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CCA-F Exam Practice Questions

Claude Certified Architect – Foundations

CCA-F English PDF practice questions: 134 questions in source order, with options supported by the source format, answers and available explanations. Access covers this exam only. One-time payment; no automatic renewal.

Exam overview

For solution architects designing and building agent systems and production applications with Claude.

The provider describes exams for Claude Partner Network members. Confirm eligibility, languages and scheduling in Partner Academy.

Exam code
CCA-F
Official exam / credential
Claude Certified Architect – Foundations
Exam format
Proctored, identity-verified certification assessment delivered through Pearson
Official exam languages
A complete language list could not be confirmed from the public official material. Check the provider’s booking page.

Official exam languages and practice-question file languages are listed separately. Confirm the exam version and available languages when booking with the provider.

Official sources: Provider reference 1 · Provider reference 2
Information checked: 2026-10-20

Supported languages

  • English
  • Simplified Chinese
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First 20 practice questions

134 total questions

Questions follow the source order. Expand each answer to see its explanation.

Disclaimer: All questions on ExamPico are compiled from internet sources and are provided for study reference only.

Question 16

Question#16Topic1

You’re implementing the escalation logic for when the agent should call escalate_to_human. Your team proposes four different approaches for triggering escalation. Which approach will most reliably identify cases that genuinely require human intervention?

  1. A. Implement sentiment analysis that monitors for frustration indicators (negative language, repeated questions, exclamation marks) and trigger escalation when the frustration score exceeds a configured threshold.
  2. B. Configure the agent to escalate after three consecutive tool calls that fail to resolve the customer’s stated issue, ensuring a reasonable attempt before involving a human.
  3. C. Instruct the agent to escalate when the customer requests a human, when the issue requires policy exceptions, or when the agent cannot make meaningful progress.
  4. D. Build a rules engine that maps specific issue types, customer segments, and product categories to escalation decisions, removing the need for model judgment calls.
Show answer and explanation

Answer

C

Explanation

Reliable escalation combines explicit triggers: customer requests a human, policy exceptions are required, or the agent cannot make meaningful progress. This covers genuine intervention cases better than sentiment scores or rigid retry counts alone.

Question 17

Question#17Topic1

Your order management system requires tools for three distinct operations: issuing refunds (requires amount and reason), canceling orders (requires reason), and requesting reshipments (requires shipping address). Each operation shares an order_id parameter but has different additional requirements. You notice during testing that with your current unified tool design, the agent frequently omits required parameters or includes irrelevant ones. What design change will most effectively improve parameter accuracy?

  1. A. Keep one unified tool with a nested operation_details object parameter whose internal structure varies by operation type, documented in the tool description.
  2. B. Keep one unified tool but add JSON Schema if-then-else conditionals to enforce that parameters like amount are required only when the operation type is “refund”.
  3. C. Split into three separate tools (issue_refund, cancel_order, request_reshipment), each defining only the parameters required for that specific operation.
  4. D. Keep one unified tool with all parameters marked optional, but add detailed few-shot examples in the system prompt showing correct parameter combinations for each operation type.
Show answer and explanation

Answer

C

Explanation

Separate tools with operation-specific required parameters reduce ambiguity. Splitting into issue_refund, cancel_order, and request_reshipment gives the model a clearer schema for each action and improves parameter accuracy.

Question 18

Question#18Topic1

Your post_content tool requires user confirmation before publishing. The current workflow displays “Ready to post to social media. Confirm?” and analytics show users approve 98% of requests within 2 seconds. Post-mortems reveal incidents where posts went to wrong accounts, were scheduled for wrong times, or contained errors – all confirmed by users without catching the mistakes. How should you redesign the confirmation workflow?

  1. A. Auto-approve routine posts and only require explicit confirmation for unusual patterns like posting to new accounts or large audiences
  2. B. Require users to type a confirmation phrase instead of clicking a button
  3. C. Add a mandatory waiting period before the confirm option becomes available
  4. D. Include the complete post text, target account, scheduled time, and platform in the confirmation request
Show answer and explanation

Answer

D

Explanation

Rubber-stamp confirmations fail because users do not review minimal prompts. Including the complete post text, target account, scheduled time, and platform in the confirmation request forces meaningful review before approval.

Question 19

Question#19Topic1

Your agent uses three tools: get_property_details(property_id) returns data including street address, get_price_history(property_id) returns historical pricing, and get_neighborhood_info(address) returns area statistics. You observe that get_neighborhood_info always requires get_property_details first just to extract the address, even when users specify the property by ID. This creates unnecessary latency and failure coupling – if the first call fails, the neighborhood request also fails. What tool design change best addresses this?

  1. A. Create a lookup_address(property_id) helper tool for retrieving addresses.
  2. B. Add retry logic and timeout handling to get_property_details.
  3. C. Change get_neighborhood_info to accept property_id, resolving the address internally.
  4. D. Consolidate into a single get_property_with_neighborhood(property_id) tool returning both datasets.
Show answer and explanation

Answer

C

Explanation

Tool coupling caused by parameter mismatch is best fixed at the interface. Changing get_neighborhood_info to accept property_id and resolve the address internally removes the unnecessary dependency on get_property_details.

Question 20

Question#20Topic1

Your update_user_profile tool accepts a user_id (required) and an optional fields_to_update object. In testing, Claude frequently omits user_id or passes incorrectly structured data. What is most critical for helping Claude understand what parameter values to provide?

  1. A. Strict JSON Schema type constraints marking user_id as required and defining fields_to_update as an object type
  2. B. Verbose parameter names encoding format hints, such as user_id_string_uuid_format
  3. C. Detailed error responses explaining why invalid parameter values were rejected
  4. D. Clear parameter descriptions explaining expected format, such as “user_id: UUID of the user to update (required)”
Show answer and explanation

Answer

A

Explanation

JSON Schema constraints (required fields, correct types) are the primary mechanism that helps Claude understand which parameters to provide and in what structure during tool invocation.

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