AI0-001 AI Infrastructure and Technologies Practice Test 3 — 15 Questions

Question 1

A machine learning engineer needs to deploy a PyTorch model for real-time inference with low latency. The model uses custom operators that are not supported by standard ONNX conversion. Which deployment approach is MOST appropriate?

Accepted Answer

Deploy the model using TorchServe with a custom handler. TorchServe is the native serving solution for PyTorch models and supports custom operators through custom handlers, allowing you to implement arbitrary preprocessing, inference, and postprocessing logic in Python. This approach avoids the need for ONNX conversion entirely, which is critical when custom operators are not supported by the ONNX standard. It also provides built-in features like model versioning, batching, and metrics for low-latency real-time inference.

Answer

Use TensorFlow Serving with a saved model format

Answer

Wrap the model in a Flask app and deploy on a VM

Answer

Convert the model to ONNX and serve with ONNX Runtime

Question 2

A data scientist is training a large language model on a custom dataset using PyTorch on AWS. The training is taking too long due to GPU memory constraints. The team wants to use multiple GPUs across instances with minimal code changes. Which AWS service should they use?

Accepted Answer

Amazon SageMaker with distributed training libraries. SageMaker distributed training libraries support data parallelism and model parallelism with minimal code changes, enabling multi-GPU training across instances efficiently.

Answer

AWS Elastic Fabric Adapter (EFA)

Answer

AWS Batch with GPU instances

Answer

AWS ParallelCluster with Slurm

Question 3

A company wants to build a real-time anomaly detection system for IoT sensor data using edge AI. The model must run on resource-constrained devices with minimal power consumption. Which model optimization technique is MOST important?

Accepted Answer

Model quantization (INT8). Quantization reduces model precision (e.g., FP32 to INT8), decreasing model size and computation, which is critical for resource-constrained edge devices.

Answer

Use FP32 precision

Answer

Increase the number of layers

Answer

Use a larger batch size

Question 4

A team is building a retrieval-augmented generation (RAG) pipeline. They need to store embeddings of company documents and perform fast similarity searches. Which data store is BEST suited for this task?

Accepted Answer

Pinecone. Pinecone is a purpose-built vector database designed for storing and querying high-dimensional embeddings with fast approximate nearest neighbor (ANN) search. In a RAG pipeline, embeddings of company documents must be retrieved quickly to feed relevant context to the LLM, and Pinecone’s optimized indexing (e.g., HNSW or IVF) and serverless scaling make it the ideal choice for this task.

Answer

Snowflake

Answer

Apache Kafka

Answer

Amazon S3

Question 5

A data engineering team needs to orchestrate a complex ML pipeline that involves data extraction, transformation, model training, and deployment. They require scheduling, monitoring, and retry logic. Which MLOps tool is BEST suited for this task?

Accepted Answer

Apache Airflow. Apache Airflow is a workflow orchestration tool that supports complex DAGs, scheduling, monitoring, and retries, making it ideal for ML pipelines.

Answer

Weights & Biases

Answer

Kubeflow

Answer

MLflow

Question 6

A company uses Azure OpenAI to generate customer support responses. The team notices that repeated queries with similar context incur high costs due to token usage. They want to reduce costs without affecting response quality. Which strategy is MOST effective?

Accepted Answer

Implement prompt caching. Prompt caching stores and reuses tokens from previous queries, reducing token consumption for similar requests and lowering costs without quality loss.

Answer

Use a larger model to improve efficiency

Answer

Increase the frequency penalty

Answer

Reduce the max_tokens parameter

Question 7

A developer is using Hugging Face Transformers to fine-tune a BERT model for sentiment analysis. They want to track experiments, log metrics, and compare runs. Which MLOps tool should they integrate?

Accepted Answer

MLflow. MLflow is the correct choice because it is purpose-built for experiment tracking, metric logging, and run comparison in machine learning workflows. It provides an API to log parameters, metrics, and artifacts, and its UI allows easy comparison of different fine-tuning runs, which directly matches the developer's need to track experiments and compare runs for a BERT sentiment analysis model.

Answer

Apache Airflow

Answer

Docker

Answer

Kubeflow

Question 8

A company is deploying a computer vision model to smartphones for offline object detection. The model was trained in PyTorch. Which format should they use for deployment on iOS devices?

Accepted Answer

Core ML. Core ML is Apple's framework for on-device machine learning on iOS, and PyTorch models can be converted to Core ML format.

