AI0-001 AI Infrastructure and Technologies Practice Test 2 — 15 Questions

Question 1

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?

Choose an answer to begin — your selection is scored in the full session.

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.

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Increase the learning rate to converge faster

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Switch to a CPU-based instance

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Reduce the number of attention heads in the model

Question 2

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. 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.

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Use gRPC streaming for lower latency

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

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Deploy the model on edge devices to avoid centralised logging

Question 3

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.

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Increase the chunk size of documents

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Switch from cosine similarity to Euclidean distance

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Reduce the number of retrieved documents from 5 to 3

Question 4

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.

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Add more GPUs and distribute the model

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Increase the batch size to process multiple requests simultaneously

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Switch from GPU to CPU

Question 5

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.

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Amazon S3

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pgvector

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BigQuery

Question 6

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

Accepted Answer

Use a data policy that prohibits training on customer data. The 'No Training' data policy option explicitly prevents the cloud AI service 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.

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Set data retention to 30 days

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Enable content filtering

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Enable rate limiting

Question 7

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.

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GPU

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NPU

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FPGA

Question 8

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.

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Kubeflow Pipelines component

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Cluster Autoscaler

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Vertical Pod Autoscaler (VPA)

Question 9

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.

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Apache Airflow

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Docker

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Kubeflow

Question 10

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.

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Pinecone

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pgvector

Question 11

A data scientist needs to deploy a PyTorch model to production with low-latency inference. The model must be served as a REST API and should support GPU acceleration. Which combination of tools is MOST suitable for this task?

Accepted Answer

Docker container with a FastAPI application and Nvidia GPU support. It combines Docker containerization with FastAPI for a lightweight REST API and NVIDIA GPU support (via nvidia-docker or NVIDIA Container Toolkit) to enable low-latency GPU-accelerated inference. This stack directly meets the requirements of low-latency inference, REST API serving, and GPU acceleration without unnecessary overhead.

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ONNX runtime with a gRPC endpoint on a CPU-only node

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Apache Spark with MLlib to serve the model in batch mode

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Kubeflow Pipelines to deploy the model as a scheduled job

Question 12

A company is building a recommendation system that uses user embeddings stored in a vector database. The system must retrieve the top 10 most similar items for a given user query. Which vector database feature is MOST critical for this task?

Accepted Answer

Approximate nearest neighbor (ANN) search. Approximate nearest neighbor (ANN) search is the most critical feature because it enables the vector database to efficiently find the top-10 most similar items to a user query embedding without scanning the entire dataset. Unlike exact nearest neighbor search, ANN algorithms (e.g., HNSW, IVF) trade a small amount of accuracy for massive performance gains, which is essential for real-time recommendation systems handling millions of high-dimensional vectors.

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Built-in data versioning

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ACID transaction support

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SQL query interface

Question 13

A developer wants to integrate an AI-powered text summarization API into their application. They need to authenticate securely and manage usage limits. What is the standard mechanism for authenticating with cloud-based AI services?

Accepted Answer

Include an API key in the HTTP request header. Cloud-based AI services, including text summarization APIs, standardize authentication via API keys passed in the HTTP header (e.g., `Authorization: Bearer ` or `x-api-key: `). This method keeps credentials out of URLs and request bodies, preventing exposure in logs or caches, and aligns with RESTful API best practices and OWASP guidelines for secure API access.

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Provide a username and password in the request body

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Embed the API key in the URL query string

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Use a digital certificate for each request

Question 14

A data engineering team is building a pipeline to ingest streaming user activity data, process it in real-time, and store features in a feature store for ML models. Which streaming technology is BEST suited for this real-time data ingestion and processing?

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 provides publish-subscribe messaging, durable log storage, and stream processing capabilities, making it ideal for ingesting streaming user activity data and feeding it into a feature store for ML models.

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Apache Spark SQL

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Apache Airflow

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Apache Hadoop MapReduce

Question 15

A team has trained a large transformer model that achieves 95% accuracy but requires 8 GB of GPU memory for inference. They need to deploy it on edge devices with only 2 GB of memory and minimal accuracy loss. Which combination of techniques should they apply?

Accepted Answer

Apply INT8 quantization, pruning, and model distillation. The team needs to reduce the model's memory footprint from 8 GB to under 2 GB while preserving accuracy. INT8 quantization reduces memory by 4x (from 32-bit floats to 8-bit integers), weight pruning removes redundant connections, and model distillation trains a smaller student model to mimic the teacher, collectively achieving the required compression with minimal accuracy loss.

Answer

Use model distillation to create a smaller student model

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Apply INT8 quantization and weight pruning only

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Use FP16 precision and increase batch size