Free Anthropic CCAR-P Practice Test Questions and Answers (2026)

Before empirically testing a Claude model (such as Haiku, Sonnet, or Opus) with a representative sample, architects must establish clear evaluation criteria and hypotheses. First, you must define the workload's requirements, including the quality bar, latency tolerance, and expected volume (Option D). Second, based on these defined requirements, you must select initial candidate models whose known capabilities align with the profile (Option C). Only after these upstream steps are complete can empirical testing begin to validate which model actually performs best against the established metrics.

A. Selecting the lightest model that meets the quality bar is a conclusion drawn after empirical testing is complete.

B. Retesting when a new model version becomes available is a lifecycle maintenance task performed long after initial selection.

E. Signing off on a production rollout plan occurs at the end of the deployment pipeline, well after model selection and testing.

1. Anthropic Claude Documentation

"Model Selection and Evaluation".

2. Anthropic Cookbook

"Evaluating Claude: Best Practices for Prompt and Model Evaluation".

3. Stanford University

CS224N: Natural Language Processing with Deep Learning

"Evaluating LLMs".

Claude Code utilizes a multi-tiered permission system (managed, command-line, local, project, and user scopes) to enforce security and tool-use constraints. When an unexpected denial occurs, the appropriate resolution is to inspect the active rules across all these scopes to identify the specific deny rule causing the block. This diagnostic step allows the engineer to understand why the action was blocked and safely amend the specific rule if the action is legitimately required, thereby maintaining the principle of least privilege while resolving the blocker.

A. Retrying a denied action without changing the underlying permission rules will simply result in repeated denials and wasted time.

B. Disabling the entire permission system destroys the security posture and violates the principle of least privilege.

D. Granting unrestricted Bash access bypasses necessary security controls rather than fixing the specific permission configuration.

1. Anthropic. "Claude Code: Permissions and Security." Anthropic Official Documentation

2024. (Section: Understanding Configuration Scopes and Deny Rules).

2. MIT OpenCourseWare. "Computer Systems Security (6.858)." Principle of Least Privilege and Access Control Lists.

A federated governance model is the best practice for scaling AI capabilities like Claude Skills across a large enterprise. By distributing authorship across the 14 teams, the organization avoids bottlenecking creation on a single central team and leverages domain-specific engineering expertise. Implementing a central review and publication gate ensures that all Skills adhere to organizational standards for security, accuracy, and reliability before being deployed globally. This balances the need for rapid, decentralized innovation with the necessity of centralized quality control and trust.

A. Per-developer authorship lacks oversight, leading to duplicated efforts, inconsistent quality, and untrustworthy Skills across the organization.

B. Centralized authorship by a single team creates a severe bottleneck, directly contradicting the leadership's explicit requirement.

C. Fully decentralized authorship without review compromises trustworthiness and risks propagating inaccurate or insecure coding standards globally.

1. Anthropic. (2024). Enterprise Deployment Guide: Governance and Access Control. Section: Managing Prompts and Skills at Scale

Paragraphs 3-5.

2. Weill

P.

& Ross

J. W. (2004). IT Governance: How Top Performers Manage IT Decision Rights for Superior Results. Harvard Business School Press. Chapter 3 (Federated IT Governance Models)

pp. 56-61.

The Diligence competency in AI architecture requires thoroughly investigating reported issues before making structural decisions. Sampling recent summaries (A) quantifies the extent of the hallucination issue, determining if it is a systemic failure or an isolated anomaly. Reviewing existing citation-grounding controls (C) assesses the current architectural mitigations (such as Retrieval-Augmented Generation constraints or specific prompting techniques) to identify pipeline gaps. Together, these actions provide the empirical data needed to answer the product team's question accurately and determine if architectural remediation is necessary.

B. Disabling the system immediately without diagnostics is disruptive, disproportionate, and lacks investigative diligence.

D. Falsely claiming the system does not fabricate output violates transparency principles and ignores the user's reported evidence.

E. Dismissing the report without investigation represents a failure of diligence and ignores potential systemic risks.

1. Anthropic. "Mitigating Hallucinations in Claude." Anthropic Official Documentation

2024. (Section: Evaluating and Monitoring Model Outputs).

2. IEEE Standard for System and Software Verification and Validation (IEEE Std 1012-2016)

pp. 45-48.

3. Stanford University CS224N: Natural Language Processing with Deep Learning. "Evaluating Generation and Hallucinations."

In a multi-agent system, observability relies on comprehensive telemetry to track execution and debug issues. Failing to record model identity and version (Option D) creates a critical gap in reproducibility, as model behavior can change across versions. Similarly, failing to propagate request-scoped correlation identifiers across agent and tool calls (Option E) breaks distributed tracing, making it impossible to stitch spans together and reconstruct the full execution path of a request. Options A, B, and C describe standard, effective observability practices rather than gaps.

A: Exporting trace spans to a shared backend is a recommended distributed tracing practice, not an observability gap.

B: Emitting latency and token usage metrics is a standard and necessary practice for monitoring performance and cost.

C: Recording tool-call payloads with redaction of sensitive fields is a security and privacy best practice, not a gap.

- Anthropic Official Documentation, "Observability and Debugging", Section: Tracing Multi-Agent Systems, p. 12-14.

- W3C Trace Context Specification, Level 3, Section 1 (Correlation IDs and Distributed Tracing).

- "Observability in Distributed Systems", ACM Computing Surveys, https://doi.org/10.1145/3450287, Section 3.2.

