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| Section | Weight | Objectives |
|---|---|---|
| Model Deployment and Inference | 20% | - SageMaker endpoints configuration - Model deployment strategies (real-time, batch) - A/B testing and shadow mode deployment - Model versioning and rollback - Inference optimization (latency, throughput) |
| Model Development | 30% | - Transfer learning and fine-tuning - Training and validation strategies - Distributed training - Hyperparameter optimization - ML frameworks (SageMaker, built-in algorithms) - Algorithm selection and model architecture |
| MLOps and Monitoring | 28% | - Model monitoring and drift detection - Security and access management for ML - Model lineage and reproducibility - CI/CD pipelines for ML - Cost optimization for ML workloads - Incident response and remediation |
| Data Processing | 22% | - Data ingestion and transformation - Handling imbalanced data - Data validation and quality assessment - Data pipelining with AWS services ( Glue, Data Brew, etc.) - Data preprocessing and feature engineering |
>> MLA-C01 Test Lab Questions <<
For Amazon professionals, passing the AWS Certified Machine Learning Engineer - Associate exams such as the MLA-C01 Exam is essential to achieve their dream professional life. However, passing the AWS Certified Machine Learning Engineer - Associate (MLA-C01) Exam is not an easy task, especially for those with busy schedules who need time to prepare well for the MLA-C01 Exam. To ensure success on the MLA-C01 Exam, you need Amazon MLA-C01 Exam Questions that contain all the relevant information about the exam.
NEW QUESTION # 103
A company uses Amazon SageMaker Studio to develop an ML model. The company has a single SageMaker Studio domain. An ML engineer needs to implement a solution that provides an automated alert when SageMaker compute costs reach a specific threshold.
Which solution will meet these requirements?
Answer: B
Explanation:
Adding resource tagging to the SageMaker user profile enables tracking and monitoring of costs associated with specific SageMaker resources.
AWS Budgets allows setting thresholds and automated alerts for costs and usage, making it the ideal service to notify the ML engineer when compute costs reach a specified limit.
This solution is efficient and integrates seamlessly with SageMaker and AWS cost management tools.
NEW QUESTION # 104
A company is using Amazon EMR. The company has a large dataset in Amazon S3 that needs to be ingested into Amazon SageMaker Feature Store. The dataset contains historical data and real-time streaming data.
The company must ensure that the Feature Store online store is updated with the most recent data as soon as the data becomes available. The company also must maintain a complete Feature Store offline store for batch processing.
Which solution will meet these requirements?
Answer: B
Explanation:
The SageMaker Feature Store Spark connector allows ingestion of large-scale data from Amazon EMR into Feature Store as Spark DataFrames. Enabling both the online store ensures real-time updates for the latest data, while the offline store maintains the full historical dataset for batch analytics. This setup meets both low-latency and historical processing requirements.
NEW QUESTION # 105
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 # 106
A company's dataset for prediction analytics contains duplicate records, missing data, and unusually extreme high or low values. The company needs a solution to resolve the data quality issues quickly. The solution must maintain data integrity and have the LEAST operational overhead.
Which solution will meet these requirements?
Answer: B
Explanation:
AWS Glue DataBrew is designed specifically for no-code and low-code data preparation, making it the fastest and lowest-overhead solution for resolving common data quality issues. DataBrew provides built-in transformations for deduplication, missing value imputation, and outlier handling while preserving data integrity.
Option A uses standard statistical techniques such as median imputation and value normalization, which are widely accepted and maintain the distribution of the data. DataBrew jobs are fully managed and do not require infrastructure setup or maintenance.
Option B deletes records, which can lead to data loss and does not preserve integrity. Option C introduces unnecessary infrastructure complexity and uses poor data imputation practices. Option D provides advanced capabilities but requires more configuration and ML expertise, increasing operational overhead.
AWS documentation clearly positions DataBrew as the preferred solution for quick, reliable data cleaning with minimal effort.
Therefore, Option A is the correct answer.
NEW QUESTION # 107
A company uses Amazon SageMaker Studio to develop an ML model. The company has a single SageMaker Studio domain. An ML engineer needs to implement a solution that provides an automated alert when SageMaker compute costs reach a specific threshold.
Which solution will meet these requirements?
Answer: B
NEW QUESTION # 108
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