Applications and Integration
33.1%of exam
Model Selection and Optimization
16.8%of exam
Agents and Workflows
14.7%of exam
Prompt and Context Engineering
11.0%of exam
Tools and MCPs
10.6%of exam
Security and Safety
8.1%of exam
Claude Code
3.1%of exam
Eval, Testing, and Debugging
2.6%of exam
Quick Facts
- Exam code
- CCDV-F
- Credential
- Claude Certified Developer Foundations
- Items
- 53 multiple-choice/multiple-response
- Time
- 120 minutes
- Pass
- 720 of 100-1,000
- Fee
- $125 USD
- Domains
- 8 weighted domains
- Delivery
- Pearson VUE proctored
- Validity
- 12 months
- Retake waits
- 14, 30, 90 days
- Attempts
- Four per rolling year
- Blueprint
- v1.0, July 2026
Weight Order
Apps 33, Model 17, Agents 15
Prompt and context 11Tools and MCP 10.6Security 8.1Code 3.1, Eval 2.6
Streaming vs Batch
Streaming
- Tokens arrive live
- Someone is waiting
- Full price
Batch
- Submit and poll
- Nobody waiting
- Half price
Now vs overnight
API Feature Picker
- Bulk job, nobody waiting→Message Batches API(50% cost)
- User watching output→Streaming(SSE deltas)
- Very large max_tokens→Streaming(Avoids timeout)
- Same large prefix repeats→Prompt caching(0.1x reads)
- Response must be JSON→Structured outputs
- Claude must call code→Custom tool(input_schema)
- Same file used repeatedly→Files API
- Need cost estimate first→count_tokens
- History outgrows window→Compaction(Or clearing)
Applications Skill Weights
- Claude Application Design
- 8.6%largest skill
- Software Engineering Foundations
- 7.4%
- Claude API Mechanics
- 6.8%
- Configuration Management
- 4.1%
- Understanding Requirements
- 3.4%
- Systems Life Cycle
- 2.8%
Stream Event Order
Start -> Blocks -> Deltas -> Stop
message_start openscontent_block_delta repeatsmessage_delta has stop_reasonToken counts are cumulative
Messages API Core
- model
- Exact model ID
- max_tokens
- Output ceiling, required
- system
- Top-level standing instructions
- messages
- user and assistant turns
- tools
- Tool schema array
- stream
- Incremental SSE output
- Stateless
- Resend history every call
- usage
- Token counts returned
- request-id
- Header for support tracing
Stop Reasons
- end_turn
- Finished naturally
- max_tokens
- Truncated at your ceiling
- tool_use
- Tool call pending
- stop_sequence
- Custom sequence hit
- pause_turn
- Resumable long turn
- refusal
- Declined; read stop_details
HTTP Error Codes
- 400
- invalid_request_error
- 401
- authentication_error
- 402
- billing_error
- 403
- permission_error
- 404
- not_found_error
- 409
- conflict_error
- 413
- request_too_large
- 429
- rate_limit_errorretry
- 500
- api_errorretry
- 504
- timeout_error
- 529
- overloaded_errorretry
Streaming Events
- message_start
- Empty message shell
- content_block_start
- Block opens
- content_block_delta
- Incremental chunk
- content_block_stop
- Block closes
- message_delta
- stop_reason and usage
- message_stop
- Stream ends
- ping
- Keepalive, safely ignored
- text_delta
- Text fragment
- input_json_delta
- Partial tool arguments
- thinking_delta
- Reasoning fragment
- signature_delta
- Precedes thinking block stop
Message Batches API
- Discount
- 50% of standard prices
- Typical run
- Most finish within 1 hour
- Hard window
- Expires after 24 hours
- Batch cap
- 100,000 requests or 256MB
- custom_id
- Matches result to request
- Ordering
- Results return in any order
- Retention
- Results available 29 days
- processing_status
- in_progress then ended
- Result types
- succeeded errored canceled expired
- max_tokens
- Must be at least 1
Request Size Limits
- Messages API
- 32 MB
- Token Counting API
- 32 MB
- Batch API
- 256 MB
- Files API
- 500 MB
- Over the limit
- 413 request_too_large
Cache Prefix Order
Tools -> System -> Messages
Stable content firstVolatile content lastFour breakpoints maximumAny change invalidates
Prompt Caching vs Batches
Prompt caching
- Repeated prefix
- Realtime latency
- 0.1x cache reads
