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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: ML Model Development | 26% | - Model tuning and evaluation
|
| Topic 2: ML Solution Monitoring, Maintenance, and Security | 24% | - Monitoring and observability
|
| Topic 3: Deployment and Orchestration of ML Workflows | 22% | - Model deployment
|
| Topic 4: Data Preparation for Machine Learning (ML) | 28% | - Data preprocessing and transformation
|
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NEW QUESTION # 57
A company wants to reduce the cost of its containerized ML applications. The applications use ML models that run on Amazon EC2 instances, AWS Lambda functions, and an Amazon Elastic Container Service (Amazon ECS) cluster. The EC2 workloads and ECS workloads use Amazon Elastic Block Store (Amazon EBS) volumes to save predictions and artifacts.
An ML engineer must identify resources that are being used inefficiently. The ML engineer also must generate recommendations to reduce the cost of these resources.
Which solution will meet these requirements with the LEAST development effort?
Answer: B
NEW QUESTION # 58
Case Study
An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.
The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data.
Which AWS service or feature can aggregate the data from the various data sources?
Answer: D
NEW QUESTION # 59
An ML engineer is developing a classification model. The ML engineer needs to use custom libraries in processing jobs, training jobs, and pipelines in Amazon SageMaker. Which solution will provide this functionality with the LEAST implementation effort?
Answer: A
Explanation:
Building a custom Docker container with the required libraries and hosting it in Amazon ECR allows SageMaker jobs, training, and pipelines to consistently use the same environment. This approach minimizes manual setup, ensures portability, and provides the least ongoing implementation effort compared to repeatedly installing or managing libraries separately.
NEW QUESTION # 60
A company is using an ML model to classify motion in videos. The data is stored in MP4 format in Amazon S3. When the company created the model, the company needed 4 months to label all the video frames.
The company needs to retrain the model with an existing training workflow in Amazon SageMaker AI. An ML engineer must implement a solution that decreases the labeling time.
Which solution will meet these requirements?
Answer: C
Explanation:
Option A is correct because Amazon SageMaker Ground Truth is the AWS service specifically designed to build labeled datasets for machine learning, including video classification and video frame labeling use cases.
AWS documentation states that Ground Truth can take video files in MP4 format stored in Amazon S3, automatically connect to that data, and extract video frames for labeling. This directly matches the scenario in the question, where the source data is MP4 in S3 and the company needs to relabel or expand training data for retraining an existing SageMaker workflow.
Ground Truth is also the best choice because AWS documents explain that it can reduce labeling time and cost through automated data labeling (active learning) for supported built-in task types. Instead of relying only on manual human labeling for every item, Ground Truth can use ML-assisted labeling to lower the total amount of human work needed, which is exactly what the company wants after previously spending four months labeling frames.
The other options do not fit as well. JumpStart provides pretrained models and solution templates, but AWS documentation does not position it as the service for running a video-frame labeling operation. Data Wrangler is for data preparation, transformation, and feature engineering, not human labeling of video frames. Amazon A2I with Rekognition is mainly documented for human review workflows such as Rekognition moderation outputs, not as the primary service for large-scale MP4 video-frame annotation used to create training datasets.
NEW QUESTION # 61
A company is using an ML model to classify motion in videos. The data is stored in MP4 format in Amazon S3. When the company created the model, the company needed 4 months to label all the video frames.
The company needs to retrain the model with an existing training workflow in Amazon SageMaker AI. An ML engineer must implement a solution that decreases the labeling time.
Which solution will meet these requirements?
Answer: C
Explanation:
Option A is correct because Amazon SageMaker Ground Truth is the AWS service specifically designed to build labeled datasets for machine learning, including video classification and video frame labeling use cases. AWS documentation states that Ground Truth can take video files in MP4 format stored in Amazon S3 , automatically connect to that data, and extract video frames for labeling. This directly matches the scenario in the question, where the source data is MP4 in S3 and the company needs to relabel or expand training data for retraining an existing SageMaker workflow.
Ground Truth is also the best choice because AWS documents explain that it can reduce labeling time and cost through automated data labeling (active learning) for supported built-in task types. Instead of relying only on manual human labeling for every item, Ground Truth can use ML-assisted labeling to lower the total amount of human work needed, which is exactly what the company wants after previously spending four months labeling frames.
The other options do not fit as well. JumpStart provides pretrained models and solution templates, but AWS documentation does not position it as the service for running a video-frame labeling operation. Data Wrangler is for data preparation, transformation, and feature engineering, not human labeling of video frames. Amazon A2I with Rekognition is mainly documented for human review workflows such as Rekognition moderation outputs, not as the primary service for large-scale MP4 video-frame annotation used to create training datasets.
NEW QUESTION # 62
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