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Question 290
An innovation lab wants a general model ready today for summarization tests. Which path avoids unnecessary training cost?
- Custom model training
- Pretrained model reuse
- Model inference API
- No-code AI design interface
Explanation
Correct Answer
B:Pretrained models support immediate experimentation. Pretrained models provide learned capabilities without custom full-model training.
Incorrect Answers
A:Custom training builds or extensively adapts models when simpler existing options do not meet needs. This does not directly meet the case requirement to use learned capabilities without full retraining.
C:An API allows applications to send input to a model programmatically. This does not directly meet the case requirement to use learned capabilities without full retraining.
D:No-code builders let users configure supported models or agents through visual controls. This does not directly meet the case requirement to use learned capabilities without full retraining.
Question 291
A simple extraction workflow uses a very large model whose extra capability does not improve accuracy. Which optimization should be evaluated?
- Financial KPI ownership
- Batch processing
- Capacity and concurrency testing
- Model right-sizing
Explanation
Correct Answer
D:Smaller sufficient models may reduce cost and latency. Right-sizing compares lighter and larger model variants for the needed quality and cost.
Incorrect Answers
A:A financial KPI compares AI system cost and benefit with business outcomes. This does not directly meet the case requirement to use a model proportional to task difficulty.
B:Batch processing handles non-interactive work asynchronously when immediate responses are unnecessary. This does not directly meet the case requirement to use a model proportional to task difficulty.
C:Capacity testing measures how services behave under expected simultaneous requests. This does not directly meet the case requirement to use a model proportional to task difficulty.
Question 292
A cost estimate ignores that prompts contain hundreds of pages each day. Which forecast input is missing?
- Model right-sizing
- Financial KPI ownership
- Token-volume forecasting
- Capacity and concurrency testing
Explanation
Correct Answer
C:Input/output size and call rate drive model consumption. Token forecasting estimates request input/output size and frequency to predict spend.
Incorrect Answers
A:Right-sizing compares lighter and larger model variants for the needed quality and cost. This does not directly meet the case requirement to estimate token usage under realistic traffic.
B:A financial KPI compares AI system cost and benefit with business outcomes. This does not directly meet the case requirement to estimate token usage under realistic traffic.
D:Capacity testing measures how services behave under expected simultaneous requests. This does not directly meet the case requirement to estimate token usage under realistic traffic.
Question 293
An assistant repeats identical summaries of an unchanged product sheet thousands of times. Which optimization can reduce redundant work?
- Batch processing
- Model right-sizing
- Financial KPI ownership
- Caching repeated work
Explanation
Correct Answer
D:Caching can reuse valid outputs when inputs and permissions allow. Caching can avoid recomputation of unchanged content where safe and supported.
Incorrect Answers
A:Batch processing handles non-interactive work asynchronously when immediate responses are unnecessary. This does not directly meet the case requirement to avoid regenerating unchanged material unnecessarily.
B:Right-sizing compares lighter and larger model variants for the needed quality and cost. This does not directly meet the case requirement to avoid regenerating unchanged material unnecessarily.
C:A financial KPI compares AI system cost and benefit with business outcomes. This does not directly meet the case requirement to avoid regenerating unchanged material unnecessarily.
Question 294
A retailer needs to generate product descriptions overnight, with no interactive user waiting. Which execution pattern is suitable?
- Caching repeated work
- Capacity and concurrency testing
- Batch processing
- Token-volume forecasting
Explanation
Correct Answer
C:Asynchronous batches suit offline high-volume jobs. Batch processing handles non-interactive work asynchronously when immediate responses are unnecessary.
Incorrect Answers
A:Caching can avoid recomputation of unchanged content where safe and supported. This does not directly meet the case requirement to process many jobs without synchronous responses.
B:Capacity testing measures how services behave under expected simultaneous requests. This does not directly meet the case requirement to process many jobs without synchronous responses.
D:Token forecasting estimates request input/output size and frequency to predict spend. This does not directly meet the case requirement to process many jobs without synchronous responses.
Question 295
A prototype works for one user but slows sharply under 1,000 concurrent users. Which test should precede rollout?
- Caching repeated work
- Capacity and concurrency testing
- Batch processing
- Token-volume forecasting
Explanation
Correct Answer
B:Load testing reveals throughput and queuing issues. Capacity testing measures how services behave under expected simultaneous requests.