Batches
- Unique requests
- Async, 24 hours
- 50% price
Reuse prefix vs wait
Model Tier Picker
- Hardest reasoning work→Opus(Capability first)
- Balanced production default→Sonnet
- High volume, simple task→Haiku(Cheapest)
- Latency is the constraint→Haiku
- Cost too high→Cache, then lower effort
- Quality slipped→Raise effort first
- Behavior changed on deploy→Pin the model ID
- Two options look equal→Run an eval
Model and Prompt Weights
- Technical Fundamentals
- 6.1%
- LLM Fundamentals
- 5.2%
- Prompt Engineering
- 4.6%
- Context Engineering
- 3.8%
- Cost and Token Management
- 2.8%
- Model Selection Tradeoffs
- 2.7%
- Output Handling
- 2.6%
Model Tiers
- Opus
- Complex agentic, enterprise tier
- Sonnet
- Balanced speed and intelligence
- Haiku
- Fastest and cheapest tier
- Pinned model ID
- Upgrades become deliberate
- effort
- low medium high xhigh max
- Adaptive thinking
- Model decides reasoning depth
- Fast mode
- Higher throughput, premium price
- Real metric
- Cost per completed task
Prompt Caching
- Prefix order
- tools, system, messages
- Breakpoints
- Four maximum per request
- Default TTL
- 5 minutes
- Extended TTL
- 1 hour option
- Write cost
- 1.25x base input
- Read cost
- 0.1x base input
- cache_read_input_tokens
- Proves a cache hit
- Invalidator
- Any prefix byte change
- Placement
- Stable first, volatile last
Rate Limits
- RPM
- Requests per minute
- ITPM
- Input tokens per minute
- OTPM
- Output tokens per minute
- Algorithm
- Token bucket, continuously replenished
- Cache reads
- Usually excluded from ITPM
- max_tokens
- Does not affect OTPM
- retry-after
- Seconds to wait
- Scope
- Per organization, per model
Token and Cost Levers
- count_tokens
- Size prompt before sending
- Prompt caching
- First free saving
- Batch API
- Half price, asynchronous
- Lower effort
- Fewer reasoning tokens
- Smaller model
- Only if quality holds
- Prune tool output
- Shrinks resent history
- Streaming
- Avoids idle-connection timeouts
- Context window
- Shared by every part
Agent SDK vs Claude Code
Agent SDK
- Library you embed
- Python or TypeScript
- Programmatic agent
Claude Code
- Terminal and IDE
- Interactive session
- Human in loop
Embed vs operate
Agent or Workflow
- One call would work→Single LLM call(Start here)
- Fixed ordered subtasks→Prompt chaining
- Distinct input categories→Routing
- Independent parallel subtasks→Sectioning
- Need higher confidence→Voting
- Subtasks unknown upfront→Orchestrator-workers
- Clear grading criteria→Evaluator-optimizer
- Steps cannot be scripted→Autonomous agent
- Context filling up→Subagent isolation
Agent and Tool Weights
- Agent Construction
- 5.3%
- Agent Patterns
- 4.9%
- Agent Architecture
- 4.5%
- Tool Implementation
- 4.4%
- Agentic Customization
- 4.1%
- MCP Server Development
- 2.1%
Workflow vs Agent
Workflow
- Predefined code paths
- Predictable
- Easy to test
Agent
- Claude directs steps
- Open-ended
- Harder to predict
You route vs Claude routes
Workflow Patterns
- Single call
- Default starting point
- Prompt chaining
- Fixed ordered subtasks
- Routing
- Classify, then specialize
- Parallel sectioning
- Independent subtasks merged
- Parallel voting
- Same task repeated
- Orchestrator-workers
- Runtime subtask decomposition
- Evaluator-optimizer
- Draft and critique loop
- Subagent
- Isolated context window
Agent Loop Anatomy
- Request
- Model plus tool definitions
- stop_reason
- Loop continuation signal
- tool_use
- Execute, then return result
- tool_result
- Sent in user turn
- end_turn
- Loop terminates
- Hook
- Deterministic enforced checkpoint
- Iteration cap
- Stops runaway loops
Claude Agent SDK
- Languages
- Python and TypeScript
- Built-in tools
- Read, write, edit, bash
- Agent loop
- Supplied, not hand-written
- Subagents
- Focused sub-tasks
- Hooks
- Agent lifecycle code points
- Permissions
- Auto-run versus approval
- Sessions
- Resume or fork context
