100+ Free GCP GenAI Engineer Practice Questions

Pass your Google Cloud Professional Cloud GenAI Engineer exam on the first try — instant access, no signup required.

✓ No registration✓ No credit card✓ No hidden fees✓ Start practicing immediately

~65-75% Pass Rate

100+ Questions

100% Free

1 / 10

Question 1

Score: 0/0

Which Google Cloud service offers a unified platform for building and deploying generative AI solutions, including access to Gemini models, Imagen, and third-party models such as Llama and Claude?

Vertex AI

BigQuery ML

Cloud Run

Dialogflow CX

to track

2026 Statistics

Key Facts: GCP GenAI Engineer Exam

50-60

Exam Questions

Google Cloud

~70%

Passing Score

Google Cloud (estimated)

120 min

Exam Duration

Google Cloud

$200

Exam Fee

Google Cloud USD

Vertex AI

Core Platform

Gemini, Search, Agents

2 years

Validity

Google Cloud recertification

The Google Cloud Professional Cloud GenAI Engineer exam has 50-60 multiple-choice / multi-select questions in 120 minutes with a passing score of approximately 70%. Released in 2024, it focuses on building production GenAI apps on Vertex AI — including Gemini, Vertex AI Search, Agent Builder, RAG, tuning, evaluation, and MLOps. Valid 2 years. Exam fee is $200. Delivered via Kryterion Webassessor onsite or online proctored.

Sample GCP GenAI Engineer Practice Questions

Try these sample questions to test your GCP GenAI Engineer exam readiness. Each question includes a detailed explanation. Start the interactive quiz above for the full 100+ question experience with AI tutoring.

1Which Google Cloud service offers a unified platform for building and deploying generative AI solutions, including access to Gemini models, Imagen, and third-party models such as Llama and Claude?

A.Vertex AI

B.BigQuery ML

C.Cloud Run

D.Dialogflow CX

Explanation: Vertex AI is Google Cloud's unified ML/GenAI platform. It provides Vertex AI Studio, Model Garden (Gemini, Imagen, Veo, Chirp, Codey, plus partner models from Anthropic, Meta, Mistral), Workbench notebooks, Pipelines, Endpoints, Feature Store, and the Gen AI Evaluation Service. BigQuery ML brings ML to SQL but is data-warehouse-centric; Cloud Run hosts containers; Dialogflow CX is a conversational AI product.

2A team needs to ground Gemini responses in their private corporate documents stored in Cloud Storage. Which service is best suited to provide a managed RAG pipeline?

A.Vertex AI Search (Discovery Engine)

B.Cloud SQL

C.BigQuery

D.Pub/Sub

Explanation: Vertex AI Search (built on Discovery Engine) is a managed search-and-grounding service that ingests unstructured, structured, or website data, builds indices, and supports generative AI summaries with grounding and multi-turn conversations. Cloud SQL/BigQuery store data but don't natively provide grounded generation; Pub/Sub is messaging.

3Which Gemini model variant is optimized for the lowest cost and latency, suitable for high-volume tasks like classification or summarization?

A.Gemini 2.5 Pro

B.Gemini 2.5 Flash

C.Gemini 2.5 Flash-Lite

D.Gemini Ultra

Explanation: Gemini Flash-Lite is the smallest, fastest, lowest-cost variant in the Gemini family — designed for high-volume, latency-sensitive workloads such as classification, summarization, and intent detection. Pro is the highest-quality reasoning model; Flash balances quality and speed. 'Gemini Ultra' was a 1.0-era name and is no longer the current naming.

4Which Vertex AI feature lets you reuse a long static prompt prefix (such as a large system prompt or document context) across many requests to reduce input token cost?

A.Provisioned Throughput

B.Context caching

C.Batch prediction

D.Generation config

Explanation: Vertex AI context caching stores the model's representation of a static prompt prefix and reuses it across requests, dramatically lowering per-request input token cost for workflows like RAG with large fixed contexts or repeated long system prompts. Provisioned Throughput reserves capacity, batch prediction is asynchronous bulk, and generation config tunes sampling.

5Which Gemini API capability lets the model produce structured outputs that conform to a specified JSON schema?

