Traditional ML vs Foundation Models: 10 practice questions

Question 1 of 10

Which task is most directly shaped as traditional ML?

1. APredicting one clearly defined numeric demand target
2. BHolding a broad conversational exchange with users
3. CGenerating open-ended marketing copy from instructions
4. DSummarizing many documents in natural language

Question 2 of 10

What does Amazon Bedrock provide for foundation-model applications?

1. AA guarantee that every model meets every regulatory obligation
2. BManaged access to foundation models for generative applications
3. CA replacement for evaluating cost, latency, and explanation needs
4. DA requirement that customers host every foundation model directly

Question 3 of 10

How should regulatory concerns affect model-family selection?

1. AIgnore deployment evidence when the predictive target is narrow
2. BSelect traditional ML without examining its data use or controls
3. CMatch candidate controls and evidence to regulatory obligations
4. DSelect a foundation model before identifying the applicable obligations

Question 4 of 10

Which question directly tests an explainability requirement?

1. ACan the candidate supply audit evidence for permitted data use?
2. BCan the candidate provide the required broad generative capability?
3. CCan the candidate show why its inputs drove this output?
4. DCan it meet the stated latency limits?

Question 5 of 10

Which set contains operational constraints relevant to comparing model families?

1. ALatency, throughput, cost, infrastructure, integration, model size, and skills
2. BFeature influence, stakeholder understanding, and explanation evidence
3. CNumeric prediction shape, predefined classes, and generative task breadth
4. DPermitted data use, required controls, audit evidence, and output risk

Question 6 of 10

What is the sound default when comparing traditional ML with foundation models?

1. APrefer traditional ML before checking generative needs
2. BPrefer the larger model before checking operating limits
3. CPrefer neither; compare both against the full requirement set
4. DPrefer foundation models before defining the target task

Question 7 of 10

When is a foundation model the stronger candidate?

1. AIts latency and cost exceed the application's operating limits
2. BThe candidate lacks evidence required by the applicable regulation
3. CRequired generative capability with every stated constraint satisfied
4. DA fixed numeric target exists and generative output is prohibited

Question 8 of 10

A regulated lender needs one narrow risk prediction and auditable evidence for the complete predictive pipeline. Which choice is best supported?

1. AChoose a foundation model solely because its responses are fluent
2. BChoose either family without checking data use or audit evidence
3. CEvaluate focused traditional ML against the regulatory controls
4. DReject the regulatory controls because the prediction target is narrow

Question 9 of 10

A decision owner must see how specific input features influenced each prediction. What should drive selection?

1. AChoose the candidate with the broadest unrelated generative abilities
2. BRequire the candidate to provide feature-level explanation evidence
3. CChoose the candidate that writes the most confident-sounding response
4. DChoose the candidate before defining what stakeholders must understand

Question 10 of 10

A team needs broad text generation at variable scale and prefers managed API access over hosting a large model. What is the strongest candidate?

1. AA classification model because it returns one predefined label
2. BA regression model because it returns one numeric target
3. CA clustering model because it discovers groups without labels
4. DManaged foundation-model access after constraint checks