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It has a table with one row per customer and columns for customer_id, account_age_months, average_monthly_data_usage, and average_monthly_call_minutes. No customer groups have been defined or assigned. Which approach best matches the available data and the company's goal?","Consider whether a consistent table schema or a unique record identifier provides the outcome that a model is supposed to learn.","single",[179,184,188,192],{"id":180,"questionId":174,"content":181,"hint":182,"isCorrect":183,"position":152,"matchAnswerId":152},212451,"Treat average_monthly_data_usage as an existing target label and train a regression model using the remaining columns to produce the customer groups.","Monthly data usage could be a regression target for a different task, but predicting that value does not discover customer groups based on similarities across the available attributes.",false,{"id":185,"questionId":174,"content":186,"hint":187,"isCorrect":183,"position":152,"matchAnswerId":152},212452,"Treat the table as unstructured, unlabeled data and convert each row into a text document before using a text-clustering model.","The attributes already follow an explicit schema and are structured data. Converting these numerical attributes to prose is unnecessary and does not correctly characterize the available data.",{"id":189,"questionId":174,"content":190,"hint":191,"isCorrect":9,"position":152,"matchAnswerId":152},212453,"Treat the table as structured, unlabeled data and use the account-age and usage columns as inputs to a clustering model.","The consistent rows and columns make this structured, tabular data. It is unlabeled for customer grouping because no group assignments exist. Clustering in SageMaker AI can discover similarities without predefined group labels.",{"id":193,"questionId":174,"content":194,"hint":195,"isCorrect":183,"position":152,"matchAnswerId":152},212454,"Treat the table as labeled data, use customer_id as the target label, and train a supervised model to predict each customer's identifier.","A customer identifier distinguishes records but does not describe a meaningful customer group. Predicting identifiers would not discover groups with similar usage patterns.",{"id":197,"title":198,"subtitle":152,"description":152,"questions":199},317,"Identify practical use cases for AI",[],{"id":201,"title":202,"subtitle":152,"description":152,"questions":203},318,"Describe the AI\u002FML development lifecycle",[],{"id":205,"title":206,"subtitle":152,"description":152,"content":152,"coverImageUrl":152,"weight":207,"subcategories":208},84,"Fundamentals of GenAI",24,[209,234,238],{"id":210,"title":211,"subtitle":152,"description":152,"questions":212},319,"Explain the basic concepts of generative AI (GenAI)",[213],{"id":214,"subcategoryId":210,"content":215,"hint":216,"type":177,"isPublic":9,"answers":217},47563,"A logistics company uses an Amazon Bedrock foundation model to assign shipment exception reports to company-specific routing categories. Providing expert-labeled examples in each prompt improves accuracy, but the examples make requests too long. Sending those examples once does not improve later, independent requests. The company has thousands of reviewed report-and-category pairs, and its selected model supports customization. It wants a reusable adaptation that works without including labeled examples in every request. Which approach best meets these requirements?","Distinguish information available only within a request from behavior learned through a separate training process.",[218,222,226,230],{"id":219,"questionId":214,"content":220,"hint":221,"isCorrect":183,"position":152,"matchAnswerId":152},213630,"Send the reviewed examples to the original model as ordinary inference requests before each day's classification workload.","Ordinary inference requests do not update the model's learned parameters. Examples sent in separate requests do not become persistent learned behavior for later, independent requests.",{"id":223,"questionId":214,"content":224,"hint":225,"isCorrect":183,"position":152,"matchAnswerId":152},213631,"Continue pre-training the model on unlabeled shipment reports without including the approved routing categories.","Continued pre-training can improve familiarity with domain language, but it does not directly teach the required mapping between reports and approved routing categories. The reviewed input-output pairs are better suited to task-specific training.",{"id":227,"questionId":214,"content":228,"hint":229,"isCorrect":183,"position":152,"matchAnswerId":152},213632,"Reduce