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| Section | Weight | Objectives |
|---|---|---|
| Model Deployment and Inference | 20% | - Inference optimization (latency, throughput) - Model deployment strategies (real-time, batch) - A/B testing and shadow mode deployment - SageMaker endpoints configuration - Model versioning and rollback |
| Data Processing | 22% | - Data ingestion and transformation - Data validation and quality assessment - Handling imbalanced data - Data pipelining with AWS services ( Glue, Data Brew, etc.) - Data preprocessing and feature engineering |
| Model Development | 30% | - Hyperparameter optimization - ML frameworks (SageMaker, built-in algorithms) - Training and validation strategies - Algorithm selection and model architecture - Transfer learning and fine-tuning - Distributed training |
| MLOps and Monitoring | 28% | - Model monitoring and drift detection - Security and access management for ML - CI/CD pipelines for ML - Incident response and remediation - Model lineage and reproducibility - Cost optimization for ML workloads |
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NEW QUESTION # 113
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 # 114
A construction company is using Amazon SageMaker AI to train specialized custom object detection models to identify road damage. The company uses images from multiple cameras. The images are stored as JPEG objects in an Amazon S3 bucket.
The images need to be pre-processed by using computationally intensive computer vision techniques before the images can be used in the training job. The company needs to optimize data loading and pre-processing in the training job. The solution cannot affect model performance or increase compute or storage resources.
Which solution will meet these requirements?
Answer: B
Explanation:
AWS documentation recommends using RecordIO format with lazy loading to optimize data input pipelines for image-based training workloads. RecordIO is a binary data format that enables sequential reads, reducing I
/O overhead and improving throughput during training.
By converting JPEG images into RecordIO format, the training job can read data more efficiently from Amazon S3. Lazy loading ensures that only the required data is loaded into memory when needed, which optimizes CPU utilization during computationally intensive preprocessing steps.
Option A (file mode) results in many small S3 GET requests, which can become a bottleneck for large image datasets. Option B changes training behavior and can negatively affect convergence and performance. Option C reduces image quality, which directly impacts model accuracy and violates the requirement.
AWS SageMaker documentation explicitly highlights RecordIO and lazy loading as best practices for high- performance image training pipelines, especially when preprocessing is CPU-intensive.
Therefore, Option D is the correct and AWS-aligned solution.
NEW QUESTION # 115
A company is using Amazon SageMaker AI to build an ML model to predict customer behavior. The company needs to explain the bias in the model to an auditor. The explanation must focus on demographic data of the customers.
Which solution will meet these requirements?
Answer: C
Explanation:
AWS documentation identifies Amazon SageMaker Clarify as the primary service for detecting, measuring, and explaining bias in ML models, particularly across demographic and sensitive attributes such as age, gender, and location. Clarify can analyze bias before training, after training, and during inference, making it suitable for audit and compliance requirements.
SageMaker Clarify generates bias reports using established fairness metrics such as difference in positive proportions, disparate impact, and conditional demographic disparity. These reports are exportable and auditor-friendly, directly meeting the requirement to explain bias to an external party.
AWS Glue DataBrew focuses on data preparation and quality, not bias detection. Amazon QuickSight does not provide ML fairness metrics. Amazon CloudWatch captures operational metrics, not demographic bias indicators.
AWS best practices explicitly recommend SageMaker Clarify for model transparency, fairness evaluation, and regulatory reporting.
Therefore, Option A is the correct and AWS-verified solution.
NEW QUESTION # 116
A company is using Amazon SageMaker to create ML models. The company's data scientists need fine-grained control of the ML workflows that they orchestrate. The data scientists also need the ability to visualize SageMaker jobs and workflows as a directed acyclic graph (DAG). The data scientists must keep a running history of model discovery experiments and must establish model governance for auditing and compliance verifications.
Which solution will meet these requirements?
Answer: A
NEW QUESTION # 117
A company needs an AWS solution that will automatically create versions of ML models as the models are created.
Which solution will meet this requirement?
Answer: C
NEW QUESTION # 118
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