Incorrect Answers
A:Caching can avoid recomputation of unchanged content where safe and supported. This does not directly meet the case requirement to validate concurrency capacity at scale.
C:Batch processing handles non-interactive work asynchronously when immediate responses are unnecessary. This does not directly meet the case requirement to validate concurrency capacity at scale.
D:Token forecasting estimates request input/output size and frequency to predict spend. This does not directly meet the case requirement to validate concurrency capacity at scale.
Question 296
Executives ask whether a model's support labor savings outweigh inference fees. What measure should be established?
- Financial KPI ownership
- Model right-sizing
- Token-volume forecasting
- Capacity and concurrency testing
Explanation
Correct Answer
A:Value assessment connects operational spend to outcome. A financial KPI compares AI system cost and benefit with business outcomes.
Incorrect Answers
B:Right-sizing compares lighter and larger model variants for the needed quality and cost. This does not directly meet the case requirement to compare AI expense against measurable benefits.
C:Token forecasting estimates request input/output size and frequency to predict spend. This does not directly meet the case requirement to compare AI expense against measurable benefits.
D:Capacity testing measures how services behave under expected simultaneous requests. This does not directly meet the case requirement to compare AI expense against measurable benefits.
Question 297
An expense classifier can use a faster smaller model with comparable verified accuracy. Which cost strategy applies?
- Token-volume forecasting
- Batch processing
- Model right-sizing
- Caching repeated work
Explanation
Correct Answer
C:Model size should match verified task complexity. Right-sizing compares lighter and larger model variants for the needed quality and cost.
Incorrect Answers
A:Token forecasting estimates request input/output size and frequency to predict spend. This does not directly meet the case requirement to select adequate not maximum model capacity.
B:Batch processing handles non-interactive work asynchronously when immediate responses are unnecessary. This does not directly meet the case requirement to select adequate not maximum model capacity.
D:Caching can avoid recomputation of unchanged content where safe and supported. This does not directly meet the case requirement to select adequate not maximum model capacity.
Question 298
Finance needs to predict how a longer answer format affects monthly inference bills. Which estimation matters?
- Batch processing
- Token-volume forecasting
- Financial KPI ownership
- Capacity and concurrency testing
Explanation
Correct Answer
B:Output length influences token-based charges. Token forecasting estimates request input/output size and frequency to predict spend.
Incorrect Answers
A:Batch processing handles non-interactive work asynchronously when immediate responses are unnecessary. This does not directly meet the case requirement to forecast spending from generated token counts.
C:A financial KPI compares AI system cost and benefit with business outcomes. This does not directly meet the case requirement to forecast spending from generated token counts.
D:Capacity testing measures how services behave under expected simultaneous requests. This does not directly meet the case requirement to forecast spending from generated token counts.
Question 299
A team has 100,000 files to categorize by tomorrow with no user-facing latency constraint. Which processing style can help?
- Caching repeated work
- Token-volume forecasting
- Batch processing
- Financial KPI ownership
Explanation
Correct Answer
C:Batch execution aligns with asynchronous throughput demands. Batch processing handles non-interactive work asynchronously when immediate responses are unnecessary.
Incorrect Answers
A:Caching can avoid recomputation of unchanged content where safe and supported. This does not directly meet the case requirement to run high-volume offline classification jobs.
B:Token forecasting estimates request input/output size and frequency to predict spend. This does not directly meet the case requirement to run high-volume offline classification jobs.
D:A financial KPI compares AI system cost and benefit with business outcomes. This does not directly meet the case requirement to run high-volume offline classification jobs.
Question 300
A service's 99th-percentile response time violates contract limits during peak hours. Which test is missing?
- Capacity and concurrency testing
- Caching repeated work
- Token-volume forecasting
- Model right-sizing
Explanation
Correct Answer
A:Realistic concurrency testing reveals tail latency under load. Capacity testing measures how services behave under expected simultaneous requests.
Incorrect Answers
B:Caching can avoid recomputation of unchanged content where safe and supported. This does not directly meet the case requirement to stress serving under anticipated peak demand.
C:Token forecasting estimates request input/output size and frequency to predict spend. This does not directly meet the case requirement to stress serving under anticipated peak demand.
D:Right-sizing compares lighter and larger model variants for the needed quality and cost. This does not directly meet the case requirement to stress serving under anticipated peak demand.
Generative AI Leader Practice Tests