the original model's temperature and remove the labeled examples from classification prompts.","A lower temperature can reduce response variability, but it does not teach the company-specific category mappings demonstrated by the labeled examples.",{"id":231,"questionId":214,"content":232,"hint":233,"isCorrect":9,"position":152,"matchAnswerId":152},213633,"Fine-tune the model using the reviewed report-and-category pairs, then use the customized model for classification.","Fine-tuning uses targeted examples to update learned parameters or adaptation parameters. It can teach the report-to-category mapping as reusable behavior without requiring the training examples in every inference request.",{"id":235,"title":236,"subtitle":152,"description":152,"questions":237},320,"Understand the capabilities and limitations of GenAI for solving business problems",[],{"id":239,"title":240,"subtitle":152,"description":152,"questions":241},321,"Describe AWS infrastructure and technologies for building GenAI applications",[],{"id":243,"title":244,"subtitle":152,"description":152,"content":152,"coverImageUrl":152,"weight":245,"subcategories":246},85,"Applications of Foundation Models",28,[247,272,276,280],{"id":248,"title":249,"subtitle":152,"description":152,"questions":250},322,"Describe design considerations for applications that use foundation models (FMs)",[251],{"id":252,"subcategoryId":248,"content":253,"hint":254,"type":177,"isPublic":9,"answers":255},47363,"A consulting firm uses a retrieval-augmented generation (RAG) assistant to answer questions about market research. Document embeddings are stored in an Amazon OpenSearch Service vector index. The firm switches to a different embedding model for user questions but leaves the existing document embeddings unchanged. Although both embedding models produce vectors of the same length, the assistant now retrieves unrelated passages. Which change should the firm make to restore meaningful retrieval?","Consider whether vectors from different embedding models represent meaning in the same way, even when they contain the same number of values.",[256,260,264,268],{"id":257,"questionId":252,"content":258,"hint":259,"isCorrect":183,"position":152,"matchAnswerId":152},212695,"Increase the number of passages retrieved from the existing OpenSearch index without changing the document embeddings.","Retrieving more passages does not resolve the incompatibility between the document and query embeddings. It can instead supply more irrelevant context to the generation model.",{"id":261,"questionId":252,"content":262,"hint":263,"isCorrect":183,"position":152,"matchAnswerId":152},212696,"Fine-tune the answer-generation model on the research documents while leaving the retrieval configuration unchanged.","Fine-tuning the generation model does not repair the embedding mismatch in the retrieval layer. The OpenSearch index would still return results based on incompatible vector representations.",{"id":265,"questionId":252,"content":266,"hint":267,"isCorrect":183,"position":152,"matchAnswerId":152},212697,"Increase the generation model's context window so it can interpret the passages retrieved by the existing index.","A larger context window allows more input text but does not improve which passages the search layer retrieves. The embedding incompatibility must be addressed before generation.",{"id":269,"questionId":252,"content":270,"hint":271,"isCorrect":9,"position":152,"matchAnswerId":152},212698,"Regenerate the document embeddings with the new embedding model, rebuild the OpenSearch vector index, and use that model for query embeddings.","Document and query embeddings must use compatible representations for meaningful vector similarity searches. Regenerating the document embeddings with the new model aligns them with query embeddings. Equal vector lengths alone do not establish compatibility.",{"id":273,"title":274,"subtitle":152,"description":152,"questions":275},323,"Choose effective prompt engineering techniques",[],{"id":277,"title":278,"subtitle":152,"description":152,"questions":279},324,"Describe the training and fine-tuning process for FMs",[],{"id":281,"title":282,"subtitle":152,"description":152,"questions":283},325,"Describe methods to evaluate FM performance",[],{"id":285,"title":286,"subtitle":152,"description":152,"content":152,"coverImageUrl":152,"weight":287,"subcategories":288},86,"Guidelines for Responsible AI",14,[289,314],{"id":290,"title":291,"subtitle":152,"description":152,"questions":292},326,"Explain the development of AI systems that are