A.System instructions

B.Function calling

C.Controlled generation (responseSchema)

D.Safety settings

Explanation: Controlled generation (responseSchema in generationConfig) constrains Gemini's output to a JSON schema you supply, useful for downstream parsing. Function calling lets the model select and supply arguments to declared tools but doesn't dictate the final JSON shape. System instructions set persona/behavior; safety settings control harm filters.

6Which Vertex AI service provides a vector store that scales to billions of embeddings with low-latency similarity search, often used as the backend for RAG systems?

A.BigQuery

B.Vertex AI Vector Search

C.Cloud Spanner

D.Cloud Bigtable

Explanation: Vertex AI Vector Search (formerly Matching Engine) is a managed approximate-nearest-neighbor service that scales to billions of vectors with millisecond p99 latency. You build an index, deploy it to an index endpoint, and query with embeddings. BigQuery added VECTOR_SEARCH but is OLAP-oriented; Spanner and Bigtable are databases without ANN-as-primary capability.

7Which Vertex AI capability lets a developer evaluate generative model outputs at scale using both computation-based metrics (e.g., ROUGE) and model-based autoraters?

A.Vertex AI Pipelines

B.Vertex AI Gen AI Evaluation Service

C.Vertex AI Model Registry

D.Vertex AI Experiments

Explanation: The Vertex AI Gen AI Evaluation Service supports pointwise and pairwise evaluation, computation metrics (BLEU, ROUGE, exact match) and model-based autoraters with rubric prompts. It accepts your dataset and produces aggregate metrics. Pipelines orchestrate, Model Registry catalogs, Experiments tracks runs.

8A team wants Gemini to call out to a corporate inventory API when a user asks 'How many units of SKU 482 are in stock?'. Which Gemini API feature is appropriate?

A.Function calling

B.Controlled generation

C.Context caching

D.Safety settings

Explanation: Function calling lets you declare available functions (with name, description, and parameters JSON schema). The model decides when to call a function and returns structured arguments; your application executes it and returns results back to the model for the final response. Controlled generation enforces output schema; context caching reuses prompt prefixes; safety settings filter harmful content.

9Which generationConfig parameter controls the randomness of token sampling in Gemini, where 0.0 produces deterministic output and higher values produce more diverse output?

A.topP

B.topK

C.temperature

D.maxOutputTokens

Explanation: Temperature scales the softmax distribution. At 0.0, the highest-probability token is chosen deterministically; higher values flatten the distribution for more diverse output. topP and topK constrain the candidate set (nucleus and top-k filtering). maxOutputTokens caps response length.

10Which Vertex AI capability lets developers ground Gemini responses in real-time public web information without managing their own search infrastructure?

A.Grounding with Google Search

B.Vertex AI Search with private datastore

C.Custom RAG with Cloud Storage

D.BigQuery ML

Explanation: Grounding with Google Search uses Google's web index as a retrieval backend, returning citations alongside responses. It is the easiest way to incorporate fresh public information. Private-datastore grounding is for proprietary documents; custom RAG requires building the pipeline; BigQuery ML serves SQL ML, not LLM grounding.

About the GCP GenAI Engineer Exam

The Google Cloud Professional Cloud GenAI Engineer certification validates the skills to design, build, and operate generative AI solutions on Vertex AI. It covers the Gemini API, Vertex AI Studio, Vertex AI Model Garden (Gemini, Imagen, Veo, Chirp, plus partner models like Anthropic Claude and Mistral), Vertex AI Search, Agent Builder, RAG architectures, embeddings, model tuning, evaluation, MLOps for GenAI, and security/responsible AI on Google Cloud.

Questions

60 scored questions

Time Limit

120 minutes

Passing Score

~70% (scaled, exact threshold not published)

Exam Fee

$200 (Google Cloud / Kryterion Webassessor)

GCP GenAI Engineer Exam Content Outline

~25%

Designing GenAI Solutions on Google Cloud

Choose Gemini variants (Pro, Flash, Flash-Lite), Imagen, Veo, partner models (Claude, Llama, Mistral); design for cost, latency, and quality; pick provisioned vs on-demand vs batch

~25%

Building GenAI Solutions with Vertex AI

Use Vertex AI Studio, Gemini API (multimodal, function calling, structured output, streaming, system instructions, safety settings, generationConfig); build agents with Agent Builder, Reasoning Engine, and tools (function/OpenAPI/RAG/Code Interpreter)

