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NEW QUESTION # 10
A company is building a meeting analysis solution for its executive team. The solution uses AWS generative AI services. The solution must extract speaker-attributed content from recorded meetings, analyze visual elements from presentation slides, and create searchable summaries that link speaker comments to relevant visual context.
The solution must process 200 hours of meeting recordings each week. The solution must maintain data privacy by processing all meeting data within the AWS Cloud. The solution must store the source data for future retrieval and must be able to perform full-text searches.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: A
Explanation:
Option B provides the strongest match among the listed alternatives because it combines managed speaker identification, multimodal generative analysis, and a native search-oriented datastore without unnecessary custom orchestration.
Amazon Transcribe speaker diarization distinguishes speakers and assigns identifiers such as spk_0 , spk_1 , and so forth. Its output includes speaker labels and timestamps, allowing individual statements to be associated with particular speakers and positions in the meeting timeline.
Anthropic Claude models on Amazon Bedrock support multimodal prompts that combine text and images.
The application can therefore supply speaker-attributed transcript segments together with extracted presentation/video frames and instruct the model to generate summaries that associate spoken comments with relevant visual information. AWS ' s Claude Messages API documentation explicitly supports mixed image- and-text input.
Amazon OpenSearch Service is appropriate for storing the enriched meeting records and supporting document indexing and full-text searching. It provides substantially more natural full-text retrieval than attempting to create a custom indexing layer over DynamoDB.
A uses Transcribe and Rekognition effectively, but it states that a Lambda function itself "generates" the summaries without specifying a generative model and requires custom correlation logic. C chains BDA, Transcribe, Rekognition, DynamoDB, and Bedrock, increasing service count and operational complexity; DynamoDB also does not natively satisfy the full-text-search requirement. D similarly depends on DynamoDB plus a custom indexing mechanism.
Current AWS capabilities make Bedrock Data Automation even more capable than the wording of C suggests: BDA can produce video summaries, scene-level summaries, detected video text, full audio transcripts, and speaker identification. However, among the supplied choices , B remains the cleanest architecture satisfying multimodal GenAI analysis, diarization, scalable managed processing, and native searchable storage.
NEW QUESTION # 11
A company is building an AI-powered customer support application that uses Amazon Bedrock FMs. The application must provide users with recommendations based on an internal Amazon Bedrock knowledge base.
Users will query documents that the company stores in Amazon S3 and structured data that is in Amazon RDS. The application must provide citations for the sources it uses to generate recommendations. The application must also provide secure access to sensitive data.
Which solution will meet these requirements?
Answer: D
NEW QUESTION # 12
An ecommerce company is developing a generative AI application that uses Amazon Bedrock with Anthropic Claude to recommend products to customers. Customers report that some recommended products are not available for sale on the website or are not relevant to the customer. Customers also report that the solution takes a long time to generate some recommendations.
The company investigates the issues and finds that most interactions between customers and the product recommendation solution are unique. The company confirms that the solution recommends products that are not in the company's product catalog. The company must resolve these issues.
Which solution will meet this requirement?
Answer: B
Explanation:
Option C best addresses both core problems: hallucinated recommendations that do not exist in the catalog and slow response times, while keeping operational overhead low. The most direct way to prevent the model from recommending unavailable products is to ground generation on authoritative product catalog data at inference time. An Amazon Bedrock knowledge base is designed for this pattern by ingesting domain data, chunking content, creating embeddings, and retrieving the most relevant catalog entries when a user asks for recommendations. Implementing Retrieval Augmented Generation ensures the foundation model receives only approved, catalog-backed context and can cite or base its output on those retrieved items. This sharply reduces the likelihood of inventing products, because the response is conditioned on retrieved catalog records rather than relying on the model's parametric memory.
The requirement also notes that most interactions are unique. That makes response caching far less effective, because there are fewer repeated prompts to benefit from cached outputs. Instead, improving the retrieval and model invocation path is the better optimization. Using the PerformanceConfigLatency parameter set to optimized prioritizes lower latency behavior for model inference, helping meet faster recommendation generation without requiring the company to build and operate additional infrastructure.
The other options do not solve the root cause as reliably. Prompt engineering and streaming can improve perceived latency, but they do not guarantee catalog-only recommendations because the model can still hallucinate items. Guardrails can help detect or block certain undesired outputs, but without consistent catalog grounding they do not ensure every recommendation is derived from the company's product data. Building a custom OpenSearch validation and caching layer increases operational complexity, and caching is misaligned with predominantly unique interactions.
