AI0-001 AI Infrastructure and Technologies Practice Test 5 — 15 Questions

Question 1

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?

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

Accepted Answer

torch.nn.DataParallel. DataParallel (or DistributedDataParallel) is PyTorch's built-in feature to split batches across multiple GPUs. It is straightforward for single-machine multi-GPU training.

Answer

TorchScript tracing

Answer

torch.optim.SGD

Answer

PyTorch Lightning's zero_grad function

Question 2

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

Accepted Answer

It enables framework interoperability for model inference. ONNX provides a standardized, open format for representing machine learning models, enabling seamless interoperability between different frameworks (e.g., PyTorch, TensorFlow, scikit-learn). This allows a model trained in one framework to be deployed for inference using a different runtime or hardware accelerator without requiring retraining or manual conversion, which is a key advantage in heterogeneous production environments.

Answer

It automatically quantizes models to INT8

Answer

It compresses model size by 90%

Answer

It reduces training time

Question 3

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?

Accepted Answer

Enable batching of multiple queries into a single request. Batching multiple queries into a single request reduces the overhead of repeated gRPC connection setup, serialization, and network round trips for small payloads. This amortizes the fixed cost of each inference call across several queries, directly lowering per-query latency in high-throughput scenarios.

Answer

Convert the model to ONNX and use ONNX Runtime

Answer

Switch from gRPC to REST with HTTP/2

Answer

Use a larger instance type with more CPU

Question 4

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?

Accepted Answer

Weaviate. Weaviate and Pinecone are both purpose-built vector databases that natively support high-performance approximate nearest neighbor (ANN) search using algorithms like HNSW (Weaviate) or proprietary indexing (Pinecone). They offer managed cloud services with automatic scaling, making them ideal for low-latency similarity search over millions of product embeddings without requiring manual infrastructure management.

Answer

Chroma

Answer

pgvector (PostgreSQL extension)

Answer

Amazon DynamoDB

Question 5

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?

Accepted Answer

GPU with thousands of CUDA cores. A GPU with thousands of CUDA cores is the most suitable hardware for parallel matrix operations because deep learning training involves massive matrix multiplications and tensor operations that can be decomposed into thousands of independent threads. CUDA cores execute these threads in a massively parallel SIMT (Single Instruction, Multiple Thread) fashion, achieving significantly higher throughput than a CPU for such workloads, which leads to faster training times.

Answer

TPU designed for TensorFlow

Answer

CPU with high clock speed

Answer

FPGA for reconfigurable logic

Question 6

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?

Accepted Answer

Deploy the model as a REST API endpoint using a containerized inference server. Deploying the model as a REST API endpoint using a containerized inference server (e.g., TensorFlow Serving, TorchServe, or NVIDIA Triton Inference Server) is the most appropriate approach for handling thousands of inference requests per second. These servers are designed for high-throughput, low-latency serving, support horizontal scaling via load balancers, and provide built-in batching and model versioning. REST APIs are stateless and can be easily integrated with existing web infrastructure, making them ideal for production-scale inference.

Answer

Export the model to ONNX format and use a batch processing pipeline

Answer

Use gRPC streaming for all inference requests

Answer

Run the model directly on the client device

Question 7

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?

Accepted Answer

MLflow Tracking. MLflow Tracking is the correct complementary feature because it provides a centralized API and UI for logging parameters, metrics, and artifacts (e.g., model checkpoints, datasets) from each SageMaker Pipeline run. This enables the team to version their pipeline executions and compare experiments across different runs, directly addressing the requirement for tracking and versioning.

Answer

MLflow Models

Answer

MLflow Projects

Answer

MLflow Model Registry

Question 8

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?

Accepted Answer

Apache Spark with Structured Streaming. Apache Spark with Structured Streaming is best suited because it provides a unified, scalable engine for both stream processing and batch processing, enabling real-time feature engineering on Kafka streams and direct writing to a data lake (e.g., Parquet format in Amazon S3). Its micro-batch or continuous processing model integrates natively with Kafka, allowing exactly-once semantics and low-latency transformations for ML inference pipelines.

Answer

Apache Airflow

Answer

TensorFlow Data Validation

Answer

SageMaker Processing jobs

Question 9

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?

Accepted Answer

A data lake like Amazon S3. Amazon S3 is a highly scalable object storage service that can store unstructured text data in its native format (e.g., CSV, JSON, Parquet) and supports SQL-based queries via services like Amazon Athena or S3 Select. This makes it ideal as a primary data source for AI model training while enabling analytics without requiring data transformation or loading into a separate system.

Answer

A vector database like Pinecone

Answer

A data warehouse like Snowflake

Answer

A NoSQL database like DynamoDB

Question 10

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?

Accepted Answer

Kubeflow Katib. Katib is the hyperparameter tuning component in Kubeflow. Pipelines orchestrate workflows; KFServing is for inference; Notebooks are for development.

Answer

KFServing

Answer

Kubeflow Notebooks

Answer

Kubeflow Pipelines

Question 11

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?

Accepted Answer

Use a sidecar container that pre-warms the model and set a minimum replica count. A sidecar warm-up agent and a minimum replica count keep pods ready. Increasing resources may not fix cold starts; autoscaling based on request count may lag; vertical scaling helps but not directly.

Answer

Configure the autoscaler based on request count with a shorter cooldown period

Answer

Increase CPU and memory requests for the inference container

Answer

Switch to vertical pod autoscaling

Question 12

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?

Accepted Answer

Use prompt caching to avoid reprocessing identical inputs. Implementing rate limits prevents exceeding token budgets; prompt caching reduces repeated API calls. Fine-tuning is expensive; increasing max tokens may increase costs; using a less capable model may harm quality.

Answer

Fine-tune the model on previous marketing copy

Answer

Switch to a cheaper, less capable model

Answer

Increase max tokens per response to ensure completeness

Question 13

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?

Accepted Answer

Weaviate. Pinecone, Weaviate, and pgvector are vector databases designed for similarity search. Snowflake and S3 are not optimized for vector search.

Answer

Snowflake

Answer

Amazon S3

Question 14

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?

Accepted Answer

Vertex AI Pipelines. Vertex AI Pipelines orchestrates ML workflows; Cloud Build automates builds; Cloud Deploy manages deployments. BigQuery is for analytics; GKE is for containers but not CI/CD specific.

Answer

BigQuery

Answer

Google Kubernetes Engine (GKE)

Question 15

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