responsible",[293],{"id":294,"subcategoryId":290,"content":295,"hint":296,"type":177,"isPublic":9,"answers":297},47079,"A healthcare provider is evaluating an AI model that recommends patients for additional screening. The model has high overall accuracy, but the prevalence of the condition differs across demographic groups. Clinical leaders require that patients who actually have the condition have comparable chances of being referred, regardless of demographic group. Which evaluation approach most directly determines whether the model meets this requirement?","Consider which patients should form the comparison population, rather than comparing referral totals across all patients.",[298,302,306,310],{"id":299,"questionId":294,"content":300,"hint":301,"isCorrect":9,"position":152,"matchAnswerId":152},211343,"Among patients with independently confirmed cases, compare referral rates across demographic groups and investigate any material gaps.","This compares true positive rates across groups, directly testing whether patients who have the condition have comparable chances of referral. Investigating material gaps supports addressing harmful disparities before deployment.",{"id":303,"questionId":294,"content":304,"hint":305,"isCorrect":183,"position":152,"matchAnswerId":152},211344,"Exclude demographic attributes from the model inputs and verify that inference requests contain none of those attributes.","Excluding demographic attributes does not establish equitable outcomes because other inputs can act as proxies. The provider must evaluate actual referral outcomes across groups to assess the stated requirement.",{"id":307,"questionId":294,"content":308,"hint":309,"isCorrect":183,"position":152,"matchAnswerId":152},211345,"Compare the percentage of all patients referred in each demographic group and require those percentages to be equal.","Equal overall referral rates do not establish comparable referral chances for patients who actually have the condition. Different condition prevalence across groups can make equal referral totals inappropriate for the stated requirement.",{"id":311,"questionId":294,"content":312,"hint":313,"isCorrect":183,"position":152,"matchAnswerId":152},211346,"Measure overall accuracy on a larger representative test dataset and approve the model if accuracy exceeds the clinical target.","Overall accuracy can conceal differences in missed cases across demographic groups. A larger dataset improves the evidence but does not replace the required group-specific evaluation.",{"id":315,"title":316,"subtitle":152,"description":152,"questions":317},327,"Recognize the importance of transparent and explainable models",[],{"id":319,"title":320,"subtitle":152,"description":152,"content":152,"coverImageUrl":152,"weight":287,"subcategories":321},87,"Security, Compliance, and Governance for AI Solutions",[322,326],{"id":323,"title":324,"subtitle":152,"description":152,"questions":325},328,"Explain methods to secure AI systems",[],{"id":327,"title":328,"subtitle":152,"description":152,"questions":329},329,"Recognize governance and compliance regulations for AI systems",[330],{"id":331,"subcategoryId":327,"content":332,"hint":333,"type":177,"isPublic":9,"answers":334},47550,"A company is piloting an AI assistant on AWS that summarizes regulatory publications and includes citations to source documents. During user testing, compliance analysts assume that a cited summary has been verified against its sources and can be used without further checking. The company's governance policy requires users to understand the system's limitations before receiving production access. Which change to mandatory onboarding would BEST address the finding?","Consider what a citation allows a user to inspect and what it does not establish about generated content.",[335,339,343,347],{"id":336,"questionId":331,"content":337,"hint":338,"isCorrect":183,"position":152,"matchAnswerId":152},213570,"Teach analysts how the assistant ranks retrieved documents and display relevance scores beside citations so analysts can select the highest-ranked source.","Retrieval rankings indicate relevance, not whether a generated summary accurately represents a source. This training would not resolve the misunderstanding that cited content has been verified.",{"id":340,"questionId":331,"content":341,"hint":342,"isCorrect":183,"position":152,"matchAnswerId":152},213571,"Train analysts on approved data classifications, prohibited prompt content, and the retention policy for questions submitted to the assistant.","These are useful privacy and data-governance topics, but they do not address