NCP-AAI Exam: Planning and Reasoning Strategies

Table of Contents

• Planning and Reasoning in Agentic AI
• NCP-AAI Exam Coverage
• Key Planning Frameworks
• NVIDIA Planning Tools
• Common Exam Scenarios
• Practice with Preporato

Planning and Reasoning in Agentic AI

Planning enables agents to break down complex goals into executable steps. Reasoning allows agents to evaluate options, handle uncertainty, and adapt to changing conditions.

Core Capabilities Tested on NCP-AAI

• Task decomposition: Breaking complex requests into subtasks
• Multi-step orchestration: Sequencing actions logically
• Conditional branching: Adapting plans based on runtime conditions
• Error recovery: Replanning when tasks fail
• Goal optimization: Finding efficient paths to objectives

NCP-AAI Exam Coverage

Planning Topics (12-15% of Exam)

Exam Format: Scenario-based questions requiring you to select the appropriate planning strategy.

Key Planning Frameworks

1. ReAct (Reasoning + Acting)

The ReAct framework alternates between reasoning and action execution:

Thought: I need to find the cheapest flight
Action: search_flights(from="NYC", to="SF", date="2025-01-15")
Observation: Found 3 flights: $250, $280, $310
Thought: The $250 flight is cheapest
Action: book_flight(flight_id="AA123", price=250)
Observation: Booking confirmed, confirmation=CONF456
Answer: Booked flight AA123 for $250

Exam Key Point: ReAct makes reasoning explicit and traceable (vs. implicit black-box planning).

2. Chain-of-Thought (CoT)

Breaks down reasoning into step-by-step logical chains:

Question: "Calculate 23 × 15"
Chain-of-Thought:
  Step 1: Break down 23 × 15 into (20 + 3) × 15
  Step 2: Calculate 20 × 15 = 300
  Step 3: Calculate 3 × 15 = 45
  Step 4: Add results: 300 + 45 = 345
Answer: 345

Exam Tip: CoT is best for complex reasoning tasks (math, logic puzzles).

3. Tree-of-Thoughts (ToT)

Explores multiple reasoning paths in parallel, backtracking when needed:

Goal: Plan a 3-day vacation

Branch 1: Beach vacation
  ├─ Day 1: Flight + hotel → $500
  ├─ Day 2: Activities → $200
  └─ Day 3: Return → $100
  Total: $800

Branch 2: Mountain vacation
  ├─ Day 1: Drive + cabin → $300
  ├─ Day 2: Hiking (free) → $0
  └─ Day 3: Return → $50
  Total: $350 ✓ (Best option)

Exam Question: "When should you use Tree-of-Thoughts instead of ReAct?" Answer: When multiple solution paths exist and you need to explore/compare them (ToT allows branching and backtracking).

4. Hierarchical Task Networks (HTN)

Decomposes high-level goals into hierarchical subtasks:

Goal: "Prepare dinner"
  ├─ Subtask 1: Choose recipe
  │   ├─ Check ingredients
  │   └─ Select based on availability
  ├─ Subtask 2: Cook meal
  │   ├─ Preheat oven
  │   ├─ Prepare ingredients
  │   └─ Follow recipe steps
  └─ Subtask 3: Serve

Exam Use Case: Complex workflows with clear hierarchical structure (e.g., software deployment, project management).

NVIDIA Planning Tools

1. NeMo Agent Toolkit Planning Module

Provides built-in support for ReAct and multi-step planning:

from nemo_agent import Agent, PlanningStrategy

agent = Agent(
    model="nvidia/llama-3-70b-nemo",
    planning_strategy=PlanningStrategy.REACT,  # Options: REACT, COT, DIRECT
    max_planning_steps=10,
    planning_timeout=30  # seconds
)

Exam Tip: Know the difference between:

• REACT: Explicit reasoning + action loop
• COT: Step-by-step reasoning (no actions)
• DIRECT: No intermediate planning (fastest, least flexible)

2. NVIDIA AIQ Toolkit Agent Graph

Visualizes and executes agent workflows as directed graphs:

agent_graph:
  nodes:
    - id: search
      type: tool_call
      tool: search_database
    - id: analyze
      type: llm_reasoning
      prompt: "Analyze search results"
    - id: respond
      type: tool_call
      tool: send_response
  edges:
    - from: search
      to: analyze
      condition: "search.status == success"
    - from: analyze
      to: respond

Exam Focus: Understand conditional edges (branching logic based on node outputs).