Answer

TorchScript

Answer

ONNX

Answer

TensorFlow Lite

Question 9

A data scientist is building a recommendation system using Apache Spark for feature engineering. They need to process streaming user click data in real-time before feeding into the model. Which tool should they use for the streaming data ingestion?

Accepted Answer

Apache Kafka. Apache Kafka is the correct choice because it is a distributed streaming platform designed for high-throughput, fault-tolerant, real-time data ingestion. It acts as a durable message broker that can ingest streaming click data and make it available for Spark Structured Streaming to process in micro-batches or continuous processing mode, which is essential for real-time feature engineering in a recommendation system.

Answer

Amazon S3

Answer

Airflow

Answer

Snowflake

Question 10

A team is deploying a model on AWS SageMaker and needs to handle variable traffic patterns with automatic scaling based on request latency. They want to minimize costs during low traffic. Which endpoint configuration should they use?

Accepted Answer

Configure automatic scaling with a target tracking metric based on latency. Option D is correct because SageMaker's automatic scaling with a target tracking metric based on latency allows the endpoint to dynamically adjust the number of instances in response to real-time request latency, ensuring cost efficiency during low traffic while maintaining performance during spikes. This approach uses a predefined or custom metric (e.g., SageMakerVariantInvocationsPerInstance) to trigger scaling policies, minimizing over-provisioning and idle costs.

Answer

Use AWS Lambda with SageMaker

Answer

Provision a fixed number of instances with Multi-Model Endpoints

Answer

Use a single large instance to handle peak traffic

Question 11

Which hardware accelerator is specifically designed by Google for training and inference of machine learning models, particularly their TensorFlow framework?

Accepted Answer

TPU. TPU (Tensor Processing Unit) is Google's custom ASIC designed to accelerate ML workloads, especially with TensorFlow.

Answer

NPU

Answer

FPGA

Answer

GPU

Question 12

A company is using Google Cloud Vertex AI for model training. They want to automate the retraining pipeline when new data arrives in BigQuery. Which Vertex AI feature should they use?

Accepted Answer

Vertex AI Pipelines. Vertex AI Pipelines is the correct choice because it enables you to define, automate, and orchestrate end-to-end ML workflows, including retraining models when new data arrives. By integrating with BigQuery triggers or Cloud Scheduler, you can set up a pipeline that automatically ingests new data, preprocesses it, retrains the model, and deploys the updated version—all without manual intervention.

Answer

Vertex AI Prediction

Answer

Vertex AI Model Registry

Answer

Vertex AI Feature Store

Question 13

A data scientist is deploying a model on edge devices using TensorFlow Lite. The model currently uses FP32 precision. Which TWO techniques can reduce the model size and improve inference speed without significant accuracy loss? (Choose TWO.)

Accepted Answer

Post-training quantization to INT8. Post-training quantization to INT8 reduces model size by converting FP32 weights and activations to 8-bit integers, which also speeds up inference on edge devices by leveraging integer-optimized hardware. This technique typically preserves accuracy within 1–2% of the original FP32 model, making it suitable for deployment on resource-constrained devices.

Answer

Use a larger batch size during inference

Answer

Increase the number of layers

Answer

Convert to FP16 precision

Question 14

A company is building a multi-modal AI application that processes text, images, and audio. They need a unified platform to store embeddings for all modalities, perform hybrid search (vector + metadata filtering), and scale to millions of vectors. Which THREE services are suitable for this purpose? (Choose THREE.)

Accepted Answer

Weaviate. Weaviate is a purpose-built vector database that natively supports multi-modal embeddings (text, images, audio) through its vectorizer modules and provides hybrid search combining vector similarity with metadata filtering (e.g., using GraphQL or REST APIs). It is designed to scale to millions of vectors with built-in sharding and replication, making it a strong fit for the described unified platform.

Answer

Amazon S3

Answer

Snowflake

Question 15

A team is using Kubeflow to orchestrate ML workflows on Kubernetes. They need to ensure reproducibility, track experiments, and share models across the organization. Which THREE components or tools should they integrate? (Choose THREE.)

Accepted Answer

Kubeflow Pipelines. Kubeflow Pipelines is a core component of Kubeflow that enables the definition, deployment, and management of end-to-end ML workflows on Kubernetes. It provides a platform for building reproducible pipelines by capturing the entire workflow as a directed acyclic graph (DAG) of containerized steps, ensuring that each run can be exactly recreated. This directly addresses the team's need for reproducibility and orchestration within their existing Kubernetes environment.

Answer

Apache Airflow

Answer

Weights & Biases