AWS AIF-C01 Certification Exam Sample Questions

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AWS AIF-C01 Sample Questions:

01. What is the role of data integrity in securing the data used by AI systems?
a) To reduce the overall size of the training dataset
b) To improve the efficiency of data labeling
c) To eliminate all model biases automatically
d) To keep data accurate, consistent and unaltered
 
02. A bank wants to select a generative AI model for a customer-facing chat assistant. Customers expect answers within seconds, and the bank operates under strict financial regulations.
Which factors are MOST relevant to these requirements?
(Choose TWO.)
a) Image generation capability
b) Discounted batch inference pricing
c) Latency requirements
d) Support for reinforcement fine-tuning
e) Compliance constraints
 
03. An insurance company wants one foundation model that accepts a photo of vehicle damage together with the customer's written description and then produces a claim summary.
Which type of model does this use case require?
a) A text-only large language model
b) A regression model trained on past claim amounts
c) A multimodal foundation model
d) An automatic speech recognition model
 
04. A data team is preparing customer records that will be used to train an ML model.
Which practice follows secure data engineering best practices?
a) Copy production data into development accounts so that models train on realistic records
b) Restrict data access to approved roles and assess data quality before training
c) Remove validation steps from the ingestion pipeline so that data arrives faster
d) Keep one unversioned copy of the training data to reduce storage costs
 
05. Why is it important to perform subgroup analysis when evaluating model fairness?
a) To find bias that affects specific demographic groups differently
b) To reduce the overall cost of training the model
c) To simplify how the model is deployed to production
d) To raise overall accuracy by training only on the largest data segments
 
06. How does Amazon Augmented AI (Amazon A2I) support responsible AI?
a) By generating synthetic training data to make datasets more diverse
b) By routing selected ML predictions to human reviewers
c) By encrypting all model outputs to protect data privacy
d) By automatically scaling model architectures to handle more traffic
 
07. A team uses ROUGE and BLEU scores to evaluate summaries and translations produced by a foundation model.
What limitation should the team keep in mind when relying on these automated metrics?
a) They score word overlap, so they can miss context and coherence
b) They are the only reliable way to evaluate a generative model's output
c) They are always more accurate than evaluations by human reviewers
d) They remove all subjectivity, so no human review is ever needed
 
08. Why do open source models support transparency and explainability?
a) They are always more efficient than proprietary models.
b) They guarantee higher accuracy than proprietary models on every task.
c) They remove all data privacy risks for the organizations that use them.
d) Others can inspect and validate how the model was built.
 
09. Auditors ask a company to show which documents and datasets were used to train its model and to produce the answers its AI application returns.
Which practice supports this requirement?
a) Data residency controls
b) Source citation
c) Data retention policies
d) Output toxicity filtering
 
10. Which practice is an essential component of data monitoring in a data governance strategy?
a) Granting all users unrestricted access to the data
b) Reducing data redundancy across storage systems
c) Tracking and logging who accesses or changes the data
d) Deleting historical logs after one month to manage storage

Answers:

Question: 01
Answer: d		 Question: 02
Answer: c, e		 Question: 03
Answer: c		 Question: 04
Answer: b		 Question: 05
Answer: a
Question: 06
Answer: b		 Question: 07
Answer: a		 Question: 08
Answer: d		 Question: 09
Answer: b		 Question: 10
Answer: c

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