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A company has implemented a data ingestion pipeline for sales transactions from its ecommerce website. The company uses Amazon Data Firehose to ingest data into Amazon OpenSearch Service. The buffer interval of the Firehose stream is set for 60 seconds. An OpenSearch linear model generates real-time sales forecasts based on the data and presents the data in an OpenSearch dashboard.
The company needs to optimize the data ingestion pipeline to support sub-second latency for the real-time dashboard.
Which change to the architecture will meet these requirements?
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A
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Explanation
The primary requirement in this scenario is achieving sub-second latency for a real-time analytics dashboard powered by Amazon OpenSearch Service. The current architecture uses Amazon Data Firehose, which buffers incoming records based on time or size before delivering them to the destination. A buffer interval of 60 seconds introduces unavoidable latency, making it unsuitable for near-real-time or sub-second use cases. According to AWS documentation, reducing or eliminating buffering in Firehose is the correct approach when low-latency ingestion is required. Setting the Firehose buffer interval to zero seconds forces Firehose to deliver records as soon as they are received. Additionally, tuning the PutRecordBatch batch size allows efficient ingestion while minimizing delivery delay. This configuration is explicitly recommended for latency-sensitive analytics pipelines. Option B is incorrect because AWS DataSync is designed for batch-oriented data transfers between storage systems, not real-time streaming. Enhanced fan-out consumers are a feature of Amazon Kinesis Data Streams, not DataSync, making this option invalid. Option C directly contradicts the requirement. Increasing the buffer interval from 60 seconds to 120 seconds would further increase latency and degrade real-time performance. Option D is also incorrect because Amazon SQS is a message queueing service, not a streaming ingestion service optimized for indexing data into OpenSearch with minimal latency. Using SQS would add additional processing layers and would not inherently provide sub-second ingestion into OpenSearch. Therefore, using zero buffering in the Firehose stream and tuning the PutRecordBatch batch size is the only change that aligns with AWS best practices for achieving sub-second latency in real-time analytics pipelines. |
A company uses an ML model to recommend videos to users. The model is deployed on Amazon SageMaker AI. The model performed well initially after deployment, but the model's performance has degraded over time.
Which solution can the company use to identify model drift in the future?
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B
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Explanation
AWS recommends Amazon SageMaker Model Monitor for detecting data drift and model drift in deployed models. Model Monitor works by comparing live inference data against a baseline, which must first be created from the training dataset. AWS documentation clearly specifies the required order: Create a baseline using training data statistics Create a monitoring schedule to compare incoming data against the baseline Option A reverses this order and is therefore incorrect. Option C is incorrect because SageMaker Clarify focuses on bias and explainability, not ongoing drift detection. Option D is reactive and does not provide continuous monitoring. Model Monitor integrates with Amazon CloudWatch, enabling automated alerts and downstream retraining workflows. This proactive approach allows companies to detect degradation early and maintain model quality. Therefore, Option B is the correct and AWS-verified answer. |
A company is creating an application that will recommend products for customers to purchase. The application will make API calls to Amazon Q Business. The company must ensure that responses from Amazon Q Business do not include the name of the company's main competitor.
Which solution will meet this requirement?
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A
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Explanation
Amazon Q Business provides built-in guardrails to control and constrain model responses. One such control is the ability to define blocked phrases, which explicitly prevents specified terms from appearing in generated responses. Configuring the competitor’s name as a blocked phrase guarantees that Amazon Q Business will never include that name in responses, fully meeting the requirement. This approach is deterministic and does not rely on ranking or retrieval behavior. Option B is incorrect because retrievers control what documents are fetched, not how responses are generated. Option C adds unnecessary complexity and does not guarantee exclusion in generated answers. Option D only deprioritizes content and does not prevent it from appearing. Therefore, blocking the competitor’s name is the correct solution. |
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Find answers to the most common questions about the Amazon Web Services MLA-C01 exam, including what it is, how to prepare, and how it can boost your career.
The Amazon Web Services MLA-C01 certification is a globally-acknowledged credential that is awarded to candidates who pass this certification exam by obtaining the required passing score. This credential attests and validates the candidates' knowledge and hands-on skills in domains covered in the Amazon Web Services MLA-C01 certification syllabus. The Amazon Web Services MLA-C01 certified professionals with their verified proficiency and expertise are trusted and welcomed by hiring managers all over the world to perform leading roles in organizations. The success in Amazon Web Services MLA-C01 certification exam can be ensured only with a combination of clear knowledge on all exam domains and securing the required practical training. Like any other credential, Amazon Web Services MLA-C01 certification may require periodic renewal to stay current with new innovations in the concerned domains.