~25%

Grounding, Tuning & Evaluation

Implement RAG with Vertex AI Search, Vector Search, RAG Engine, embeddings; ground with Google Search and private data; perform supervised fine-tuning, RLHF, distillation; evaluate with Gen AI Evaluation Service (pointwise, pairwise, AutoSxS, computation and model-based metrics)

~25%

MLOps, Security, and Responsible AI

Productionize with Vertex AI Pipelines (KFP), Cloud Build, Artifact Registry, Cloud Logging/Monitoring; secure with IAM, VPC Service Controls, CMEK, Workload Identity, Private Service Connect; apply Responsible AI Toolkit, Cloud DLP, citation metadata, hallucination mitigation

How to Pass the GCP GenAI Engineer Exam

What You Need to Know

Passing score: ~70% (scaled, exact threshold not published)
Exam length: 60 questions
Time limit: 120 minutes
Exam fee: $200

Keys to Passing

Complete 500+ practice questions
Score 80%+ consistently before scheduling
Focus on highest-weighted sections
Use our AI tutor for tough concepts

GCP GenAI Engineer Study Tips from Top Performers

1Master the Gemini API: multimodal inputs, function calling, controlled generation, system instructions, safety settings, generationConfig, streaming, and context caching

2Know Vertex AI Studio, Model Garden (Gemini, Imagen, Veo, Chirp, plus Claude, Llama, Mistral), Workbench, Pipelines, and Endpoints inside out

3Build a complete RAG system with Vertex AI Search and Vector Search — understand chunking, embeddings, re-ranking, and grounding

4Practice agent design with Agent Builder, Reasoning Engine, tools (function/OpenAPI/RAG/Code Interpreter), and ReAct/CoT patterns

5Use the Gen AI Evaluation Service for pointwise, pairwise, AutoSxS evaluations with computation and model-based metrics

6Study security: VPC Service Controls, CMEK with Cloud KMS, IAM least privilege, Workload Identity, Private Service Connect

7Cost optimization: Flash vs Pro selection, context caching, Provisioned Throughput, batch prediction, semantic caching

Frequently Asked Questions

What is the Google Cloud Professional Cloud GenAI Engineer exam?

It is a professional-level Google Cloud certification that validates the skills to design, build, and operate generative AI applications on Vertex AI. The exam covers the Gemini API, Vertex AI Studio, Vertex AI Search, Agent Builder, RAG, model tuning, evaluation, MLOps for GenAI, security, and responsible AI.

How many questions are on the GenAI Engineer exam?

The exam contains 50-60 multiple-choice and multi-select questions delivered in 120 minutes. The passing threshold is approximately 70% (Google does not publish exact scaled scores). Results are typically provided immediately at completion.

Are there prerequisites for the GenAI Engineer exam?

No formal prerequisites, but Google recommends 3+ years of industry experience, including 1+ year designing and managing solutions using Google Cloud, with hands-on experience building GenAI applications on Vertex AI. The certification is valid for 2 years.

What does the GenAI Engineer exam cover?

The exam covers four major areas: designing GenAI solutions on Google Cloud (model selection, cost/latency/quality trade-offs); building with Vertex AI (Gemini API, Agent Builder, Reasoning Engine); grounding, tuning, and evaluation (RAG, Vertex AI Search, fine-tuning, Gen AI Evaluation Service); and MLOps, security, and responsible AI (Vertex AI Pipelines, VPC Service Controls, CMEK, hallucination mitigation).

How should I prepare for the GenAI Engineer exam?

Plan 60-100 hours over 6-10 weeks. Use the official Google Cloud Skills Boost GenAI learning path, work through Vertex AI Studio and the Gemini API hands-on, build a real RAG application with Vertex AI Search, and complete 100+ practice questions. Aim for 80%+ on practice tests before scheduling.

What jobs can I get with the GenAI Engineer certification?

GenAI Engineer demonstrates production-ready skills for roles such as Generative AI Engineer, Machine Learning Engineer, AI Solutions Architect, Applied AI Developer, and Cloud Engineer specializing in AI. It pairs well with the Google Cloud Professional Machine Learning Engineer certification.