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| Section | Weight | Objectives |
|---|---|---|
| Model Development | 30% | - Algorithm selection and model architecture - Hyperparameter optimization - ML frameworks (SageMaker, built-in algorithms) - Training and validation strategies - Transfer learning and fine-tuning - Distributed training |
| Model Deployment and Inference | 20% | - A/B testing and shadow mode deployment - Model deployment strategies (real-time, batch) - SageMaker endpoints configuration - Inference optimization (latency, throughput) - Model versioning and rollback |
| MLOps and Monitoring | 28% | - CI/CD pipelines for ML - Cost optimization for ML workloads - Model lineage and reproducibility - Security and access management for ML - Model monitoring and drift detection - Incident response and remediation |
| Data Processing | 22% | - Data validation and quality assessment - Handling imbalanced data - Data preprocessing and feature engineering - Data pipelining with AWS services ( Glue, Data Brew, etc.) - Data ingestion and transformation |
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NEW QUESTION # 225
A financial company receives a high volume of real-time market data streams from an external provider. The streams consist of thousands of JSON records every second.
The company needs to implement a scalable solution on AWS to identify anomalous data points.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: A
Explanation:
This solution is the most efficient and involves the least operational overhead:
Amazon Kinesis data streams efficiently handle real-time ingestion of high-volume streaming data.
Amazon Managed Service for Apache Flink provides a fully managed environment for stream processing with built-in support for RANDOM_CUT_FOREST, an algorithm designed for anomaly detection in real- time streaming data.
This approach eliminates the need for deploying and managing additional infrastructure like SageMaker endpoints, Lambda functions, or external tools, making it the most scalable and operationally simple solution.
NEW QUESTION # 226
An ML engineer needs to run intensive model training jobs each month that can take 48-72 hours. The jobs can be interrupted and resumed. The engineer has a fixed budget and needs the most cost-effective compute option.
Which solution will meet these requirements?
Answer: D
Explanation:
Amazon EC2 Spot Instances provide unused compute capacity at discounts of up to 90%. AWS documentation strongly recommends Spot Instances for interruptible workloads such as long-running ML training jobs that can resume from checkpoints.
By enabling automatic checkpointing, SageMaker saves model state periodically to Amazon S3. If the Spot Instance is interrupted, training can resume with minimal loss of progress.
Reserved Instances and Savings Plans require long-term commitment and are not as cost-effective for sporadic workloads. On-Demand Instances are the most expensive option.
Therefore, Option D is the most cost-effective solution.
NEW QUESTION # 227
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 # 228
A company needs to host a custom ML model to perform forecast analysis. The forecast analysis will occur with predictable and sustained load during the same 2-hour period every day.
Multiple invocations during the analysis period will require quick responses. The company needs AWS to manage the underlying infrastructure and any auto scaling activities.
Which solution will meet these requirements?
Answer: C
Explanation:
SageMaker Serverless Inference is ideal for workloads with predictable, intermittent demand. By enabling provisioned concurrency, the model can handle multiple invocations quickly during the high-demand 2-hour period. AWS manages the underlying infrastructure and scaling, ensuring the solution meets performance requirements with minimal operational overhead. This approach is cost-effective since it scales down when not in use.
NEW QUESTION # 229
A company has an existing Amazon SageMaker model (v1) on a production endpoint. The company develops a new model version (v2) and needs to test v2 in production before substituting v2 for v1.
The company needs to implement a solution to minimize the risk of v2 generating incorrect output in production. The solution must prevent any disruption of production traffic during the change to v2.
Which solution will meet these requirements?
Answer: D
Explanation:
A shadow variant allows the new model (v2) to receive a copy of 100% of production traffic while only v1's outputs are returned to users. This enables safe side-by-side evaluation of v2 without impacting production responses, minimizing risk and ensuring no disruption of live traffic until v2 is validated and promoted.
NEW QUESTION # 230
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