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

Question 1

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?

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

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 2

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 3

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 4

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 5

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 6

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 7

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 8

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 9

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 10

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 11

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 12

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

Question 13

A machine learning engineer needs to train a deep neural network on a large image dataset. Which hardware component is specifically optimized for this task due to its high parallel processing capability and is commonly used in AI training?

Accepted Answer

Graphics Processing Unit (GPU). Graphics Processing Units (GPUs) are specifically optimized for the parallel processing required in deep neural network training. Their architecture contains thousands of smaller cores designed to handle multiple matrix operations simultaneously, which is the core computation in backpropagation and forward passes of neural networks. This makes GPUs the standard choice for training large image datasets in AI.

Answer

Central Processing Unit (CPU)

Answer

Neural Processing Unit (NPU)

Answer

Tensor Processing Unit (TPU)

Question 14

An MLOps team observes that their production inference API experiences increasing latency as more concurrent requests arrive. They need to scale horizontally while maintaining session state of preprocessing steps. Which deployment strategy should they implement?

Accepted Answer

Deploy multiple instances behind a round-robin load balancer with sticky sessions. Sticky sessions (session affinity) ensure that all requests from a given client are routed to the same backend instance, preserving the in-memory session state of preprocessing steps. Combined with a round-robin load balancer, this allows horizontal scaling while maintaining stateful behavior, which is essential for the described latency issue under concurrent load.

Answer

Deploy stateless containers without session persistence

Answer

Use a single larger GPU instance to handle all requests

Answer

Implement a message queue (e.g., Kafka) to buffer requests

Question 15

An organization wants to centralize experiment tracking, model versioning, and deployment management across its data science team. Which MLOps platform is specifically designed for experiment tracking and model registry?

Accepted Answer

MLflow. MLflow is an open-source MLOps platform that provides a centralized experiment tracking API (MLflow Tracking) and a model registry (MLflow Model Registry) for versioning, staging, and deploying machine learning models. It is specifically designed to address the need for experiment tracking and model lifecycle management, making it the correct choice for this scenario.

Answer

Apache Airflow

Answer

Weights & Biases

Answer

Kubeflow