Common Exam Scenarios

Scenario 1: Flight Booking Agent

User Request: "Book the cheapest flight from NYC to SF next week"

Step-by-Step Planning:

1. Determine travel dates: "Next week" → Jan 13-19, 2025
2. Search flights: Call search_flights(from="NYC", to="SF", dates="Jan 13-19")
3. Filter by price: Find minimum price option
4. Confirm availability: Check seat availability
5. Book ticket: Execute booking
6. Send confirmation: Email user

Exam Question: "Which planning framework is most appropriate?" Answer: ReAct (requires reasoning between steps + tool calls).

Scenario 2: Multi-Hop Question Answering

User Request: "What is the capital of the country where the 2024 Olympics were held?"

Planning Steps:

1. Decompose question:
   • Subquestion 1: Where were the 2024 Olympics held? → Paris
   • Subquestion 2: What country is Paris in? → France
   • Subquestion 3: What is the capital of France? → Paris
2. Execute retrieval: Query knowledge base for each subquestion
3. Synthesize answer: "Paris"

Exam Tip: This requires backward chaining (start from goal, work backwards to find required information).

Scenario 3: Error Recovery

Initial Plan:

1. Query database for user preferences → FAILS (database timeout)
2. Generate recommendations → Cannot proceed

Replanning:

1. Query database → Use cached preferences (fallback)
2. Generate recommendations → Proceed with cached data

Exam Question: "What planning feature enables error recovery?" Answer: Conditional branching or fallback strategies (plan B when plan A fails).

Practice with Preporato

Why Practice Tests Matter

The NCP-AAI exam tests your ability to select the right planning strategy for different scenarios. Our Preporato NCP-AAI Practice Tests include:

✅ 50+ planning scenarios with step-by-step explanations ✅ Framework comparisons (ReAct vs. CoT vs. ToT) ✅ NVIDIA tool usage (NeMo Agent Toolkit, AIQ) ✅ Error recovery challenges (replanning, fallback strategies)

Sample Practice Question

Scenario: An agent must solve a Sudoku puzzle. Which planning framework is most appropriate?

A) ReAct (reasoning + action loop) B) Chain-of-Thought (step-by-step reasoning) C) Tree-of-Thoughts (explore multiple solution paths) D) Hierarchical Task Networks (decompose into subtasks)

Correct Answer: C - Sudoku requires exploring multiple cell placements and backtracking when constraints are violated (classic search problem).

Try Free Practice Test →

Key Takeaways for NCP-AAI Exam

1. ReAct is the most tested framework (reasoning + action alternation)
2. Tree-of-Thoughts is for search problems with multiple solution paths
3. NVIDIA NeMo Agent Toolkit supports ReAct, CoT, and direct planning
4. Error recovery requires conditional branching (plan B when plan A fails)
5. Hierarchical planning is for complex workflows with clear task decomposition

Recommended Study Path

1. Week 1: Learn ReAct, CoT, ToT frameworks
2. Week 2: Practice task decomposition exercises
3. Week 3: Study NVIDIA planning tools (NeMo, AIQ)
4. Week 4: Take Preporato practice tests
5. Week 5: Review error recovery and fallback patterns

Additional Resources

• ReAct Paper: "ReAct: Synergizing Reasoning and Acting in Language Models"
• Chain-of-Thought Paper: "Chain-of-Thought Prompting Elicits Reasoning"
• NVIDIA NeMo Agent Toolkit: Official documentation
• Preporato NCP-AAI Bundle: Practice tests + flashcards

Next Steps:

• Error Handling and Recovery in Agentic Workflows →
• Building Production-Ready AI Agents →
• Take NCP-AAI Practice Test

Master planning strategies with Preporato - Your NCP-AAI exam prep platform.