AI0-001 AI Infrastructure and Technologies Practice Test 6 — 15 Questions

Question 1

A data scientist needs to train a deep learning model on a large image dataset. Which hardware component is specifically designed to accelerate deep learning training workloads?

Accepted Answer

GPU. B (GPU) is correct because GPUs contain thousands of parallel cores designed for matrix operations, which are fundamental to deep learning training. They significantly accelerate the forward and backward passes of neural networks compared to CPUs, making them the standard choice for training large image datasets.

Answer

TPU

Answer

NPU

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CPU

Question 2

An MLOps team wants to deploy a trained PyTorch model to production with low latency inference. The model must be interoperable across different frameworks and runtimes. Which approach is BEST?

Accepted Answer

Export the model to ONNX format and deploy using ONNX Runtime. Option D is correct because ONNX (Open Neural Network Exchange) provides a standardized, framework-agnostic format that ensures interoperability across different runtimes and hardware accelerators. By exporting the PyTorch model to ONNX and deploying with ONNX Runtime, the team achieves low-latency inference through graph optimizations and hardware-specific execution providers, while avoiding vendor lock-in.

Answer

Deploy the native PyTorch model using TorchServe

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Quantize the model to INT8 and deploy as a TensorFlow Lite model

Answer

Convert the model to TensorFlow SavedModel and deploy using TensorFlow Serving

Question 3

A company is deploying a real-time object detection model on a fleet of IoT cameras. The model must run at 30 FPS on a device with limited memory and no internet connectivity. Which combination of techniques is MOST suitable?

Accepted Answer

Apply INT8 quantization and pruning, then deploy using TensorFlow Lite. Option D is correct because INT8 quantization reduces model size and latency, while pruning removes redundant weights, making the model suitable for memory-constrained edge devices. TensorFlow Lite is optimized for on-device inference with no internet dependency, supporting real-time 30 FPS object detection on IoT cameras.

Answer

Use FP16 inference and deploy via Docker containers

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Use model distillation to create a smaller model and deploy via ONNX Runtime

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Deploy on a GPU-based edge server with a full PyTorch model

Question 4

A team is using an API from a cloud AI service to generate text. They notice that repeated requests with the same prompt return different outputs. They want consistent responses for testing. Which parameter should they adjust?

Accepted Answer

Set the temperature to 0. Setting the temperature to 0 makes the model deterministic, producing the same output for the same input, which is ideal for testing.

Answer

Increase the top_p parameter to 1.0

Answer

Set the frequency_penalty to 0

Answer

Increase the max_tokens parameter

Question 5

A company needs to store large volumes of unstructured data (PDFs, images, logs) for future AI model training. The data must be easily accessible by data scientists using Spark and must support cost-effective storage. Which data infrastructure is MOST appropriate?

Accepted Answer

Amazon S3 data lake. A data lake stores raw, unstructured data at low cost and integrates with Spark. Data warehouses are for structured, processed data; vector databases are for embeddings.

Answer

Snowflake data warehouse

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Relational database like Amazon RDS

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Pinecone vector database

Question 6

A machine learning engineer wants to track experiment parameters, metrics, and model artifacts across multiple runs. Which MLOps tool is specifically designed for experiment tracking?

Accepted Answer

Weights & Biases. Weights & Biases (W&B) is purpose-built for experiment tracking, offering a centralized dashboard to log hyperparameters, metrics, and model artifacts across runs. It provides automatic logging for popular frameworks (e.g., PyTorch, TensorFlow) and supports rich visualizations like loss curves and parallel coordinate plots, making it the correct choice for this specific requirement.

Answer

Apache Airflow

Answer

MLflow

Answer

Kubeflow

Question 7

An organization is deploying a large language model on-premises for compliance reasons. They need to serve inference requests with low latency. Which architecture should they use?

Accepted Answer

Containerize the model and deploy it on a Kubernetes cluster with autoscaling. Containerizing the model and deploying it on a Kubernetes cluster with autoscaling is the correct architecture because it provides horizontal scaling, low-latency inference through load-balanced pods, and supports on-premises deployment for compliance. Kubernetes can automatically scale replicas based on CPU/memory utilization or custom metrics (e.g., request queue depth), ensuring consistent response times under varying load.

Answer

Use a batch processing system like Apache Spark

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Use a serverless function like AWS Lambda

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Deploy the model as a REST API on a single powerful server

Question 8

A team uses a retrieval-augmented generation (RAG) system to answer questions from a large enterprise document repository. They observe that the generated answers sometimes contain information not present in the retrieved documents. What is the MOST likely cause?

