MLA-C01 RELIABLE EXAM SIMS | MLA-C01 TEST STUDY GUIDE

MLA-C01 Reliable Exam Sims | MLA-C01 Test Study Guide

MLA-C01 Reliable Exam Sims | MLA-C01 Test Study Guide

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q35-Q40):

NEW QUESTION # 35
An ML engineer needs to use Amazon SageMaker to fine-tune a large language model (LLM) for text summarization. The ML engineer must follow a low-code no-code (LCNC) approach.
Which solution will meet these requirements?

  • A. Use SageMaker Studio to fine-tune an LLM that is deployed on Amazon EC2 instances.
  • B. Use SageMaker Autopilot to fine-tune an LLM that is deployed on Amazon EC2 instances.
  • C. Use SageMaker Autopilot to fine-tune an LLM that is deployed by SageMaker JumpStart.
  • D. Use SageMaker Autopilot to fine-tune an LLM that is deployed by a custom API endpoint.

Answer: C

Explanation:
SageMaker JumpStart provides access to pre-trained models, including large language models (LLMs), which can be easily deployed and fine-tuned with a low-code/no-code (LCNC) approach. Using SageMaker Autopilot with JumpStart simplifies the fine-tuning process by automating model optimization and reducing the need for extensive coding, making it the ideal solution for this requirement.


NEW QUESTION # 36
A company that has hundreds of data scientists is using Amazon SageMaker to create ML models. The models are in model groups in the SageMaker Model Registry.
The data scientists are grouped into three categories: computer vision, natural language processing (NLP), and speech recognition. An ML engineer needs to implement a solution to organize the existing models into these groups to improve model discoverability at scale. The solution must not affect the integrity of the model artifacts and their existing groupings.
Which solution will meet these requirements?

  • A. Use SageMaker ML Lineage Tracking to automatically identify and tag which model groups should contain the models.
  • B. Create a Model Registry collection for each of the three categories. Move the existing model groups into the collections.
  • C. Create a model group for each category. Move the existing models into these category model groups.
  • D. Create a custom tag for each of the three categories. Add the tags to the model packages in the SageMaker Model Registry.

Answer: D

Explanation:
Using custom tags allows you to organize and categorize models in the SageMaker Model Registry without altering their existing groupings or affecting the integrity of the model artifacts. Tags are a lightweight and scalable way to improve model discoverability at scale, enabling the data scientists to filter and identify models by category (e.g., computer vision, NLP, speech recognition). This approach meets the requirements efficiently without introducing structural changes to the existing model registry setup.


NEW QUESTION # 37
A company stores time-series data about user clicks in an Amazon S3 bucket. The raw data consists of millions of rows of user activity every day. ML engineers access the data to develop their ML models.
The ML engineers need to generate daily reports and analyze click trends over the past 3 days by using Amazon Athena. The company must retain the data for 30 days before archiving the data.
Which solution will provide the HIGHEST performance for data retrieval?

  • A. Organize the time-series data into partitions by date prefix in the S3 bucket. Apply S3 Lifecycle policies to archive partitions that are older than 30 days to S3 Glacier Flexible Retrieval.
  • B. Put each day's time-series data into its own S3 bucket. Use S3 Lifecycle policies to archive S3 buckets that hold data that is older than 30 days to S3 Glacier Flexible Retrieval.
  • C. Keep all the time-series data without partitioning in the S3 bucket. Manually move data that is older than 30 days to separate S3 buckets.
  • D. Create AWS Lambda functions to copy the time-series data into separate S3 buckets. Apply S3 Lifecycle policies to archive data that is older than 30 days to S3 Glacier Flexible Retrieval.

Answer: A

Explanation:
Partitioning the time-series data by date prefix in the S3 bucket significantly improves query performance in Amazon Athena by reducing the amount of data that needs to be scanned during queries. This allows the ML engineers to efficiently analyze trends over specific time periods, such as the past 3 days. Applying S3 Lifecycle policies to archive partitions older than 30 days to S3 Glacier FlexibleRetrieval ensures cost- effective data retention and storage management while maintaining high performance for recent data retrieval.


NEW QUESTION # 38
An ML engineer needs to implement a solution to host a trained ML model. The rate of requests to the model will be inconsistent throughout the day.
The ML engineer needs a scalable solution that minimizes costs when the model is not in use. The solution also must maintain the model's capacity to respond to requests during times of peak usage.
Which solution will meet these requirements?

  • A. Create AWS Lambda functions that have fixed concurrency to host the model. Configure the Lambda functions to automatically scale based on the number of requests to the model.
  • B. Deploy the model to an Amazon SageMaker endpoint. Create SageMaker endpoint auto scaling policies that are based on Amazon CloudWatch metrics to adjust the number of instances dynamically.
  • C. Deploy the model to an Amazon SageMaker endpoint. Deploy multiple copies of the model to the endpoint. Create an Application Load Balancer to route traffic between the different copies of the model at the endpoint.
  • D. Deploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster that uses AWS Fargate. Set a static number of tasks to handle requests during times of peak usage.

Answer: B


NEW QUESTION # 39
A company has deployed an XGBoost prediction model in production to predict if a customer is likely to cancel a subscription. The company uses Amazon SageMaker Model Monitor to detect deviations in the F1 score.
During a baseline analysis of model quality, the company recorded a threshold for the F1 score. After several months of no change, the model's F1 score decreases significantly.
What could be the reason for the reduced F1 score?

  • A. The original baseline data had a data quality issue of missing values.
  • B. Concept drift occurred in the underlying customer data that was used for predictions.
  • C. The model was not sufficiently complex to capture all the patterns in the original baseline data.
  • D. Incorrect ground truth labels were provided to Model Monitor during the calculation of the baseline.

Answer: B

Explanation:
* Problem Description:
* The F1 score, which is a balance of precision and recall, has decreased significantly. This indicates the model's predictions are no longer aligned with the real-world data distribution.
* Why Concept Drift?
* Concept driftoccurs when the statistical properties of the target variable or features change over time. For example, customer behaviors or subscription cancellation patterns may have shifted, leading to reduced model accuracy.
* Signs of Concept Drift:
* Deviation in performance metrics (e.g., F1 score) over time.
* Declining prediction accuracy for certain groups or scenarios.
* Solution:
* Monitor for drift using tools like SageMaker Model Monitor.
* Regularly retrain the model with updated data to account for the drift.
* Why Not Other Options?:
* B: Model complexity is unrelated if the model initially performed well.
* C: Data quality issues would have been detected during baseline analysis.
* D: Incorrect ground truth labels would have resulted in a consistently poor baseline.
Conclusion: The decrease in F1 score is most likely due toconcept driftin the customer data, requiring retraining of the model with new data.


NEW QUESTION # 40
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