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Claude-Architect Practice Questions

Claude Certified Architect Foundations

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Question 1

Your schema includes a `skills: string[]` field. Production monitoring reveals three consistency issues: (1) compound phrases like "Python and SQL" are sometimes kept as one entry, sometimes split; (2) implied but unstated skills occasionally appear in extractions; (3) similar documents produce wildly different array lengths (5-10 vs 40+ entries). Your prompt currently says "Extract all skills mentioned."

What's the most effective improvement?

Add few-shot examples demonstrating compound phrase handling, explicit mention criteria, and appropriate entry granularity.
Add constraints: "Extract 10-20 skills maximum, one skill per entry, only explicitly named skills."
Enrich the schema to `{skill: string, confidence: float, source_quote: string}[]` to capture extraction metadata.
Add post-extraction normalization that maps skills to a canonical taxonomy and deduplicates similar entries.

Select an answer above, then submit your vote. Reading or practicing does not cast a vote.

Question 2

Your extraction system exposes three tools: `extract_fields`, whose schema-validated JSON output is written to your database, plus `lookup_vendor` and `flag_for_review`, which support operator questions and edge cases. Every submitted document must produce an `extract_fields` record.

In testing, when an operator submits a document with a request like "extract this invoice and check whether the vendor already exists," Claude sometimes calls only `lookup_vendor` and never calls `extract_fields`, so no record is written.

Which approach guarantees an `extract_fields` call for every submitted document while still allowing Claude to handle the rest of the operator's request?

Set `tool_choice` to `{"type": "tool", "name": "extract_fields"}` on the first request after submission, then return `tool_choice` to "auto" on subsequent turns so Claude can address the rest of the operator's request.
Set `tool_choice` to "any" so Claude must use a tool, combined with system prompt instructions prioritizing `extract_fields`.
Set `tool_choice` to `"auto"` and reorder the tool definitions so `extract_fields` appears first in the tools array, since Claude prioritizes earlier-listed tools.
Set `tool_choice` to `{"type": "tool", "name": "extract_fields"}` for every API call in the pipeline, ensuring Claude always extracts fields before addressing the rest of the operator's request.

Select an answer above, then submit your vote. Reading or practicing does not cast a vote.

Question 3

Your extraction pipeline validates outputs against JSON schemas, but you need to implement human review given limited reviewer capacity (they can handle approximately 5% of total extraction volume).

What's the most effective basis for selecting which extractions to route for human review?

Route extractions containing specific high-priority entity types (e.g., financial figures, dates) for human review, regardless of extraction confidence.
Randomly sample 5% of extractions for review.
Route extractions where the model indicates low confidence or where source documents contain ambiguous or contradictory information.
Route extractions for review only when downstream systems report data quality issues or processing failures.

Select an answer above, then submit your vote. Reading or practicing does not cast a vote.

Question 4

Monitoring shows 12% of extractions fail Pydantic validation with specific errors like "expected float for quantity, got '2 to 3'". Retrying these requests without modification produces identical failures.

What's the most effective approach to recover from these validation failures?

Pre-process source documents to standardize problematic formats before sending them for extraction
Implement a secondary pipeline using a larger model tier to reprocess documents that fail validation
Set temperature to 0 to eliminate output variability and ensure consistent formatting
Send a follow-up request including the validation error, asking the model to correct its output

Select an answer above, then submit your vote. Reading or practicing does not cast a vote.

Question 5

Your extraction system implements automatic retries when validation fails. On each retry, the specific validation error is appended to the prompt. This retry-with-error-feedback approach resolves most failures within 2-3 attempts.

For which failure pattern would additional retries be LEAST effective?

The model extracts keywords as a nested object organized by category when the schema requires a flat array of strings
The model extracts "et al." for co-authors when the full list exists only in an external document not in the input
The model extracts dates as ISO 8601 datetime strings ("2023-03-15T00:00:00Z") when the schema requires only the date portion (YYYY-MM-DD)
The model extracts citation counts as locale-formatted strings ("1,234") when the schema requires integers

Select an answer above, then submit your vote. Reading or practicing does not cast a vote.

Question 6

Your system extracts event metadata (date, location, organizer, attendee_count) from news articles using a JSON schema with all nullable fields. During evaluation, you observe the model frequently generates plausible but incorrect values for fields not mentioned in the article—for example, outputting "500" for attendee_count when the source contains no attendance information.

What's the most effective way to reduce these false extractions?

Make all schema fields required (non-nullable) with strict validation rules to ensure the model only outputs verifiable data.
Add a post-processing step using a second LLM call to verify each extracted value exists in the source document.
Add prompt instructions to return null for any field where information is not directly stated in the source.
Upgrade to a more capable model tier with improved instruction-following to reduce hallucination tendencies.

Select an answer above, then submit your vote. Reading or practicing does not cast a vote.

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