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Question # 1
For a production AI platform you must achieve end-to-end observability: correlate a user request across an API,
a queue, a worker, and model/database calls; alert on latency and error-rate SLOs; and control telemetry cost.
Which combination best delivers this?
A. Autoscale provisioned throughput
B. Manual (standard) provisioned throughput sized to the steady load
C. Serverless
D. Strong consistency
Question # 2
You must evaluate whether a new RAG prompt/version improves answer quality before rolling it out to all users,
using production-like traffic and objective metrics such as groundedness and relevance. Which approach is best?
A. Only test with a handful of manual prompts once
B. Deploy the new version to 100% of users immediately and watch for complaints
C. Rely solely on unit tests of the code
D. Run an A/B test routing a portion of traffic to the new version and compare evaluation metrics (groundedness, relevance, latency, cost) before full rollout
Question # 3
An application intermittently fails to read a rotated secret from Key Vault with '403 Forbidden', but only from
certain instances. The identity has the correct role. What should you investigate first?
A. The container image size
B. Key Vault network/firewall settings and private endpoint/VNet configuration for the affected instances, plus RBAC propagation delay
C. The Service Bus lock duration
D. The model deployment quota
Question # 4
A security review requires that no application secrets, connection strings, or keys exist anywhere in code,
pipelines, or app settings across your AI solution. Which end-to-end approach best satisfies this?
Rotate keys weekly but keep them in app settings
Put secrets in a private Git repository
Store all secrets as encrypted environment variables in the image
Use managed identities for service-to-service auth (RBAC), Key Vault for the few unavoidable secrets with Key Vault references, and Entra tokens instead of keys
Question # 5
Costs for your Azure OpenAI-backed feature are rising sharply. Investigation shows long prompts with large
retrieved context and high max_tokens, plus many repeated identical requests. Which combination best reduces
token cost without degrading answer quality?
A. Send the full document corpus in every prompt
B. Trim/re-rank retrieved context to the most relevant chunks, cap max_tokens sensibly, and add a response cache for repeated queries
C. Raise temperature and top_p
D. Disable retrieval entirely
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