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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Model Development | 30% | - Algorithm selection and model architecture - Transfer learning and fine-tuning - Distributed training - ML frameworks (SageMaker, built-in algorithms) - Training and validation strategies - Hyperparameter optimization |
| Topic 2: Data Processing | 22% | - Handling imbalanced data - Data ingestion and transformation - Data validation and quality assessment - Data preprocessing and feature engineering - Data pipelining with AWS services ( Glue, Data Brew, etc.) |
| Topic 3: MLOps and Monitoring | 28% | - Cost optimization for ML workloads - Model lineage and reproducibility - Incident response and remediation - CI/CD pipelines for ML - Model monitoring and drift detection - Security and access management for ML |
| Topic 4: Model Deployment and Inference | 20% | - Model deployment strategies (real-time, batch) - SageMaker endpoints configuration - Model versioning and rollback - A/B testing and shadow mode deployment - Inference optimization (latency, throughput) |
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NEW QUESTION # 173
An ML engineer receives datasets that contain missing values, duplicates, and extreme outliers.
The ML engineer must consolidate these datasets into a single data frame and must prepare the data for ML.
Which solution will meet these requirements?
Answer: D
NEW QUESTION # 174
A company is using Amazon SageMaker and millions of files to train an ML model. Each file is several megabytes in size. The files are stored in an Amazon S3 bucket. The company needs to improve training performance.
Which solution will meet these requirements in the LEAST amount of time?
Answer: B
NEW QUESTION # 175
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 # 176
A company wants to use Amazon SageMaker AI to host an ML model that runs on CPU for real-time predictions. The model has intermittent traffic during business hours and periods of no traffic after business hours.
Which hosting option will serve inference requests in the MOST cost-effective manner?
Answer: B
Explanation:
AWS recommends SageMaker Serverless Inference for workloads with intermittent or unpredictable traffic.
Serverless inference automatically scales compute resources to zero when idle, eliminating costs during periods with no traffic.
For business-hour traffic spikes, provisioned concurrency ensures low-latency responses while still avoiding the cost of continuously running instances. This model is especially cost-effective for CPU-based inference workloads.
Real-time endpoints incur costs even when idle, and asynchronous inference is designed for long-running jobs rather than low-latency predictions.
AWS documentation explicitly states that Serverless Inference is the most cost-effective option for intermittent real-time workloads.
Therefore, Option B is the correct choice.
NEW QUESTION # 177
A company runs an ML model on Amazon SageMaker AI. The company uses an automatic process that makes API calls to create training jobs for the model. The company has new compliance rules that prohibit the collection of aggregated metadata from training jobs.
Which solution will prevent SageMaker AI from collecting metadata from the training jobs?
Answer: D
Explanation:
Amazon SageMaker AI automatically collects aggregated metadata from training jobs to improve service reliability, performance, and operational insights. This metadata can include information such as algorithm usage, instance types, resource utilization, and job configuration details. However, AWS documentation clearly states that customers can opt out of SageMaker metadata collection to meet regulatory or compliance requirements.
SageMaker provides a supported mechanism to disable metadata tracking at the training job level. By explicitly opting out of metadata tracking when submitting training jobs-either through the AWS Management Console, AWS CLI, or SDK-the service will stop collecting aggregated metadata for those jobs. This option is specifically designed for customers with strict compliance, data residency, or regulatory constraints.
Option B is incorrect because running training jobs in a private subnet within a custom VPC controls network isolation, not service-level telemetry or metadata collection. Metadata collection occurs at the SageMaker service layer and is independent of VPC configuration.
Option C is also incorrect because encrypting training data with a customer-managed AWS KMS key protects data at rest and in transit but does not prevent SageMaker from collecting operational metadata about training jobs.
Option D is incorrect because AWS Nitro instances provide enhanced security and performance isolation at the infrastructure level but have no impact on SageMaker's metadata collection mechanisms.
Therefore, opting out of metadata tracking for training jobs is the only solution that directly addresses the compliance requirement and is explicitly supported by AWS documentation.
NEW QUESTION # 178
......
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