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NEW QUESTION # 124
A company uses an Amazon SageMaker AI model for real-time inference with auto scaling enabled. During peak usage, new instances launch before existing instances are fully ready, causing inefficiencies and delays.
Which solution will optimize the scaling process without affecting response times?
Answer: A
Explanation:
Amazon SageMaker auto scaling uses cooldown periods to control how frequently scaling activities occur.
When scale-out happens too quickly, new instances may receive traffic before they are fully initialized, leading to inefficiencies and latency.
AWS documentation recommends increasing the scale-out cooldown period to give newly launched instances sufficient time to initialize and become healthy before additional scaling events occur. This ensures stable performance during traffic spikes without impacting response times.
Multi-model endpoints address model hosting efficiency, not scaling timing. API Gateway and Lambda add unnecessary latency and complexity. Decreasing scale-in cooldown does not address scale-out issues.
Therefore, Option D is the correct and AWS-aligned solution.
NEW QUESTION # 125
An ML engineer is building a generative AI application on Amazon Bedrock by using large language models (LLMs).
Select the correct generative AI term from the following list for each description. Each term should be selected one time or not at all. (Select three.)
* Embedding
* Retrieval Augmented Generation (RAG)
* Temperature
* Token
Answer:
Explanation:
Explanation:
Text representation of basic units of data processed by LLMs: Token
High-dimensional vectors that contain the semantic meaning of text: Embedding Enrichment of information from additional data sources to improve a generated response: Retrieval Augmented Generation (RAG) Comprehensive Detailed Explanation Token:
Description: A token represents the smallest unit of text (e.g., a word or part of a word) that an LLM processes. For example, " running " might be split into two tokens: " run " and " ing. " Why? Tokens are the fundamental building blocks for LLM input and output processing, ensuring that the model can understand and generate text efficiently.
Embedding:
Description: High-dimensional vectors that encode the semantic meaning of text. These vectors are representations of words, sentences, or even paragraphs in a way that reflects their relationships and meaning.
Why? Embeddings are essential for enabling similarity search, clustering, or any task requiring semantic understanding. They allow the model to " understand " text contextually.
Retrieval Augmented Generation (RAG):
Description: A technique where information is enriched or retrieved from external data sources (e.g., knowledge bases or document stores) to improve the accuracy and relevance of a model ' s generated responses.
Why? RAG enhances the generative capabilities of LLMs by grounding their responses in factual and up-to- date information, reducing hallucinations in generated text.
By matching these terms to their respective descriptions, the ML engineer can effectively leverage these concepts to build robust and contextually aware generative AI applications on Amazon Bedrock.
NEW QUESTION # 126
A hospital wants to predict patient outcomes for the coming year An ML engineer must improve several existing ML models that currently perform poorly.
Select the correct regularization method from the following list to improve each model Select each regularization method one time, more than one time, or not at all. (Select THREE.)
* L1 regularization
* L2 regularization
* Early stopping
Answer:
Explanation:
Explanation:
Linear regression model whose coefficients should shrink but not become zero answer: L2 regularization AWS says L2 produces smaller overall weight values and is the right fit when coefficients should be reduced without being forced to zero.
Polynomial regression model with irrelevant polynomial terms that should be eliminated answer: L1 regularization AWS says L1 reduces the number of features used by pushing small weights to zero , which matches elimination of irrelevant terms.
Logistic regression model that has highly correlated features to eliminate highly redundant predictors L1 regularization This is the nuanced one. AWS says L2 stabilizes weights when there is high correlation between features , but because the question explicitly says eliminate highly redundant predictors , L1 is the better match since it creates sparsity and removes predictors by zeroing coefficients.
NEW QUESTION # 127
A company needs to give its ML engineers appropriate access to training data. The ML engineers must access training data from only their own business group. The ML engineers must not be allowed to access training data from other business groups.
The company uses a single AWS account and stores all the training data in Amazon S3 buckets. All ML model training occurs in Amazon SageMaker.
Which solution will provide the ML engineers with the appropriate access?
Answer: D
Explanation:
By creating IAM policies with specific permissions, you can restrict access to Amazon S3 buckets or objects based on the user ' s business group. These policies can be attached to IAM users or IAM roles associated with the ML engineers, ensuring that each engineer can only access training data belonging to their group.
This approach is secure, scalable, and aligns with AWS best practices for access control.
NEW QUESTION # 128
An ML engineer has an Amazon Comprehend custom model in Account A in the us-east-1 Region. The ML engineer needs to copy the model to Account # in the same Region.
Which solution will meet this requirement with the LEAST development effort?
Answer: A
Explanation:
Amazon Comprehend provides the ImportModel API operation, which allows you to copy a custom model between AWS accounts. By creating a resource-based IAM policy on the model in Account A, you can grant Account B the necessary permissions to access and import the model. This approach requires minimal development effort and is the AWS-recommended method for sharing custom models across accounts.
NEW QUESTION # 129
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