data.

Answer: B,D

Confidently Practice Online with Free NCA-AIIO Exam Cram

Buying Options: