1Z0-1122-26 Tests Picking the Right Learning Type and OCI Service — Not Coding
An entry-level exam for anyone new to AI on Oracle Cloud. Half is vendor-neutral AI, ML, DL and LLM vocabulary; the rest maps a business input, such as a call recording, invoice or photo, to the right OCI service.
Check Your Readiness →Most candidates understand Oracle Cloud Infrastructure AI Foundations Associate (1Z0-1122-26) concepts — and still fail. This exam tests how you apply knowledge under pressure.
Section names come from secondary sources and Oracle publishes no weights; the seven sections are grouped into six here. Forty definition-level items in 60 minutes: a one-line business example asks which learning type, architecture or OCI feature fits.
Send the audio files straight to OCI Language
OCI Language expects text. Transcribe first with OCI Speech, typically as batch jobs on files in Object Storage, then run aspect-based sentiment in OCI Language, which scores each topic mentioned separately.
Load the data into OCI Data Science notebooks
Oracle Machine Learning runs in-database through SQL, Python and R, so data never moves. OCI Data Science suits open-source Python workflows with notebooks, model catalog and deployments, but requires loading data into the platform.
Use the default action and read the query log
runsql is the default and executes the query. showsql returns the generated SQL without running it; explainsql explains it; narrate turns results into prose; chat skips SQL generation entirely.
Deploy it because training accuracy is high
This is overfitting: the model memorized training data. Add more data, simplify the model or apply regularisation, and judge it on held-out data. Inference never improves the model; only training changes weights.
Logistic regression is a classifier: a sigmoid outputs a probability and a threshold turns it into a class. Regression predicts a continuous number. With no labels at all, the answer is clustering such as k-means, not classification.
OCI Vision image classification labels the whole image and answers what, not where. Object detection returns bounding boxes for each item. A custom Vision model, trained on your labelled images without code, is needed for your own products.
OCI Document Understanding key-value extraction returns named fields for receipts, invoices and IDs, and table extraction preserves rows and columns. Vision text detection only returns raw lines and positions, which downstream systems cannot use directly.
On-demand mode is pay-per-use inference on shared capacity with pretrained models. Fine-tuning and hosting custom models require a dedicated AI cluster, which reserves GPU capacity for one tenancy and gives predictable throughput.
Embedding models turn text into numeric vectors for semantic search and RAG; chat models generate text. In the database, the VECTOR data type stores embeddings beside business data, with HNSW or IVF indexes for approximate search.
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1Z0-1122-26 rewards matching each business input to the right service. Check your readiness today.