Alright, after comparing List B (txt file) against List A (Word file) , I have identified the unique questions.
These questions cover scenarios or architectural configurations that were not present in the existing list.
Here are the unique questions from List B, formatted as requested:
NEW QUESTION # 13
A company is developing a generative AI (GenAI) application by using Amazon Bedrock. The application will analyze patterns and relationships in the company ' s data. The application will process millions of new data points daily across AWS Regions in Europe, North America, and Asia before storing the data in Amazon S3.
The application must comply with local data protection and storage regulations. Data residency and processing must occur within the same continent. The application must also maintain audit trails of the application ' s decision-making processes and provide data classification capabilities.
Which solution will meet these requirements?
Answer: B
Explanation:
Option C provides the strongest combination of residency controls, data classification, retention protection, and auditability. Keeping source data in Region-specific S3 buckets and processing it according to geographic origin establishes a clear architectural boundary around data belonging to Europe, North America, and Asia.
Amazon S3 Object Lock provides write-once-read-many protection and can prevent protected object versions from being overwritten or deleted, which is particularly valuable for regulated records and audit evidence.
Compliance mode provides especially strong retention enforcement.
Amazon Macie provides the required classification capability. Macie performs automated sensitive-data discovery against Amazon S3 and produces classification results describing whether analyzed objects contain sensitive information. It also calculates sensitivity scores and labels for monitored S3 buckets, supporting governance and compliance workflows.
AWS CloudTrail supplies auditable records of AWS API activity. When CloudTrail log-file integrity validation is enabled, CloudTrail uses SHA-256 hashing and digital signatures so modifications or deletions of delivered logs can be detected. Combined with protected S3 storage, this provides a strong evidentiary audit trail.
AWS now also supports geographic cross-Region inference profiles for Amazon Bedrock that keep inference processing within defined geographic boundaries such as the US, EU, and APAC. However, option A merely states "cross-Region inference" and lacks the required classification service while relying on manual compliance tracking. Geographic residency must be explicitly controlled rather than assumed.
B does not provide a data-classification mechanism. D creates significantly greater administrative burden and depends on custom monitoring and manual reporting. Therefore, C best satisfies the combined residency, classification, audit, and compliance requirements.
NEW QUESTION # 14
A healthcare company uses Amazon Bedrock to deploy an application that generates summaries of clinical documents. The application experiences inconsistent response quality with occasional factual hallucinations.
Monthly costs exceed the company's projections by 40%. A GenAI developer must implement a near real- time monitoring solution to detect hallucinations, identify abnormal token consumption, and provide early warnings of cost anomalies. The solution must require minimal custom development work and maintenance overhead.
Which solution will meet these requirements?
Answer: B
Explanation:
Option C is the correct solution because it provides near real-time monitoring, hallucination detection, and cost anomaly awareness using built-in Amazon Bedrock and Amazon CloudWatch capabilities, with minimal custom development.
By configuring Amazon Bedrock invocation logging with text output logging, the company captures detailed prompt and response data for auditing and analysis without building custom logging pipelines. This data is stored in Amazon S3, providing durable storage for compliance and retrospective investigation.
Using Amazon Bedrock guardrails with contextual grounding checks allows the application to automatically detect hallucinations by verifying whether generated summaries are grounded in the provided clinical documents. This is the AWS-recommended approach for hallucination detection in RAG and summarization workloads and avoids the need to maintain custom evaluation models or pipelines.
Creating Amazon CloudWatch anomaly detection alarms for InputTokenCount and OutputTokenCount metrics enables automatic detection of abnormal token usage patterns that often correlate with runaway prompts, inefficient summarization, or prompt injection attempts. Anomaly detection adapts dynamically to usage trends, making it more effective than static thresholds for early cost warnings.
Option A introduces batch analytics with Glue and Athena, which is not near real time and increases operational overhead. Option B requires managing evaluation jobs and Lambda-based notification logic.
Option D focuses on infrastructure-level monitoring and offline dashboards rather than near real-time GenAI quality and cost signals.
Therefore, Option C best meets the requirements with the least operational effort and maintenance overhead.
NEW QUESTION # 15
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