users' misunderstanding of citations or explain how to check generated summaries before relying on them.",{"id":344,"questionId":331,"content":345,"hint":346,"isCorrect":183,"position":152,"matchAnswerId":152},213572,"Present the assistant's overall evaluation results and teach analysts to use its average accuracy score when deciding whether an individual cited summary needs review.","Overall evaluation results are useful disclosures, but average accuracy does not establish the correctness of an individual summary. This approach does not teach analysts to verify whether a citation supports the generated claims.",{"id":348,"questionId":331,"content":349,"hint":350,"isCorrect":9,"position":152,"matchAnswerId":152},213573,"Explain that citations identify supporting material but do not establish correctness, demonstrate how to compare summaries with the original sources, and define when to request expert review.","This provides understandable information about the assistant's limitations and teaches appropriate use. Source verification and an expert-review path directly address the mistaken assumption that citations prove a summary is correct.",[],{"id":40,"code":56,"slug":51,"dateLastDay":152,"whatChanged":152,"pageSlug":57},[],[355,358,361,364,367,370,373,376,379,382,385,388,391,394],{"id":5,"slug":6,"description":7,"questionCount":8,"isActive":9,"datePublicModified":10,"difficulty":356,"examVersion":357},{"title":12,"subtitle":13,"slug":14,"coverImageUrl":15},{"slug":6,"pageSlug":14},{"id":18,"slug":19,"description":20,"questionCount":21,"isActive":9,"datePublicModified":10,"difficulty":359,"examVersion":360},{"title":23,"subtitle":24,"slug":25,"coverImageUrl":26},{"slug":19,"pageSlug":25},{"id":29,"slug":30,"description":31,"questionCount":32,"isActive":9,"datePublicModified":10,"difficulty":362,"examVersion":363},{"title":34,"subtitle":35,"slug":36,"coverImageUrl":37},{"slug":30,"pageSlug":36},{"id":40,"slug":41,"description":42,"questionCount":43,"isActive":9,"datePublicModified":10,"difficulty":365,"examVersion":366},{"title":45,"subtitle":46,"slug":47,"coverImageUrl":37},{"slug":41,"pageSlug":47},{"id":50,"slug":51,"description":52,"questionCount":53,"isActive":9,"datePublicModified":10,"difficulty":368,"examVersion":369},{"title":55,"subtitle":56,"slug":57,"coverImageUrl":37},{"slug":51,"pageSlug":57},{"id":91,"slug":92,"description":93,"questionCount":94,"isActive":9,"datePublicModified":64,"difficulty":371,"examVersion":372},{"title":96,"subtitle":97,"slug":98,"coverImageUrl":99},{"slug":92,"pageSlug":98},{"id":102,"slug":103,"description":104,"questionCount":105,"isActive":9,"datePublicModified":64,"difficulty":374,"examVersion":375},{"title":107,"subtitle":108,"slug":109,"coverImageUrl":37},{"slug":103,"pageSlug":109},{"id":60,"slug":61,"description":62,"questionCount":63,"isActive":9,"datePublicModified":64,"difficulty":377,"examVersion":378},{"title":66,"subtitle":67,"slug":68,"coverImageUrl":37},{"slug":61,"pageSlug":68},{"id":71,"slug":72,"description":73,"questionCount":74,"isActive":9,"datePublicModified":64,"difficulty":380,"examVersion":381},{"title":76,"subtitle":77,"slug":78,"coverImageUrl":37},{"slug":72,"pageSlug":78},{"id":81,"slug":82,"description":83,"questionCount":84,"isActive":9,"datePublicModified":64,"difficulty":383,"examVersion":384},{"title":86,"subtitle":87,"slug":88,"coverImageUrl":37},{"slug":82,"pageSlug":88},{"id":112,"slug":113,"description":114,"questionCount":115,"isActive":9,"datePublicModified":116,"difficulty":386,"examVersion":387},{"title":118,"subtitle":119,"slug":120,"coverImageUrl":37},{"slug":113,"pageSlug":120},{"id":123,"slug":124,"description":125,"questionCount":126,"isActive":9,"datePublicModified":127,"difficulty":389,"examVersion":390},{"title":129,"subtitle":130,"slug":131,"coverImageUrl":132},{"slug":124,"pageSlug":131},{"id":135,"slug":136,"description":137,"questionCount":138,"isActive":9,"datePublicModified":127,"difficulty":392,"examVersion":393},{"title":140,"subtitle":141,"slug":142,"coverImageUrl":143},{"slug":136,"pageSlug":142},{"id":146,"slug":147,"description":148,"questionCount":149,"isActive":9,"datePublicModified":127,"difficulty":395,"examVersion":396},{"title":96,"subtitle":97,"slug":98,"coverImageUrl":99},{"slug":147,"pageSlug":152},[],[399,405,411,416,421,425,429,433,437,442],{"id":5,"slug":19,"examVersionId":400,"description":401,"difficulty":402,"examVersion":403,"lessonsCount":404},3,"Unlock your cloud computing potential with our interactive AWS course designed for the AWS Certified Cloud Practitioner exam. 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