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| Section | Weight | Objectives |
|---|---|---|
| MLOps and Monitoring | 28% | - Model lineage and reproducibility - Security and access management for ML - Model monitoring and drift detection - CI/CD pipelines for ML - Cost optimization for ML workloads - Incident response and remediation |
| Model Development | 30% | - Hyperparameter optimization - Algorithm selection and model architecture - Training and validation strategies - ML frameworks (SageMaker, built-in algorithms) - Transfer learning and fine-tuning - Distributed training |
| Model Deployment and Inference | 20% | - Model versioning and rollback - SageMaker endpoints configuration - Model deployment strategies (real-time, batch) - Inference optimization (latency, throughput) - A/B testing and shadow mode deployment |
| Data Processing | 22% | - Data preprocessing and feature engineering - Data validation and quality assessment - Data pipelining with AWS services ( Glue, Data Brew, etc.) - Handling imbalanced data - Data ingestion and transformation |
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NEW QUESTION # 154
A company is using ML to predict the presence of a specific weed in a farmer's field. The company is using the Amazon SageMaker linear learner built-in algorithm with a value of multiclass_dassifier for the predictorjype hyperparameter.
What should the company do to MINIMIZE false positives?
Answer: C
Explanation:
Thetarget_precisionhyperparameter in the Amazon SageMaker linear learner controls the trade-off between precision and recall for the model. Increasing the target_precision prioritizes minimizing false positives by making the model more cautious in its predictions. This approach is effective for use cases where false positives have higher consequences than false negatives.
NEW QUESTION # 155
An ML engineer wants to use a set of survey responses as training data for an ML classifier. All the survey responses are either "yes" or "no." The ML engineer needs to convert the responses into a feature that will produce better model training results. The ML engineer must not increase the dimensionality of the dataset.
Which methods will meet these requirements? (Choose two.)
Answer: C,D
Explanation:
Both binary encoding and label encoding convert categorical yes/no responses into numerical values without increasing dimensionality. For example, mapping yes → 1 and no → 0. Unlike one-hot encoding, which would add extra dimensions, these methods keep the dataset compact and effective for training.
NEW QUESTION # 156
A manufacturing company uses an ML model to determine whether products meet a standard for quality. The model produces an output of "Passed" or "Failed." Robots separate the products into the two categories by using the model to analyze photos on the assembly line.
Which metrics should the company use to evaluate the model's performance? (Choose two.)
Answer: A,C
NEW QUESTION # 157
A company is developing a customer support AI assistant by using an Amazon Bedrock Retrieval Augmented Generation (RAG) pipeline. The AI assistant retrieves articles from a knowledge base stored in Amazon S3.
The company uses Amazon OpenSearch Service to index the knowledge base. The AI assistant uses an Amazon Bedrock Titan Embeddings model for vector search.
The company wants to improve the relevance of the retrieved articles to improve the quality of the AI assistant's answers.
Which solution will meet these requirements?
Answer: A
Explanation:
In a Retrieval Augmented Generation (RAG) architecture, retrieval quality directly impacts response accuracy. AWS documentation for Bedrock and OpenSearch highlights the use of reranker models to improve relevance after initial vector search retrieval.
Vector search retrieves documents based on embedding similarity, but the top results are not always the most contextually relevant. A reranker model evaluates the retrieved documents against the user query and reorders them based on semantic relevance before sending them to the foundation model.
Option A improves readability but does not improve retrieval relevance. Option C filters data before retrieval, which can reduce recall. Option D improves performance, not relevance.
AWS explicitly recommends reranking as a best practice for improving answer quality in RAG systems.
Therefore, Option B is the correct solution.
NEW QUESTION # 158
Case Study
An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.
The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data.
Before the ML engineer trains the model, the ML engineer must resolve the issue of the imbalanced data.
Which solution will meet this requirement with the LEAST operational effort?
Answer: B
NEW QUESTION # 159
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