Get Started with the OCI AI Portfolio
Free Oracle Cloud Infrastructure 2025 AI Foundations Associate practice — 6 questions on Get Started with the OCI AI Portfolio, with explanations. No sign-up. Full 12-question mixed test →
A data science team already has a model trained with scikit-learn and wants to deploy it as an HTTP endpoint with autoscaling for real-time inference, while retaining full control of the model artifact inside their own OCI tenancy. Which OCI Data Science capability directly fits this need?
OCI Data Science Model Deployment takes a registered model artifact from the Model Catalog and exposes it as a managed HTTP endpoint with autoscaling, giving the team full control over their custom model while OCI manages the serving infrastructure.
An organization needs to train a 70-billion parameter foundation model from scratch and requires ultra-low-latency interconnect between thousands of GPUs distributed across multiple bare metal hosts. Which OCI infrastructure capability is specifically designed to meet this requirement?
OCI Superclusters connect bare metal GPU instances using RDMA-based cluster networking, delivering the ultra-low-latency, high-throughput interconnect required for distributed training of very large foundation models at massive GPU scale.
A company wants business analysts to query their Oracle Database 23ai tables using plain natural-language questions, with the database automatically translating the question into SQL and returning results, without moving the data out of the database. Which OCI AI capability should they use?
Select AI in Oracle Database 23ai integrates generative AI directly into the database so analysts can ask natural-language questions that are translated into SQL and executed against the data in place, with no data movement required.
In the OCI Generative AI service, an enterprise wants to fine-tune a pretrained foundation model using their own labeled dataset while ensuring the base model weights used during training are isolated from other tenants. Which resource must they provision?
OCI Generative AI dedicated AI clusters come in two types: fine-tuning clusters, which are provisioned specifically to customize a base model on isolated, tenant-specific compute, and hosting clusters, which serve inference. Fine-tuning requires the fine-tuning cluster type.
Which statement accurately distinguishes OCI AI Services from the OCI Generative AI service?
OCI AI Services expose ready-to-use, pretrained APIs for specific tasks like vision, speech, and language, whereas OCI Generative AI focuses on large language models for generative tasks (chat, summarization, text generation) and supports optional fine-tuning.
A logistics company needs to detect unusual patterns in real-time sensor readings from delivery trucks but has no in-house data science team and wants the fastest path to production using a ready-made capability. Which part of the OCI AI portfolio should they use?
OCI AI Services Anomaly Detection is a pretrained, ready-to-use API designed specifically to identify unusual patterns in time-series sensor data, requiring no in-house data science expertise or model building.
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