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

Question 1

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

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Redis with vector similarity module

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PostgreSQL with pgvector

Question 2

A data scientist is choosing a hardware accelerator for training a large transformer model. Which of the following is specifically designed for deep learning workloads and offers the highest throughput for matrix multiplications?

Accepted Answer

TPU. The TPU (Tensor Processing Unit) is an application-specific integrated circuit (ASIC) designed by Google specifically to accelerate deep learning workloads. Its systolic array architecture is optimized for the matrix multiplications and convolutions that dominate transformer model training, delivering the highest throughput among the listed options for these operations.

Answer

GPU

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NPU

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CPU

Question 3

An ML team deploys a model on edge devices using INT8 quantization. They notice a significant drop in accuracy on a subset of classes. Which technique should they apply to recover accuracy without increasing model size?

Accepted Answer

Apply quantization-aware training (QAT). Quantization-aware training (QAT) simulates INT8 quantization effects during the forward pass of training, allowing the model to learn weights and activations that are more robust to the lower precision. This recovers accuracy lost during post-training quantization without increasing the model's size, as the architecture and number of parameters remain unchanged.

Answer

Use pruning to remove less important weights

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Increase the model architecture size

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Switch to FP16 quantization

Question 4

A data engineering team is designing a data pipeline to process streaming sensor data and feed it into an ML model for anomaly detection. Which THREE components are essential for this pipeline?

Accepted Answer

Amazon S3 as a data lake for storing raw sensor data. Amazon S3 is essential as a data lake for storing raw sensor data because it provides durable, scalable, and cost-effective object storage that can serve as a central repository for streaming data before and after processing. In a streaming pipeline, raw data must be persisted for reprocessing, historical analysis, and compliance, and S3's integration with Apache Spark and Kafka makes it a natural landing zone for sensor data.

Answer

Apache Airflow for scheduling recurring batch jobs

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Snowflake as a real-time streaming destination

Question 5

A team is selecting a vector database for a RAG application that requires low-latency similarity search on millions of embeddings. They prioritize ease of use and fully managed cloud service. Which TWO options meet these requirements?

Accepted Answer

Pinecone. Pinecone is a fully managed vector database designed for production-scale RAG applications, offering low-latency similarity search on millions of embeddings without requiring users to manage infrastructure. Its serverless architecture and simple API align directly with the team's priorities of ease of use and a fully managed cloud service.

Answer

pgvector

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Chroma

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Milvus

Question 6

A company is deploying a large language model via a REST API using a cloud AI service. They expect high traffic and need to minimize latency while controlling costs. Which THREE strategies should they implement?

Accepted Answer

Enable prompt caching. Prompt caching stores the intermediate key-value (KV) cache from previous inference runs for identical or similar prompts. When a cached prompt is reused, the model skips recomputing the attention keys and values for the cached portion, significantly reducing time-to-first-token (TTFT) latency and lowering compute cost per request. This is especially effective for high-traffic scenarios where many users submit the same or slightly varied prompts.

Answer

Auto-scale the number of API endpoints

Answer

Quantize the model to FP16

Question 7

A team is evaluating MLOps platforms to manage experiments, track model versions, and deploy models to production. Which THREE platforms provide end-to-end capabilities including experiment tracking and model deployment?

Accepted Answer

SageMaker Pipelines. SageMaker Pipelines is correct because it provides a fully managed MLOps service that integrates experiment tracking, model versioning, and automated deployment pipelines within the AWS ecosystem. It allows teams to define, visualize, and execute end-to-end workflows, including model training, evaluation, and deployment to production endpoints, all with native integration to SageMaker's experiment management and model registry.

Answer

Kubeflow

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Weights & Biases

Question 8

A machine learning team is training a large transformer model on a text corpus. They need to reduce training time while maintaining model accuracy. Which hardware configuration would be MOST effective for this task?

