AI0-001 AI Infrastructure and Technologies Practice Test 4 — 15 Questions

Question 1

A data science team is deploying a deep learning model for real-time inference on edge devices with limited power and memory. Which model optimisation technique would be MOST effective for reducing latency and memory footprint while maintaining acceptable accuracy?

Accepted Answer

Apply quantisation to convert weights from FP32 to INT8. Quantization reduces the precision of model weights from 32-bit floating point (FP32) to 8-bit integer (INT8), which directly cuts memory usage by 75% and accelerates inference on edge devices by leveraging integer arithmetic. This technique is specifically designed for resource-constrained environments where power and memory are limited, and it typically preserves accuracy within 1-2% of the original model.

Answer

Use a larger batch size during inference

Answer

Train the model for more epochs to improve convergence

Answer

Increase the number of layers to improve feature extraction

Question 2

A company uses AWS SageMaker to train a large language model. The training job fails with an out-of-memory error. The team is already using the largest available GPU instance. Which step should the team take to resolve the issue without modifying the model architecture?

Accepted Answer

Enable gradient accumulation in the training script. Gradient accumulation allows the model to simulate a larger batch size by accumulating gradients over several forward/backward passes before performing an optimizer step. This reduces per-step memory usage because the gradients are stored and averaged rather than requiring the entire batch to be loaded into GPU memory at once. Since the team cannot change the instance type or model architecture, enabling gradient accumulation is the correct approach to resolve the out-of-memory error.

Answer

Increase the learning rate to converge faster

Answer

Switch to a CPU-based instance

Answer

Reduce the number of attention heads in the model

Question 3

An AI team wants to version control datasets, track experiments, and log model parameters across multiple projects. Which MLOps platform is specifically designed for experiment tracking and model management?

Accepted Answer

MLflow. MLflow is an open-source MLOps platform specifically designed for experiment tracking, model management, and reproducibility. It provides a unified API to log parameters, metrics, and artifacts across multiple projects, making it the correct choice for versioning datasets, tracking experiments, and managing models.

Answer

SageMaker Pipelines

Answer

Vertex AI Pipelines

Answer

Kubeflow

Question 4

A financial institution requires that all AI model predictions be explainable and auditable for regulatory compliance. Which model serving approach should be used to meet these requirements?

Accepted Answer

Deploy the model as a containerised microservice with REST API and log all request/response pairs. Option D is correct because logging all request/response pairs provides a complete audit trail, which is essential for regulatory compliance in financial institutions. Containerized microservices with REST APIs are stateless and can be easily integrated with centralized logging systems (e.g., ELK stack) to capture every prediction for explainability and review. This approach ensures that model decisions are transparent and can be traced back to specific inputs, satisfying both explainability and auditability requirements.

Answer

Use gRPC streaming for lower latency

Answer

Export the model to ONNX format and run on a dedicated inference server

Answer

Deploy the model on edge devices to avoid centralised logging

Question 5

A company is implementing a retrieval-augmented generation (RAG) pipeline using a vector database. They notice that the retrieved documents often lack relevance to the query. Which adjustment would MOST improve retrieval quality?

Accepted Answer

Use a better embedding model fine-tuned on domain-specific data. Retrieval quality in a RAG pipeline is fundamentally determined by the semantic alignment between query embeddings and document embeddings. A domain-specific fine-tuned embedding model captures the unique terminology, context, and relationships within the company's data, producing vector representations that are far more relevant than those from a generic model. This directly improves the similarity search results in the vector database, leading to higher-quality retrieved documents.

Answer

Increase the chunk size of documents

Answer

Switch from cosine similarity to Euclidean distance

Answer

Reduce the number of retrieved documents from 5 to 3

Question 6

An organisation needs to deploy PyTorch models on mobile devices with minimal latency. Which framework or tool should they use to convert and optimise the model for on-device inference?

Accepted Answer

TorchScript. TorchScript is the correct choice because it is PyTorch's native model serialization and optimization format, designed specifically for deploying PyTorch models on mobile devices with minimal latency. It allows you to trace or script a PyTorch model into a static graph that can be run efficiently on iOS and Android via the PyTorch Mobile runtime, without the overhead of Python interpreter.

Answer

TensorFlow Lite

Answer

Keras for mobile

Answer

ONNX Runtime with Core ML conversion

Question 7

A data engineer needs to process streaming clickstream data for real-time feature engineering in an ML pipeline. Which data pipeline technology is BEST suited for this task?

Accepted Answer

Apache Kafka. Apache Kafka is the best choice because it is a distributed streaming platform designed for high-throughput, fault-tolerant, real-time data ingestion and processing. It can capture clickstream events as they occur and make them immediately available for feature engineering in an ML pipeline, supporting exactly-once semantics and low-latency delivery.

