Question 1

Your company is deploying AI for automated loan approvals. What ethical concern should be prioritized?

Options :

A : Using AI to eliminate human involvement in loan decisions.

B : Ensuring the AI does not replicate discriminatory lending patterns.

C : Maximizing the speed of loan processing via AI.

D : Sharing model details only with marketing teams.

Answer: B

Question 2

A national health agency wants to use AI to summarize clinical notes but must comply with strict privacy regulations.Question:Why is secure AI critical in this context?

Options :

A : Improve creativity in generated summariesImprove creativity in generated summaries

B : Protect sensitive data and control accessProtect sensitive data and control access

C : Avoid using encryption controls to cut cost

D : Share all system logs with every staff member

Answer: B

Question 3

Tailwind Traders is piloting a generative AI assistant for internal policy questions. Early feedback is that answers are fluent but sometimes off-topic or missing critical rules. You explain that improving the prompts will likely fix much of this without changing the model.To improve answer quality with minimal architecture changes, the most effective next step is to focus prompt engineering on ______.

Options :

A : Adding more GPUs to the cluster

B : Rewriting prompts with clear roles and constraints

C : Migrating the app to a different Azure region

D : Shortening all responses to one sentence

Answer: B

Question 4

A telco is training a churn model. Midway through, you discover that:10% of labels are wrong due to a bug in how “churn” was recordedSome important features have inconsistent units across systemsMany recent records are missing key fieldsWhat is the most accurate description of how this data quality will affect the model?

Options :

A : The model will be largely unaffected because modern algorithms are explicitly designed to ignore label noise and unit inconsistencies during training.

B : Poor labels, inconsistent units, and missing values can distort the patterns the model learns, reducing accuracy and making outputs harder to trust, especially for borderline customers.

C : Only missing values matter; label errors and unit issues are irrelevant because the optimisation process doesn’t look at them directly.

D : Data quality problems affect only training speed, not model behaviour, so your main risk is longer training time rather than worse predictions.

Answer: B

Question 5

An enterprise wants to scale AI usage beyond pilots and has been advised to create an AI champions program. Leadership is debating what the program should look like.Which design best reflects how an effective AI champions program should operate?

Options :

A : Ask IT to nominate a few admins as “champions” whose main task is to approve access requests; do not give them time for coaching or peer engagement so they can stay focused on tickets.

B : Select only senior executives as champions so messages come from the top, and limit their involvement to recording occasional video messages about the importance of AI.

C : Outsource the champions program entirely to the vendor’s customer success team, who will run webinars but have no visibility into the organisation’s culture, policies, or specific use cases.

D : Recruit volunteer champions from across business units, give them training, time, and community support, and empower them to model usage, coach peers, and feed insights back to the central adoption and governance teams.

Answer: D

Confidently Practice Online with Free AB-731 Exam Cram

Buying Options: