Analyze Requirements for AI-powered Business Solutions AB-100 Practice Questions (Page 8)

Questions 36–40

1. 36application · medium

   A marketing agency wants to deploy an agent that generates weekly performance reports for clients. The agent will pull data from multiple advertising platforms via APIs. The team is concerned about API rate limits and occasional downtime. What should the team do to ensure the agent can generate reports reliably?

   • AReduce the number of data sources to minimize API calls.
   • BImplement a data caching layer that stores API responses and falls back to cached data during outages.
   • CUse a single API that aggregates data from all platforms.
   • DSchedule the agent to run only when all APIs are confirmed to be online.
   Reveal Solution Discussion Select an answer first
2. 37application · medium

   A financial services firm is developing an agent to assist loan officers with credit approval decisions. The agent will analyze applicant data and recommend approval or denial. The firm's historical loan data contains records from a period when the approval policy was significantly different. The team wants to use this data to train the agent. What should the team do to ensure the agent's recommendations align with current business rules?

   • AUse only the most recent month of data to ensure timeliness.
   • BInclude all historical records to maximize the training dataset size.
   • CFilter out historical records that do not reflect the current approval policy.
   • DAdjust the agent's confidence threshold to reduce false approvals.
   Reveal Solution Discussion Select an answer first
4. 38foundation · easy

   How can an AI agent enhance data analytics capabilities for business insights?

   • ABy automatically identifying patterns and trends in large datasets
   • BBy replacing the need for any human data analysts
   • CBy manually creating charts and graphs for presentations
   • DBy storing data in a more secure database
   Reveal Solution Discussion Select an answer first
5. 39foundation · easy

   Why is data cleanliness important for maintaining the integrity of AI-powered solutions?

   • AClean data is always collected in real time
   • BClean data is always stored in a data warehouse
   • CDirty data can cause AI models to produce inaccurate or biased results
   • DClean data is always more secure than dirty data
   Reveal Solution Discussion Select an answer first
6. 40expert · hard

   A manufacturing company wants to deploy an agent that predicts equipment failures and schedules maintenance. The agent will use sensor data from machines and maintenance logs. The sensor data is accurate, but the maintenance logs contain manual entries with inconsistent codes and some missing records. What should the team do to ensure the agent's predictions are reliable?

   • AIncrease the frequency of sensor data collection to compensate for log issues.
   • BClean and standardize the maintenance logs, and cross-reference them with sensor data to fill gaps.
   • CUse only the sensor data and ignore the maintenance logs.
   • DUse the maintenance logs as-is because they contain the ground truth.
   Reveal Solution Discussion Select an answer first