- MCP
- External tool connection
- Hosting
- Runs in your process
System vs User Prompt
System
- Role and standing rules
- Output format
- Stable across turns
User
- Task and data
- Changes every turn
- Carries untrusted input
Durable frame vs task
Prompt Design
- System prompt
- Role and standing rules
- User turn
- Task and its data
- XML-style tags
- Mark section boundaries
- Few-shot
- Three to five examples
- Long documents
- Place near the top
- Output constraints
- State the format explicitly
- Zero-shot
- Instruction, no examples
- Iteration
- Refine against real failures
Clearing vs Compaction
Clearing
- Drops tool results
- Leaves a placeholder
- Cheap and predictable
Compaction
- Summarizes history
- Keeps a trace
- Costs a pass
Delete vs summarize
Context Management
- Context window
- System, tools, history, output
- Clearing
- Drops old tool results
- Compaction
- Summarizes earlier history
- Context drift
- Instructions lost downstream
- Tool output pruning
- Cuts accumulated bloat
- Subagent isolation
- Separate context budget
- Thinking blocks
- Return them unchanged
Structured Output vs Strict
Structured outputs
- Shapes the response
- Schema you supply
- What Claude says
Strict tool use
- Shapes tool arguments
- Per tool definition
- How Claude calls
Response vs arguments
Output Handling
- Structured outputs
- Response matches your schema
- Strict tool use
- Arguments match your schema
- Defensive parsing
- Validate before consuming
- Field checks
- Presence, type, range
- Confident tone
- Not evidence of correctness
- Non-determinism
- Judge across several runs
- Fallback path
- Handle parse failure
Tool Round Trip
Use -> Run -> Result -> Reply
stop_reason is tool_useExecute your handlertool_result in user turnSame tool_use_id
Tool Use vs MCP
Tool use
- Schema in your app
- Serves one application
- You execute calls
MCP server
- Separate running server
- Serves many applications
- Standard protocol
In-app vs shared server
Tool Approach Picker
- Search web or run code→Server tool(No handler)
- Reach your own system→Custom tool
- Reuse across many apps→MCP server
- Adding procedure, not capability→Skill(No server)
- Need file editing→Anthropic-schema tool
- Arguments must validate→strict: true
- Handler threw an error→tool_result is_error
- Several calls at once→One user message
Tool Definition
- name
- Unique tool identifier
- description
- Primary selection signal
- input_schema
- JSON Schema parameters
- strict
- Guarantees schema-valid arguments
- tool_use
- Claude's call block
- tool_result
- Your returned output
- tool_use_id
- Pairs call with result
- is_error
- Flags a handler failure
Skill vs MCP Server
Skill
- Instructions on demand
- Uses existing tools
- No server to run
MCP server
- New callable functions
- Backed by a system
- Process to deploy
Procedure vs capability
MCP Essentials
- Tools
- Executable server actions
- Resources
- Contextual data sources
- Prompts
- Reusable interaction templates
- Elicitation
- Server asks the user
- Protocol
- JSON-RPC 2.0 messages
- stdio
- Local process transport
- Streamable HTTP
- Remote server transport
- Host
- Application running the clients
- Client
- One per connected server
Tool Types
- Custom tool
- You define and execute
- Server tool
- Anthropic runs it
- Anthropic-schema tool
- Bash, text editor, memory
- Skill
- Procedure loaded on demand
- MCP server
- Reusable across applications
- Default choice
- Least code you maintain
Injection Defense
Isolate, restrict, enforce with hooks
Untrusted in own blockMinimum tool scopeHook blocks the actionPolite prompt rules fail
Prompt Rule vs Hook
Prompt rule
- Advisory guidance
- Model may deviate
- No enforcement
Hook
- Runs every time
- Can block the call
- Deterministic control
Guidance vs enforcement
Security Skill Weights
- AI Application Security
- 3.2%
- Claude Code Operation
- 3.1%
- Debugging and Error Handling
