AI0-001 AI Infrastructure and Technologies Practice Test 2 — 15 Questions

Question 1

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 2

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. Option D is correct because 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 3

A team wants to deploy a large language model on edge devices with limited memory and compute. They need to reduce model size by at least 50% while preserving accuracy. Which combination of techniques is most effective?

Accepted Answer

Apply INT8 quantization and weight pruning. INT8 quantization reduces the precision of weights and activations from 32-bit to 8-bit, cutting memory usage by approximately 75% for those tensors, while weight pruning removes redundant connections, often achieving over 50% size reduction with minimal accuracy loss when combined. Together, they directly address the constraints of edge devices by shrinking the model footprint and computational requirements without requiring a complete architecture redesign.

Answer

Distill the model into a smaller architecture without quantization or pruning

Answer

Use FP32 precision and increase batch size

Answer

Use FP16 quantization and add more layers

Question 4

A company uses a vector database to store embeddings for a RAG application. Users report that some queries return irrelevant results. Which adjustment is most likely to improve relevance?

Accepted Answer

Change the similarity metric from cosine to dot product and use a different embedding model. Switching from cosine similarity to dot product and using a different embedding model can improve relevance because the choice of similarity metric must align with the embedding model's training objective. Many modern embedding models (e.g., text-embedding-ada-002) are optimized for dot product or cosine similarity, but if the current model was trained for cosine and the queries are not normalized, dot product may better capture magnitude and direction. A different model may also produce higher-quality embeddings that better represent semantic relationships, directly addressing irrelevant results.

Answer

Reduce the chunk size of documents

Answer

Switch from an HNSW index to a flat index

Answer

Increase the top-k retrieval count

Question 5

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 6

Which AWS service would a developer use to integrate a pre-built foundation model into an application via API, without managing underlying infrastructure?

Accepted Answer

Amazon Bedrock. Amazon Bedrock is a fully managed service that provides access to pre-built foundation models (FMs) from providers like AI21 Labs, Anthropic, Cohere, Meta, and Stability AI via a unified API. It eliminates the need to manage underlying infrastructure such as servers, GPUs, or scaling, allowing developers to integrate FMs into applications with minimal operational overhead.

Answer

Amazon EC2

Answer

Amazon SageMaker

Answer

AWS Lambda

Question 7

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 8

An organization must ensure that an AI model deployed on an IoT device meets stringent latency requirements. The model is currently in FP32 and runs at 200ms per inference on the device; the target is 50ms. Which technique will provide the greatest latency reduction with the least accuracy loss?

Accepted Answer

Quantize the model to INT8. Quantizing the model from FP32 to INT8 reduces the precision of weights and activations, which directly decreases memory bandwidth and computational load. On IoT devices with limited resources, this typically yields a 2-4x speedup, bringing the 200ms inference time close to the 50ms target, while INT8 quantization often retains over 90% of the original accuracy when using calibration techniques.

Answer

Apply weight pruning to remove 50% of parameters

Answer

Switch from TensorFlow Lite to Core ML

Answer

Distill the model into a smaller architecture

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

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A data warehouse like Snowflake

Answer

A NoSQL database like DynamoDB

Question 10

Which open-source framework is commonly used for building, training, and deploying machine learning models and provides high-level APIs like Keras?

Accepted Answer

TensorFlow. TensorFlow is the correct answer because it is the open-source framework that provides high-level APIs like Keras for building, training, and deploying machine learning models. Keras, now integrated as tf.keras, offers a user-friendly interface for rapid prototyping while TensorFlow handles the underlying computation graph, distributed training, and model serving via TensorFlow Serving.

Answer

Hugging Face Transformers

Answer

scikit-learn

Answer

PyTorch

Question 11

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 12

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 13

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 14

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 15

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)