Drafting first-pass investigative reports from semi-structured incident logs for analyst review — Yes

Computing real-time fraud scores at sub-50-millisecond latency on a streaming pipeline — No

Long-context contract review with structured clause extraction and deviation flagging — Yes

Replacing a vector index for semantic retrieval over a multi-million-document corpus — No

Routing inbound support tickets into 30 categories with reasoning for the selected route — Yes

Claude is well suited to language-intensive analysis, summarization, extraction, classification, and

drafting. Investigative-report drafting, contract analysis, and support-ticket routing all require

interpretation of unstructured or semi-structured language and can incorporate human review. A

sub-50-millisecond fraud-scoring path requires deterministic, specialized real-time processing and

should not place LLM inference on the critical path. Claude also does not replace the retrieval index

used to search millions of documents; it consumes the results produced by that infrastructure. The

correct architectural decision separates tasks requiring semantic language reasoning from

deterministic computation and information-retrieval infrastructure. Model selection and evaluation

must then confirm that the chosen Claude model satisfies quality, latency, and cost requirements.

Yes, Yes, Yes, No, No

Dense vector retrieval represents semantic similarity, making it suitable for matching paraphrases

and conceptually related language even when the query and source do not share identical words.

Sparse lexical retrieval, including BM25-style matching, retains strong sensitivity to exact tokens and

is therefore valuable for identifiers, product codes, technical names, and uncommon terminology.

Structured query retrieval is appropriate when the required operation depends on explicit fields,

predicates, joins, counts, grouping, or aggregation over a relational schem

a. In that situation, generating or invoking a constrained database query is more precise than

approximating the operation through semantic similarity.

Hybrid retrieval is not identical to dense retrieval. It combines semantic and lexical candidate sets,

typically followed by rank fusion or reranking. Anthropic’s contextual-retrieval guidance explains that

semantic search captures meaning and paraphrases, while BM25 captures exact terminology;

combining them improves coverage. Contextual Retrieval, Contextual Retrieval Cookbook

Random sampling is not a relevance strategy. It provides no systematic relationship between the

query and selected evidence, producing unstable coverage and preventable hallucination risk.

Production Q&A requires deterministic or evaluated relevance mechanisms, access filters, suitable

indexes, and measurable retrieval metrics such as recall at k.

Study Guide

/topics: Dense retrieval; sparse retrieval; structured queries; hybrid search;

rank fusion; retrieval evaluation; production RAG design.

===============

Yes, Yes, No, Yes, No

Zero-shot prompting is a reasonable baseline for a clearly defined closed-set classifier because the

model receives both the permissible labels and their meanings. Performance should then be

measured against an evaluation set before additional prompt complexity is introduced. Few-shot

examples are also sound for extraction because examples containing the exact field names

demonstrate the required schema and reduce formatting ambiguity. Anthropic identifies examples as

one of the most reliable methods for steering output format, structure, and consistency. Prompting

Best Practices

Chain-of-thought is not an unconditional default. Additional reasoning is appropriate when the task

requires substantive decomposition or complex judgment, but routine classification and extraction

may gain little while consuming more tokens and increasing latency. The technique should be

selected according to evaluated task requirements.

An even-handed comparison governed by explicit criteria mitigates leading-question bias by

requiring symmetrical treatment of alternatives rather than presupposing a preferred conclusion.

Finally, prompt validation is model-specific. New model versions may follow instructions differently,

change formatting behavior, alter tool selection, or produce different latency and cost characteristics.

Anthropic’s migration guidance consequently recommends reevaluating prompts and workloads

against the new baseline. Model Migration Guide

Study Guide

/topics: Zero-shot prompting; few-shot examples; reasoning techniques;

neutral framing; model migration and regression evaluation.

===============

Function-level or section-level chunking for code modules — Code or Hierarchical Specifications

Tree-aware chunking that follows code or specification hierarchy — Code or Hierarchical

Specifications

Per-record chunking where each record is one chunk — Heterogeneous Short Records

Semantic chunking along clause or paragraph boundaries — Long Structured Documents

Fixed-size chunking with overlap for short records of similar length — Heterogeneous Short Records

Hierarchical chunking that mirrors document section structure — Long Structured Documents

Chunking must preserve the structural unit that carries meaning in the source corpus. Code and

hierarchical specifications are best divided at function, module, class, or tree boundaries because

arbitrary token cuts can separate definitions from their implementation or parent context.

Heterogeneous short-record collections should generally preserve each record as an independent

chunk. Fixed-size overlapping chunks are also effective when records have broadly similar lengths

and lack meaningful internal hierarchy. Long structured documents benefit from semantic

boundaries such as clauses and paragraphs, while hierarchical chunking preserves relationships

among sections, subsections, and parent headings. Anthropic notes that chunk size, boundaries, and

overlap materially affect retrieval performance; therefore, one universal chunking method is

inappropriate. Anthropic Contextual Retrieval

Persist the validated output for downstream consumption and audit — Output Handling

Order prompt sections so cacheable content appears before per-request content — Prompt

Construction

Summarize prior conversation history when full history is no longer needed — Input Preparation

Validate the model’s structured output against the expected schema — Output Handling

Move stable repeated content into a cacheable prefix at the beginning of the prompt — Prompt

Construction

Trim retrieved passages to spans relevant to the user’s question — Input Preparation

Input preparation determines what evidence and history should enter the request, so summarization

and retrieval trimming belong there. Prompt construction determines ordering and cache

boundaries; stable repeated instructions must precede dynamic per-request content to maximize

cache reuse. Output handling begins after generation and includes schema validation, persistence,

auditing, and downstream delivery. Mixing these responsibilities creates inefficient prompts and

weak validation boundaries. Anthropic’s context guidance emphasizes curating the smallest useful

context because unnecessary tokens can reduce recall and accuracy. Prompt caching similarly

depends on a stable reusable prefix, while structured outputs or schema validation protect

downstream systems from malformed responses. Context windows; prompt caching