- 2.6%
- Guardrails and Safe Deployment
- 2.3%
- Identity, Secrets, Keys
- 1.6%
- Claude Hooks
- 1.0%smallest skill
Security Controls
- Prompt injection
- Untrusted text read as instruction
- Isolation
- Untrusted content, own block
- Least privilege
- Minimum tool scope
- Hooks
- Enforced, not advisory
- PII
- Redact before the request
- Retrieval scoping
- Only this user's data
- Output filtering
- Screen before display
- Guardrail layering
- Defense in depth
Keys and Secrets
- Storage
- Secrets manager, server side
- Client code
- Never ship a key
- Repository
- Never commit a key
- Rotation
- Replace on a schedule
- Expiration
- Set a key lifetime
- Suspected leak
- Disable the key immediately
- Call origin
- From your own backend
Claude Code Config
- Managed policy
- Org-wide, cannot be excluded
- User memory
- ~/.claude/CLAUDE.md
- Project memory
- ./CLAUDE.md, checked in
- Local memory
- CLAUDE.local.md, gitignored
- Load behavior
- Concatenated, never overriding
- Read order
- Broadest scope first
- Imports
- @path, five hops deep
- settings.json
- Behavior, permissions, hooks
Permission Modes
- default
- Prompts on first use
- acceptEdits
- Auto-accepts file edits
- plan
- Explores without editing
- auto
- Classifier-checked auto-approval
- bypassPermissions
- Skips permission prompts
- Rule order
- deny, then ask, allow
- PreToolUse hook
- Blocks before the call
Retry Rules
Retry 429, 500s, 529; never 4xx
Exponential backoffHonor retry-afterSDKs retry twice400 401 403 404 permanent
Debugging Method
- Step one
- Classify the error type
- Integration fault
- Handler or arguments wrong
- Model fault
- Wrong output, correct data
- Trace
- Read the tool sequence
- request-id
- Ties failure to request
- Recovery
- Retry, fallback, or fail
- Reproduce
- Sample runs, not one
Eval and Regression
- Eval set
- Cases with expected behavior
- Quality gate
- Run before shipping changes
- Model swap
- Behavior change, needs eval
- Prompt edit
- Same risk, no compiler
- Exact-match test
- Will flake
- Versioned artifacts
- Prompts, schemas, model ID
- Comparison
- New config versus current
- Monitoring
- Watch quality in production
Common Traps
Workflow vs agent
Workflow follows your code ≠ Agent chooses its steps
Batch vs parallel calls
Batches cut price 50% ≠ Parallel calls pay full price
Custom tool vs MCP
Tool lives in one app ≠ MCP server serves many
Prompt rule vs hook
Prompt rules only advise ≠ Hooks actually block
Result matching
Match batches by custom_id ≠ Never match by position
Cache read vs write
Cache reads cost 0.1x ≠ Cache writes cost 1.25x
Thinking block handling
Return them unchanged ≠ Editing them causes 400
Skill vs MCP server
Skill adds procedure ≠ MCP adds capability
tool_result placement
Goes in a user message ≠ Not the assistant turn
Domain score vs pass
Total scaled score decides ≠ Domain percentages are informational
Structured output vs strict
Structured shapes the response ≠ Strict shapes tool arguments
Bank size vs exam
Real exam has 53 items ≠ Practice banks are larger
Last Minute
- 1.53 items, 120 minutes, 720/1000
- 2.Applications 33.1% is one third
- 3.Model selection 16.8%, agents 14.7%
- 4.Cache order: tools, system, messages
- 5.Batches = 50% cost, 24 hours
- 6.Match batch results by custom_id
- 7.Messages API is stateless; resend history
- 8.tool_result rides in user message
- 9.Same tool_use_id pairs call, result
- 10.Failed handler = tool_result is_error
- 11.Retry 429, 500s, 529; never 4xx
- 12.MCP = tools, resources, prompts
- 13.stdio is local; streamable HTTP remote
- 14.Hooks enforce; prompt rules only advise
- 15.Untrusted content in its own block
- 16.System = rules; user = task
- 17.CLAUDE.md files concatenate, never override
- 18.Pin the model ID in production
- 19.Eval before every prompt change
- 20.Retake waits: 14, 30, 90 days
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