AI Infrastructure and Technologies

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?

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?

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?

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?

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?

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?

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?

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?

A data scientist is using PyTorch to train a custom NLP model. The training is slow on a single GPU. They want to speed up training by using multiple GPUs on a single machine. Which PyTorch feature should they use?

Which of the following is a key advantage of using ONNX (Open Neural Network Exchange) format for model deployment?

A team is deploying a BERT-based question-answering model using a REST API endpoint with gRPC for internal microservices. They notice high latency for small payloads. Which optimization is MOST likely to reduce latency?

A startup is building a recommendation system that requires low-latency similarity search over millions of product embeddings. They need a vector database that offers high performance and has a managed cloud option. Which TWO databases are best suited for this requirement?

A data scientist needs to train a deep learning model on a large image dataset. Which hardware is most suitable for parallel matrix operations and faster training compared to a CPU?

An ML engineer wants to deploy a model as a REST API that can scale to handle thousands of inference requests per second. Which serving approach is most appropriate?

An AI team uses SageMaker Pipelines to orchestrate their ML workflow. They need to version the pipeline and track experiments across runs. Which complementary MLflow feature should they integrate?

A team uses Apache Kafka to stream real-time sensor data for ML inference. They need to process the stream, perform feature engineering, and store results in a data lake. Which tool is best suited for this streaming ML pipeline?

A company wants to store unstructured text data for AI model training while enabling SQL-based queries for analytics. Which storage solution should they use as the primary data source?

A team uses Kubeflow to manage ML workflows on Kubernetes. They want to automate hyperparameter tuning for a training job. Which Kubeflow component should they use?

During inference, a model served via a REST API occasionally returns high latency due to cold starts. The team uses a containerized service on Kubernetes with horizontal pod autoscaling. Which solution minimizes cold start impact while controlling cost?

A company uses Azure OpenAI to generate marketing copy. They need to manage costs and ensure consistent response quality. Which TWO actions should they take?

An organization is building a recommendation system that requires low-latency vector similarity search. They need to store and query millions of embeddings. Which THREE technologies are appropriate for this task?

A data science team uses Vertex AI for model training and deployment. They want to implement CI/CD for ML pipelines. Which THREE Google Cloud services should they integrate?

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?

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?

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?

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?

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?

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?

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?

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

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?

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?

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?

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?

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)

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?

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?

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?

A data scientist is using a Hugging Face transformer model for a sentiment analysis task. They want to optimize inference latency for a mobile app. Which model format and framework combination is BEST suited for on-device deployment?

A machine learning engineer is designing a pipeline to train a computer vision model using PyTorch on a large dataset stored in an S3 data lake. They need to preprocess images (resize, normalize) and stream them efficiently to GPUs. Which THREE components are essential in this pipeline? (Select THREE.)

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?

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?

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?

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?

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

A healthcare startup needs to deploy an AI model for real-time patient monitoring on IoT devices with limited battery and compute. The model must run locally with minimal latency. Which TWO strategies are most appropriate?

A machine learning engineer needs to containerize a PyTorch model for deployment on Kubernetes. Which THREE tools or formats should they use?

A data scientist wants to develop a computer vision model using transfer learning. They need a framework that provides pre-trained models and easy-to-use APIs for data augmentation and training. Which TWO frameworks are best suited for this task?