NCP-AAI Practice Questions

A.  

Using an approach that filters mentions with basic keyword search and removes those with exceptionally low confidence scores, relying on the API data as a first-pass filter.

B.  

Using an approach that treats all mentions as equally reliable, regardless of their confidence scores, and applies a uniform data processing workflow to minimize inconsistency.

C.  

Using a threshold-based approach, accepting mentions only if their confidence score exceeds a predefined level that aligns with typical thresholds used for well-calibrated APIs.

D.  

Using an approach that combines the agent’s text analysis with the API’s confidence score, weighing the agent’s assessment more heavily when identifying mentions.

A.  

Thought -- > Answer -- > Action -- > Observation

B.  

Action -- > Thought -- > Observation -- > Action -- > Thought -- > Observation -- > Answer

C.  

Observation -- > Thought -- > Action -- > Observation -- > Thought -- > Action -- > Answer

D.  

Thought -- > Action -- > Observation -- > Thought -- > Action -- > Observation -- > Answer

A.  

Reset the agent’s long-term memory and reinitialize logs.

B.  

Update the tool function specifications and re-test action sequences.

C.  

Increase model temperature to encourage tool exploration.

D.  

Reduce tool retrieval vector similarity threshold to broaden context.

A.  

Assess performance data from recently improved agents and highlight strong results, using outcome comparisons to identify areas with the greatest impact on service quality.

B.  

Average performance metrics across all agents as this will smooth individual variations, query distribution differences, and temporal factors affecting agent behavior and accuracy.

C.  

Deploy stratified evaluation sampling across agent variants, query complexity levels, and temporal patterns while tracking decision paths using comparative analytics.

D.  

Review performance across both high- and low-accuracy agent groups, comparing case outcomes and identifying patterns contributing to top and bottom results.

A.  

The agent’s ability to predict future demand fluctuations, as accurate forecasting is crucial for effective logistics.

B.  

The total cost savings achieved through the agent’s optimization, which represents a significant financial benefit.

C.  

The percentage of delivery times that fall within the acceptable delay window, considering customer satisfaction as a key factor.

D.  

The agent’s adherence to the prescribed delivery schedules, as it’s demonstrably improving efficiency.

A.  

Set up one mixed GPU node pool with Cluster Autoscaler min=0, scale by network throughput, monitor via metrics-server and logs, and skip readiness probes for fast startup.

B.  

Place GPU pods on on-demand nodes in one zone, disable Cluster Autoscaler, run a fixed pod count for bursts, scale on CPU usage, and monitor with default health checks.

C.  

Deploy GPU pods in a node pool spanning all zones, mix GPU types, enable Cluster and Horizontal Pod Autoscalers using Prometheus GPU and latency metrics, and monitor with NVIDIA DCGM and Grafana.

D.  

Use spot-instance node pools across zones, enable Cluster Autoscaler with capped nodes, scale on memory usage, and monitor with logs and cluster events.