Accepted Answer

Use a cluster of GPUs with data parallelism. GPUs are optimized for the parallel computations required in deep learning training, offering significant speedups over CPUs. TPUs are also effective but less accessible and more specialized. The question specifies 'most effective' for training a transformer model, which aligns with GPU acceleration.

Answer

Use a high-core-count CPU with large RAM

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Use a single GPU with model parallelism

Answer

Use a single TPU with model parallelism

Question 9

An organization wants to integrate an AI-powered summarization feature into their existing web application. The AI service will be called via API. Which factor is MOST important to consider for cost management?

Accepted Answer

Token pricing of the AI model. Token pricing directly impacts cost because API calls are billed based on the number of tokens (input + output). Understanding token usage helps estimate and control expenses.

Answer

Authentication method (API key vs. OAuth)

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Rate limits per minute

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Network latency to the API endpoint

Question 10

A data science team is deploying a real-time fraud detection model on edge devices in retail stores. The model must infer under 10ms and fit within 50MB memory. Which combination of techniques should the team apply?

Accepted Answer

Model quantization to INT8 and pruning of low-weight connections. Quantization reduces model precision (e.g., FP32 to INT8) to shrink memory and speed up inference, while pruning removes redundant parameters. Distillation can further compress. These are standard for edge deployment.

Answer

Model parallelism and distributed inference

Answer

Increase batch size and use FP16 precision

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Train a larger model and use distillation to transfer knowledge

Question 11

A company has a TensorFlow model trained on-premises and wants to deploy it on AWS SageMaker for scalable inference. What is the BEST way to package the model for deployment?

Accepted Answer

Package the model in a Docker container with a TensorFlow serving script and push to Amazon ECR. SageMaker expects models in a container format; the inference container should include the model artifacts and the serving code, allowing SageMaker to host it on scalable endpoints.

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Convert the model to ONNX and upload to SageMaker

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Upload the .h5 file to S3 and create a SageMaker endpoint directly

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Use SageMaker Studio to train the model again from scratch

Question 12

A data engineer is building a pipeline to process streaming clickstream data and feed it into a real-time ML feature store. Which tool is BEST suited for the streaming ingestion?

Accepted Answer

Apache Kafka. Apache Kafka is the industry standard for high-throughput, fault-tolerant streaming data ingestion. It can handle real-time clickstream data and integrate with feature stores.

Answer

Amazon S3

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Apache Airflow

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Apache Spark (batch mode)

Question 13

A developer is building a mobile app that uses a pre-trained image classification model on-device. Which framework should they use to run the model on iOS devices?

Accepted Answer

Core ML. Core ML is Apple's framework for on-device machine learning inference on iOS. TensorFlow Lite is for mobile and embedded, but Core ML is native to iOS and optimized.

Answer

Hugging Face Transformers

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TensorFlow Lite

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PyTorch Mobile

Question 14

An ML team uses Kubeflow to orchestrate a pipeline that includes data preprocessing, model training, and evaluation. The pipeline runs on a Kubernetes cluster. After a cluster upgrade, the pipeline fails at the training step with an 'OOMKilled' error. What is the MOST likely cause?

Accepted Answer

The Kubernetes node's memory resources were not correctly allocated to the pod's resource requests or limits. OOMKilled indicates the container exceeded its memory limit. The resource requests/limits likely were not adjusted for the new cluster configuration, or the node's allocatable memory decreased after upgrade.

Answer

The training code has a memory leak

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The pipeline definition is missing a step dependency

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The training data is corrupted

Question 15

A security team needs to ensure that all data used for AI model training in the cloud is encrypted at rest and in transit. Which set of measures meets this requirement on AWS?

Accepted Answer

Enable S3 default encryption with SSE-S3 and use HTTPS for API calls. AWS provides KMS for at-rest encryption and TLS for in-transit encryption. These are standard practices to secure data across the AI pipeline.

Answer

Use Security Groups and Network ACLs

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Use client-side encryption and store keys in AWS Secrets Manager

Answer

Enable VPC peering and use VPN connections