Question 1

A legal tech company uses Amazon Bedrock to extract specific liability clauses from uploaded contracts. Users have reported instances where the model "hallucinates" clauses that do not exist in the source text.Which mechanisms should the developer implement to verify accuracy and reduce these hallucinations?

Options :

A : Define a JSON Schema representing the expected liability data structure and include it in the model instructions to enforce constrained, deterministic output.

B : Implement a post-processing workflow that calculates the semantic similarity score between the generated response and the source text, rejecting responses that fall below a defined confidence threshold.

C : Configure Amazon Bedrock Guardrails with a "Sensitive Information" filter to redact names and addresses found in the contract clauses.

D : Increase the inference temperature parameter to 0.9 and enable Top-P sampling to broaden the diversity of the generated clauses.

E : Use Amazon CloudWatch to monitor the InvocationLatency and ModelTokenCount metrics to identify anomalies in the model's processing speed.

Answer: A,B

Question 2

A news aggregator uses a RAG architecture with Amazon OpenSearch Service (Provisioned) as the vector store. The system ingests breaking news in real-time, performing thousands of vector insertions per minute.Users are reporting that the "Related Articles" search is becoming increasingly slow (high latency) during these ingestion spikes. The OpenSearch cluster metrics show high CPU utilization and high JVM memory pressure.The index is configured with the standard settings for k-NN search.Which index configuration change should the developer apply to improve search performance under heavy ingestion load?

Options :

A : Change the vector data type from float32 to binary to reduce the storage footprint.

B : Switch the index engine from nmslib to faiss and enable the ivf (Inverted File) algorithm.

C : Enable the "Force Merge" operation to run automatically every minute to reduce the number of segments in the index.

D : Increase the refresh_interval setting of the OpenSearch index (e.g., from 1s to 30s or 60s).

Answer: D

Question 3

A healthcare technology company is building a patient inquiry chatbot using Amazon Bedrock. Before logging conversation transcripts for quality assurance, they must ensure that all personally identifiable information (names, phone numbers, addresses, medical record numbers) is detected and redacted to comply with HIPAA privacy requirements.Which combination of AWS services should the developer use to detect and redact PII from conversation logs?

Options :

A : Use AWS CloudTrail to automatically redact PII from all logs

B : Use Amazon Textract to extract PII from documents, then delete the documents

C : Use Amazon Comprehend to detect PII entities, then use Amazon Bedrock Guardrails to redact them from logs

D : Use Amazon Macie to scan conversation logs stored in S3

Answer: C

Question 4

An insurance company has deployed a Generative AI chatbot using Amazon Bedrock to explain policy details to customers. To comply with strict financial regulations, the solution requires a continuous monitoring and automated remediation system for two specific governance issues:Misuse Remediation: If a specific user triggers the "Hate Speech" content filter in Amazon Bedrock Guardrails more than 5 times in a single minute, their access must be automatically and immediately suspended.Bias Monitoring: The system must strictly monitor the model's responses over time to detect if the terminology becomes statistically biased against specific demographics compared to a pre-established "Golden Set" baseline.Which combination of mechanisms satisfies these requirements?

Options :

A : Implement a scheduled AWS Step Functions workflow that uses Amazon SageMaker Clarify to analyze the aggregated inference logs stored in Amazon S3. Configure the job to calculate bias metrics against the baseline and publish findings to CloudWatch.

B : Deploy Amazon SageMaker Model Monitor on the Amazon Bedrock endpoint. Enable the "Real-time Concept Drift" feature to block any inference response that deviates more than 5% from the training data distribution.

C : Configure an Amazon EventBridge Rule to match the aws.bedrock.GuardrailIntervention event pattern. Set the target as an AWS Systems Manager Run Command to disable the user's account in the identity provider.

D : Enable Amazon Macie on the bucket containing the inference logs. configure a custom data identifier for "Biased Terminology" and set up an automated remediation job to delete non-compliant logs.

E : Create an Amazon CloudWatch Alarm that monitors the GuardrailInterventionCount metric with a distinct dimension for the user ID. Configure the alarm action to trigger an AWS Lambda function that revokes the user's IAM permissions or API keys.

Answer: A,E

Question 5

A content moderation system needs to identify specific grammatical patterns in user-generated text, such as detecting imperative sentences (commands) or questions. The team wants to understand the grammatical structure and parts of speech (nouns, verbs, adjectives) in text before making moderation decisions.Which Amazon Comprehend feature should they use?

Options :

A : Sentiment analysis

B : Syntax analysis (part-of-speech tagging)

C : Entity recognition

D : Key phrase extraction

Answer: B

Higher Test Marks with Free Online AIP-C01 Exam Practice

Buying Options: