The Google GCP-GAIL exam preparation guide is designed to provide candidates with necessary information about the GCP-GAIL exam. It includes exam summary, sample questions, practice test, objectives and ways to interpret the exam objectives to enable candidates to assess the types of questions-answers that may be asked during the Google Cloud Platform - Generative AI Leader (GCP-GAIL) exam.
It is recommended for all the candidates to refer the GCP-GAIL objectives and sample questions provided in this preparation guide. The Google GCP-GAIL certification is mainly targeted to the candidates who want to build their career in Foundational domain and demonstrate their expertise. We suggest you to use practice exam listed in this cert guide to get used to with exam environment and identify the knowledge areas where you need more work prior to taking the actual Google Generative AI Leader exam.
Google GCP-GAIL Exam Summary:
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Exam Name
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Google Generative AI Leader (GCP-GAIL) |
| Exam Code | GCP-GAIL |
| Exam Price | $99 USD |
| Duration | 90 minutes |
| Number of Questions | 50-60 |
| Passing Score | Pass / Fail (Approx 70%) |
| Recommended Training / Books |
Generative AI Leader learning path Generative AI Leader Study Guide |
| Schedule Exam | Google CertMetrics |
| Sample Questions | Google GCP-GAIL Sample Questions |
| Recommended Practice | Google Cloud Platform - Generative AI Leader (GCP-GAIL) Practice Test |
Google GCP-GAIL Syllabus:
| Section | Objectives |
|---|---|
Fundamentals of gen AI (~30% of the exam) |
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Describe core generative AI (gen AI) concepts and use cases. Considerations include:
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- Defining core gen Al concepts (e.g., artificial intelligence, natural language processing, machine learning, generative Al, foundation models, multimodal foundation models, diffusion models, prompt tuning, prompt engineering, large language models). - Describing the machine learning approaches (e.g., supervised, unsupervised, reinforcement). - Identifying the stages of the machine learning lifecycle; data ingestion, data preparation, model training, model deployment, and model management; and the Google Cloud tools for each stage. - Identifying how to choose the appropriate foundation model for a business use case (e.g., modality, context window, security, availability and reliability, cost, performance, fine-tuning, and customization). - Identifying business use cases where gen Al can create, summarize, discover, and automate (e.g., text generation, image generation, code generation, video generation, data analysis, and personalized user experience). - Describing how various data types are used in gen Al and the business implications. - Explaining the characteristics and importance of data quality and data accessibility in Al (e.g., completeness, consistency, relevance, availability, cost, format). - Identifying the differences between structured and unstructured data, and identifying real world examples of each type. - Identifying the differences between labeled and unlabeled data. |
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Describe how various data types are used in gen AI and the business implications. Considerations include:
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- Explaining the characteristics and importance of data quality and data accessibility in Al (e.g.. completeness, consistency, relevance, availability, cost, format). - Identifying the differences between structured and unstructured data, and identifying real world examples of each type. - Identifying the differences between labeled and unlabeled data. |
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Identify the core layers of the gen Al landscape and the business implications. Considerations include:
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- Infrastructure
- Models - Plaorms - Agents - Applications |
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Identify the use cases and strengths of Google’s foundation models. Considerations include:
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- Gemini
- Gemma - Imagen - Veo |
Google Cloud’s gen AI oerings (~35% of the exam) |
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Describe Google Cloud's strengths in the field of gen Al. Considerations include:
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- Describing how Google's Al-first approach and commitment to future innovation translate into cutting-edge gen Al solutions. - Describing how Google Cloud has an enterprise-ready Al platform (e.g., responsible, secure, private, reliable, scalable). - Recognizing the advantages of Google's comprehensive Al ecosystem (e.g., integration of gen Al across Google products and services). - Describing the benefits of Google Cloud's open approach. - Identifying the essential components of Google Cloud's Al-optimized infrastructure and its benefits (e.g., hypercomputer, Google's custom-designed TPUs, GPUs, data centers, cloud computing). - Explaining how Google Cloud's Al platform provides users with control over their data (e.g., security, privacy, governance, open and leading first party models, pre-built and customizable solutions, agents). - Describing how Google Cloud's Al platform democratizes Al development (e.g., low-code and no-code tools, pre-trained models, APIs). |
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Describe how Google Cloud's prebuilt gen Al offerings enable Al powered work. Considerations include:
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- Recognizing the functionality, use cases, and business value of the Gemini app and Gemini Advanced (e.g., Gems). - Recognizing the functionality, use cases, and business value of Google Agentspace (e.g., Cloud NotebookLM API, multimodal search, and custom agent capabilities). - Recognizing the functionality, use cases, and business value of Gemini for Google Workspace. |
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Describe how Google Cloud's gen Al offerings improve the customer experience. Considerations include:
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- Recognizing the functionality, use cases, and business benefits of Google Cloud's external search offerings (eg. Vertex Al Search, Google Search). - Recognizing the functionality, use cases, and business value of Google's Customer Engagement Suite (e.g... Conversational Agents, Agent Assist, Conversational Insights, Google Cloud Contact Center as a Service). |
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Describe how Google Cloud empowers developers to build with Al. Considerations include:
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- Recognizing the functionality, use cases, and business value of Vertex Al Platform (e.g.. Model Garden, Vertex Al Search, AutoML).
