GCP-GAIL: Exam Tips & Strategy

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Final Preparation

This page contains strategies, tips, and common traps to help you succeed on exam day.

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📋 Exam Format Overview

Exam Details:

• Duration: 120 minutes
• Questions: ~50-60 questions
• Format: Multiple choice, multiple select
• Passing Score: ~70% (Pass/Fail)
• Languages: English

Question Types:

1. Single Answer - Choose the ONE best answer
2. Multiple Answer - Choose ALL that apply (usually 2-3 correct)
3. Scenario-Based - Read a scenario about an ML workflow, then answer

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⏱️ Time Management Strategy

Recommended Pacing

• Total Time: 120 minutes
• Questions: ~50-60
• Time per Question: ~2 minutes
• Review Time: 15 minutes at the end

Time Allocation

First Pass (90 min):
├─ Answer all confident questions immediately
├─ Mark uncertain questions for review
└─ Don't spend more than 2.5 minutes on any question

Review Pass (15 min):
├─ Return to marked questions
├─ Verify answers on tricky scenarios
└─ Trust your first instinct unless clearly wrong

Buffer (15 min):
└─ Final check before submission

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🎯 Domain-Specific Tips

Domain 1: Vertex AI Foundation (~30%)

High-Frequency Topics:

• ⭐⭐⭐ Gemini model family differences (Ultra vs Pro vs Flash vs Nano)
• ⭐⭐⭐ Model Garden navigation and model selection
• ⭐⭐ Vertex AI Studio capabilities

Common Traps:

• Confusing Gemini Pro (general purpose) with Gemini Flash (speed optimized)
• Not knowing that Gemini Nano is for on-device/mobile only
• Forgetting Model Garden includes open-source models (Llama, Mistral)

Decision Matrix:

Which Gemini model to use?
├─ Complex reasoning, code generation? → Gemini Ultra
├─ General tasks, good balance? → Gemini Pro
├─ High-volume, low-latency? → Gemini Flash
└─ On-device (mobile/edge)? → Gemini Nano

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Domain 2: Prompt Engineering (~25%)

High-Frequency Topics:

• ⭐⭐⭐ Temperature, Top-K, Top-P parameters
• ⭐⭐⭐ Few-shot vs Zero-shot prompting
• ⭐⭐ Chain of Thought (CoT) prompting

Common Traps:

• Temperature 0 = deterministic, Temperature 1 = creative
• Top-K limits vocabulary size, Top-P limits by probability
• Few-shot requires examples; Zero-shot doesn't

Quick Reference:

Parameter	Low Value	High Value
Temperature	Factual, consistent	Creative, varied
Top-K	Conservative vocabulary	Diverse vocabulary
Top-P	Focused responses	Exploratory responses

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Domain 3: Data & Customization (~25%)

High-Frequency Topics:

• ⭐⭐⭐ RAG vs Fine-Tuning decision
• ⭐⭐⭐ Vertex AI Vector Search (formerly Matching Engine)
• ⭐⭐ Embeddings API usage

Common Traps:

• RAG = real-time data access; Fine-tuning = behavioral change
• Grounding with Google Search ≠ RAG with your own data
• Distillation creates smaller models from larger ones

Decision Matrix:

How to customize model behavior?
├─ Need real-time/changing data? → RAG + Vector Search
├─ Need specific output format? → Few-shot prompting
├─ Need domain-specific behavior? → Supervised Fine-Tuning
├─ Need smaller, faster model? → Distillation
└─ Need factual grounding? → Grounding with Google Search

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Domain 4: Responsible AI & Operations (~20%)

High-Frequency Topics:

• ⭐⭐⭐ Safety Filters and thresholds
• ⭐⭐ AutoSxS (Side-by-Side evaluation)
• ⭐⭐ Model monitoring and drift detection

Common Traps:

• If model refuses valid queries → Safety filters too strict
• Google does NOT train foundation models on customer data
• AutoSxS uses a judge model to compare two model outputs

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🚫 Common Exam Traps

Trap 1: RAG vs Fine-Tuning Confusion

Scenario: "Company needs model to answer questions about their internal documents that update weekly"

• ❌ Fine-tuning (data is changing)
• ✅ RAG with Vector Search (real-time data access)

Rule: If data changes frequently → RAG. If behavior needs to change → Fine-tuning.

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Trap 2: Grounding vs RAG

Grounding with Google Search:

• Uses public web data
• Good for factual accuracy on general topics

RAG with Vector Search:

• Uses YOUR private data
• Good for enterprise/proprietary information

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Trap 3: Model Selection

Question Pattern: "Which model for [specific use case]?"

Use Case	Best Model
Complex code generation	Gemini Ultra
General chatbot	Gemini Pro
High-throughput API	Gemini Flash
Mobile app	Gemini Nano
Image generation	Imagen
Speech-to-text	Chirp

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Trap 4: Parameter Tuning

"Model outputs are too random/unpredictable"

• ✅ Lower the temperature
• ✅ Lower Top-K and Top-P

"Model outputs are too repetitive/boring"

• ✅ Raise the temperature
• ✅ Raise Top-K and Top-P

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📊 Must-Know Comparisons

Customization Techniques

Technique	When to Use	Data Needed
Prompt Design	Quick iteration, no training	None
Few-shot	Need specific format	3-5 examples
Fine-tuning (SFT)	Domain-specific behavior	100+ examples (JSONL)
Distillation	Need smaller/faster model	Teacher model outputs
RAG	Access private/changing data	Vector database

Vertex AI Services

Service	Purpose
Model Garden	Discover and deploy models
Vertex AI Studio	Interactive prompt testing
Vector Search	High-scale embedding search
Embeddings API	Convert text to vectors
AutoSxS	Compare model outputs

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🔑 Key Facts to Remember

Important Limits

• Gemini 1.5 Pro context window: Up to 1M+ tokens
• Fine-tuning dataset format: JSONL in Cloud Storage
• Minimum fine-tuning examples: ~100 high-quality pairs

Critical Acronyms

• RAG: Retrieval-Augmented Generation
• CoT: Chain of Thought
• SFT: Supervised Fine-Tuning
• RLHF: Reinforcement Learning from Human Feedback

Data Privacy

• Google does NOT train foundation models on customer data
• Customer data in Vertex AI remains private
• Enterprise security controls available

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💡 Last-Minute Review Checklist

Before Exam

• [ ] Know the Gemini model family differences
• [ ] Understand RAG vs Fine-tuning decision tree
• [ ] Remember Temperature/Top-K/Top-P effects
• [ ] Know when to use Grounding vs RAG
• [ ] Review safety filter purposes

During Exam

• [ ] Read for keywords: "real-time", "changing data", "private data"
• [ ] RAG keywords: "documents", "knowledge base", "current information"
• [ ] Fine-tuning keywords: "specific behavior", "output format", "tone"
• [ ] Check if question asks for "MOST" efficient/cost-effective

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You've Got This!

Remember the Vertex AI workflow: Discover (Model Garden) → Experiment (Studio) → Customize (Tuning/RAG) → Deploy (Endpoints). Good luck! 🍀

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Last Updated: 2026-01-17