CertifyDeckCargando contenido…
Generative AI Leader · Sample questions
10 real questions from the CertifyDeck Generative AI Leader bank, spread across the domains of the official Google Cloud Certified: Generative AI Leader skills outline, with the correct answer and a full explanation of why the other options are wrong. They are original questions written from the public syllabus, not exam dumps, and they are the same style you will find in the practice tests and mock exams.
Correct answer: C. RAG reduces errors, but output depends on the retrieved data
Google Cloud describes RAG as a way to reduce hallucinations and answer more accurately by enriching the model's context, not as a guarantee. The quality of what is retrieved still matters: incomplete, inconsistent or inaccurate data leads to equally flawed retrieved information. RAG is in fact mainly used to bring in private organizational knowledge, so limiting it to public data is also wrong.
Correct answer: D. Ready-made Google-quality search over enterprise data
Agent Search, previously called Vertex AI Search, is a Google Search-quality retrieval engine that works as an out-of-the-box semantic and keyword search engine for RAG over enterprise data, with connectors to many source systems. Fresh public web results with nothing to manage describe Grounding with Google Search instead, and scoring how well a response is supported describes the check grounding API.
Correct answer: B. Pub/Sub
Pub/Sub is an asynchronous, scalable messaging service used to ingest user interaction and server events and to stream data from applications and IoT devices, which makes it the entry point for the data ingestion stage. Agent Platform AutoML is tempting because the project is about ML, but it belongs to the model training stage and assumes the data has already been collected and prepared.
Correct answer: C. Relevance to deployment conditions
Training data should be relevant to, and representative of, the real-world data a model will encounter once deployed; data from a single market does not reflect spending patterns in the other countries. Frequent updates are tempting because stale data also hurts models, but fresher data from the same single market still would not represent the other eleven countries.
Correct answer: A. Veo
Model Garden lists Veo for text-to-video and image-to-video generation, so it matches the output modality the startup needs. Imagen is the closest distractor, but it generates still images from text rather than video; Chirp handles speech-to-text, and Gemma is a family of lightweight open models.
Correct answer: D. Model Context Protocol (MCP)
Google Cloud's agent platform natively supports the Model Context Protocol so agents can connect to existing databases, enterprise systems and single sources of truth through an open standard. The Agent2Agent Protocol addresses a different need, letting agents built by different parties communicate with each other, and the Agent Development Kit is a framework for building agents rather than a connection standard.
Correct answer: D. Swapping models while tool connections and policies stay intact
Google Cloud says customers can swap models to take advantage of new performance gains or lower costs while keeping their underlying logic, tool connections and identity policies intact. Standardizing on a single model would create exactly the lock-in the retailer fears, giving up the choice that a multi-model platform provides.
Correct answer: D. Search results and answers respect each user's existing access rights
Gemini Enterprise delivers access-controlled search results and generative answers, with SSO and user-level access, so each lawyer only sees content that their own permissions already allow in the connected systems. Copying everything into one shared notebook would do the opposite and expose every file to every lawyer, and tuning a model on the files does not enforce per-user permissions.
Correct answer: B. Use a gen AI model with prompt engineering
Google Cloud's decision flow recommends generative AI with prompt engineering when sufficient training data is not available, and summarization is a generative task. Custom-training a traditional model gives more control, but it depends on having enough training data and takes longer, which conflicts with both stated constraints.
Correct answer: D. Use cases with high value and high feasibility
Google Cloud recommends focusing first on use cases in the high-value, high-feasibility quadrant to accelerate time to value. High-value but low-feasibility use cases may be attractive, but data, technical or risk obstacles delay results, so they are better addressed once foundations are in place.
Answer each question before opening the explanation, and write down why you discarded the other options. On Generative AI Leader, most mistakes come from choosing an option that is technically possible but does not meet every requirement in the scenario.
These 10 questions are a taste of the format, not a diagnostic. To know which domains you fail, practice by domain for free and take a timed mock exam that mirrors the official weights and duration.
Start for free, no sign-up or card. Every question includes an explanation like the ones above, and the mock exam reproduces the official Generative AI Leader format.