- Recognizing the functionality, use cases, and business value of Google Cloud's RAG offerings (e.g., prebuilt RAG with Vertex Al Search, RAG APIs).
- Recognizing the functionality, use cases, and business value of using Vertex Al Agent Builder to build custom agents. |
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Define the purpose and types of tooling for gen Al agents. Considerations include:
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- Identifying how agents use tools to interact with the external environment and achieve tasks (eg, extensions, functions, data stores, and plugins). - Identifying relevant Google Cloud services and pre-built Al APIs for agent tooling (e.g.. Cloud Storage, databases, Cloud Functions, Cloud Run, Vertex Al, Speech-to-Text API, Text-to-Speech API, Translation API, Document Translation API, Document Al API, Cloud Vision API, Cloud Video Intelligence API, Natural Language API, Google Cloud API Library). - Determining when to use Vertex Al Studio and Google Al Studio. |
Techniques to improve gen AI model output (~20% of the exam) |
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Describe how to proactively overcome foundation model limitations. Considerations include:
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- Identifying common limitations of foundation models (e.g., data dependency, the knowledge cutoff, bias, fairness, hallucinations, edge cases). - Describing the Google Cloud-recommended practices to address limitations (e.g.. grounding, retrieval-augmented generation [RAG], prompt engineering, fine-tuning, human in the loop [HITL]). - Recognizing Google-recommended practices for continuous monitoring and evaluation of gen Al models (e.g., automatic model upgrades, key performance indicators, security patches and updates, versioning, performance tracking, drift monitoring, Vertex Al Feature Store). |
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Describe prompt engineering techniques and how they drive better results. Considerations include:
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- Defining prompt engineering and describing its significance in interacting with large language models (LLMs). - Identifying prompting techniques and use cases (e.g., zero-shot, one-shot, few-shot, role prompting, prompt chaining). - Identifying advanced prompting techniques and when to use them (e.g.. chain-of-thought prompting, ReAct prompting). |
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Identify grounding techniques and their use cases. Considerations include:
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- Describing the concept of grounding in LLMs and differentiating between grounding with first-party enterprise data, third-party data, and world data.
- Describing how retrieval-augmented generation (RAG) can affect the generated output from your gen Al models. - Google Cloud grounding offerings:
- Identifying how sampling parameters and settings are used to control the behavior of gen Al models (e.g., token count, temperature, top-p [nucleus sampling], safety settings, and output length).
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Business strategies for a successful gen AI solution (~15% of the exam) |
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Describe the Google Cloud recommended steps to successfully implement a transformational gen Al solution. Considerations include:
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- Recognizing the different types of gen Al solutions (e.g., text generation, image generation, code generation, personalized user needs). - Identifying the key factors that influence gen Al needs (e.g., business requirements, technical constraints). - Describing how to choose the right gen Al solution for a specific business need. - Identifying the steps to integrate gen Al into an organization. - Identifying techniques to measure the impact of gen Al initiatives. |
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Define secure Al and its importance in protecting Al systems from malicious attacks and misuse. Considerations include:
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- Explaining security throughout the ML lifecycle. - Identifying the purpose and benefits of Google's Secure Al Framework (SAIF). - Recognizing Google Cloud security tools and their purpose (e.g., secure-by-design infrastructure, Identity and Access Management (IAM), Security Command Center, and workload monitoring tools). |
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Describe the importance of responsible Al in business. Considerations include:
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- Explaining the importance of responsible Al and transparency. - Describing privacy considerations (e.g., privacy risks, data anonymization and pseudonymization). - Describing the implications of data quality, bias, and fairness. - Describing the importance of accountability and explainability in Al systems. |