Accepted Answer

The LLM's context window is too small to include all retrieved chunks. When the LLM's context window is too small to include all retrieved chunks, the model may generate information not present in the provided context to fill gaps, a phenomenon known as hallucination. This occurs because the model relies on its internal knowledge when relevant retrieved content is truncated, leading to fabricated details. Option D directly addresses this mismatch between retrieval capacity and generation constraints.

Answer

The vector database has low recall

Answer

The temperature setting is too high

Answer

The embedding model is not accurately representing document semantics

Question 9

A company wants to build an AI pipeline that processes streaming data from IoT sensors, performs feature engineering, trains a model incrementally, and deploys the updated model. Which data pipeline technology is BEST suited for the streaming ingestion step?

Accepted Answer

Apache Kafka. Apache Kafka is the best choice for the streaming ingestion step because it is a distributed event streaming platform designed for high-throughput, fault-tolerant ingestion of real-time data streams. It acts as a durable message broker that can ingest IoT sensor data in real time and make it available for downstream processing, which aligns perfectly with the requirement for streaming data ingestion.

Answer

Amazon S3

Answer

Apache Spark

Answer

Apache Airflow

Question 10

A developer wants to deploy a scikit-learn model as a REST API endpoint with minimal infrastructure management. Which cloud service is MOST appropriate?

Accepted Answer

Use AWS SageMaker to create a real-time endpoint. AWS SageMaker provides a fully managed service for deploying machine learning models as real-time endpoints with built-in scaling, monitoring, and automatic infrastructure management. It directly supports scikit-learn models via pre-built containers, eliminating the need for custom runtime setup or manual server configuration. This makes it the most appropriate choice for a developer seeking minimal infrastructure management.

Answer

Use AWS Lambda with a custom runtime

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Deploy on an EC2 instance manually

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Use Amazon ECS with manual Docker setup

Question 11

A team is using a cloud AI service with a pay-per-token pricing model. They want to minimize costs while maintaining response quality. Which strategy is MOST effective?

Accepted Answer

Use prompt caching for repeated query patterns. Prompt caching reduces costs by avoiding redundant token processing for repeated query patterns. The cloud AI service charges per token, so caching the prefix of frequent requests (e.g., system prompts or common context) means only the new, unique tokens are billed, directly lowering expenditure without sacrificing response quality.

Answer

Switch to a smaller, less capable model

Answer

Increase the batch size for API calls

Answer

Reduce the model's max_tokens to a very low value

Question 12

An engineer is deploying a model on edge devices with limited compute. The model was trained in PyTorch. They need to convert it to a format optimized for mobile CPUs. Which framework should they use?

Accepted Answer

TensorFlow Lite. TensorFlow Lite (TFLite) is specifically designed for on-device inference on mobile CPUs with limited compute, offering quantization and reduced model size. Since the model is trained in PyTorch, it can be converted to TFLite via ONNX or directly using PyTorch's export to TorchScript followed by conversion through TensorFlow. TFLite's optimized kernels for ARM CPUs make it the best choice for mobile CPU deployment.

Answer

OpenVINO

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ONNX Runtime Mobile

Answer

Core ML

Question 13

A data science team wants to implement a feature store to serve pre-computed features for both training and inference with low latency. Which TWO tools are commonly used for building a feature store?

Accepted Answer

Feast. Feast (Feature Store) is an open-source operational data system that manages and serves machine learning features to both training and inference pipelines with low latency. It provides a consistent feature definition API, offline serving for training, and online serving via a low-latency store like Redis or DynamoDB, making it a standard choice for feature store implementations.

Answer

Kubeflow

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Apache Hive

Answer

MLflow

Question 14

A company is building a secure AI system that must comply with GDPR. They want to allow users to request deletion of their personal data from training sets and model outputs. Which THREE techniques should they implement?

Accepted Answer

Differential privacy. Differential privacy (B) is correct because it adds calibrated noise to training data or model outputs, ensuring that the inclusion or exclusion of any individual's data does not significantly affect the model's behavior. This provides a mathematical guarantee of privacy, which is essential for GDPR compliance when handling personal data. By limiting information leakage, differential privacy helps protect user data even if deletion requests are not fully implemented.

Answer

Model ensembling

Answer

Federated learning

Question 15

A developer is choosing a vector database for a RAG application that requires real-time updates and millisecond query latency. Which TWO vector databases are best suited for this requirement?

Accepted Answer

Pinecone. Pinecone is a fully managed vector database designed for production-scale RAG applications, offering sub-10ms query latency and real-time index updates without requiring manual infrastructure tuning. Weaviate similarly provides native vector search with millisecond latency and supports real-time data ingestion through its auto-schema and incremental indexing, making both ideal for latency-sensitive, frequently updated RAG workloads.

Answer

MongoDB Atlas Vector Search

Answer

Redis with vector similarity module

Answer

PostgreSQL with pgvector