Answer

Apache Spark in batch mode

Answer

Snowflake

Answer

Apache Airflow

Question 8

A team is using Hugging Face Transformers to serve an LLM via a REST API. They notice high latency during inference. The model is deployed on a single GPU. Which optimisation would reduce inference latency WITHOUT changing the model architecture?

Accepted Answer

Use model quantisation to FP16. FP16 quantization reduces the memory footprint and computational load of the model by using half-precision floating-point numbers, which allows the GPU to process more operations per second and reduces memory bandwidth usage. This directly lowers inference latency without altering the model's architecture, making it the correct choice for a single-GPU deployment.

Answer

Add more GPUs and distribute the model

Answer

Increase the batch size to process multiple requests simultaneously

Answer

Switch from GPU to CPU

Question 9

A healthcare AI startup must store and query high-dimensional embeddings of medical records for a RAG system. They need low-latency similarity search at scale. Which database should they choose?

Accepted Answer

Pinecone. Pinecone is a fully managed vector database optimized for high-dimensional embeddings and low-latency similarity search at scale. It provides built-in indexing (e.g., HNSW), automatic sharding, and serverless scaling, making it ideal for RAG systems that require fast approximate nearest neighbor (ANN) queries on medical record embeddings.

Answer

Amazon S3

Answer

pgvector

Answer

BigQuery

Question 10

A company wants to use a pre-trained model from Azure OpenAI but must ensure that customer data is not used to improve the service. Which configuration should they choose?

Accepted Answer

Use the 'No Training' data policy option. Option C is correct because the 'No Training' data policy option explicitly prevents Azure OpenAI from using customer prompts and completions to retrain or improve the underlying models. This configuration is essential for compliance with data privacy requirements, ensuring that customer data remains isolated from model improvement pipelines.

Answer

Set data retention to 30 days

Answer

Enable content filtering

Answer

Enable rate limiting

Question 11

Which AI accelerator is specifically designed by Google to accelerate the training and inference of large neural networks, especially in their cloud environment?

Accepted Answer

TPU. The Tensor Processing Unit (TPU) is Google's custom-designed ASIC specifically built to accelerate the training and inference of large neural networks. Unlike general-purpose hardware, TPUs are optimized for TensorFlow workloads and are a core component of Google Cloud's AI infrastructure, offering high throughput for matrix operations common in deep learning.

Answer

GPU

Answer

NPU

Answer

FPGA

Question 12

A team is deploying a model on Kubernetes using Kubeflow. They want to automatically scale the number of inference pods based on request latency. Which Kubernetes-native feature should they configure?

Accepted Answer

Horizontal Pod Autoscaler (HPA) with custom metrics. The Horizontal Pod Autoscaler (HPA) with custom metrics is the correct choice because it allows scaling based on application-level metrics like request latency, not just CPU or memory. By configuring HPA to use a custom metric (e.g., from Prometheus or a metrics adapter), the team can automatically adjust the number of inference pods to maintain target latency thresholds, which is essential for responsive inference serving.

Answer

Kubeflow Pipelines component

Answer

Cluster Autoscaler

Answer

Vertical Pod Autoscaler (VPA)

Question 13

A machine learning engineer wants to track hyperparameter experiments and compare results across runs. Which TWO tools are best suited for this purpose? (Choose 2)

Accepted Answer

MLflow. MLflow is correct because it provides a centralized tracking server and API to log hyperparameters, metrics, and artifacts for each run, enabling easy comparison across experiments. Weights & Biases is correct because it offers a cloud-hosted dashboard with real-time logging, hyperparameter sweeps, and collaborative comparison features, making it ideal for tracking and comparing runs.

Answer

Apache Airflow

Answer

Docker

Answer

Kubeflow

Question 14

A data scientist needs to store large volumes of unstructured log data for future AI model training. They also need to run SQL-based analytics on the data. Which THREE services are appropriate for this requirement? (Choose 3)

Accepted Answer

Snowflake. Snowflake is correct because it is a cloud-native data warehouse that supports both structured and semi-structured data (like JSON, Avro, Parquet) via its VARIANT data type, enabling SQL-based analytics on unstructured log data. It also integrates with cloud storage (e.g., Amazon S3) for storing large volumes of raw logs, making it suitable for AI model training pipelines.

Answer

Pinecone

Answer

pgvector

Question 15

An organisation is deploying a fine-tuned LLM for internal use. They need to ensure the API endpoint is secure and cost-effective. Which TWO measures should they implement? (Choose 2)

Accepted Answer

Implement API key authentication. API key authentication (Option A) is a fundamental security measure that ensures only authorized clients can access the LLM endpoint. It provides a simple, lightweight mechanism to validate requests without the overhead of full OAuth, making it both secure and cost-effective for internal deployments.

Answer

Enable content filtering

Answer

Disable logging to reduce storage costs

Answer

Use gRPC instead of REST