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| Section | Weight | Objectives |
|---|---|---|
| Data Processing | 22% | - Data ingestion and transformation - Data pipelining with AWS services ( Glue, Data Brew, etc.) - Handling imbalanced data - Data preprocessing and feature engineering - Data validation and quality assessment |
| MLOps and Monitoring | 28% | - Security and access management for ML - CI/CD pipelines for ML - Model lineage and reproducibility - Cost optimization for ML workloads - Incident response and remediation - Model monitoring and drift detection |
| Model Development | 30% | - Hyperparameter optimization - Transfer learning and fine-tuning - ML frameworks (SageMaker, built-in algorithms) - Training and validation strategies - Distributed training - Algorithm selection and model architecture |
| Model Deployment and Inference | 20% | - Inference optimization (latency, throughput) - Model versioning and rollback - SageMaker endpoints configuration - Model deployment strategies (real-time, batch) - A/B testing and shadow mode deployment |
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NEW QUESTION # 186
An ML engineer needs to create data ingestion pipelines and ML model deployment pipelines on AWS. All the raw data is stored in Amazon S3 buckets.
Which solution will meet these requirements?
Answer: D
Explanation:
AWS Glue is a serverless data integration service that is well-suited for creating data ingestion pipelines, especially when raw data is stored in Amazon S3. It can clean, transform, and catalog data, making it accessible for downstream ML tasks.
Amazon SageMaker Studio Classic provides a comprehensive environment for building, training, and deploying ML models. It includes built-in tools and capabilities to create efficient model deployment pipelines with minimal setup.
This combination ensures seamless integration of data ingestion and ML model deployment with minimal operational overhead.
NEW QUESTION # 187
Hotspot Question
An ML engineer is working on an ML model to predict the prices of similarly sized homes. The model will base predictions on several features The ML engineer will use the following feature engineering techniques to estimate the prices of the homes:
- Feature splitting
- Logarithmic transformation
- One-hot encoding
- Standardized distribution
Select the correct feature engineering techniques for the following list of features. Each feature engineering technique should be selected one time or not at all (Select three.)
Answer:
Explanation:
NEW QUESTION # 188
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: C
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 # 189
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: C
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 # 190
An ML engineer is evaluating several ML models and must choose one model to use in production. The cost of false negative predictions by the models is much higher than the cost of false positive predictions.
Which metric finding should the ML engineer prioritize the MOST when choosing the model?
Answer: C
Explanation:
Recall measures the ability of a model to correctly identify all positive cases (true positives) out of all actual positives, minimizing false negatives. Since the cost of false negatives is much higher than falsepositives in this scenario, the ML engineer should prioritize models with high recall to reduce the likelihood of missing positive cases.
NEW QUESTION # 191
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