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Amazon MLA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	Data Preparation for Machine Learning (ML): This section of the exam measures skills of Forensic Data Analysts and covers collecting, storing, and preparing data for machine learning. It focuses on understanding different data formats, ingestion methods, and AWS tools used to process and transform data. Candidates are expected to clean and engineer features, ensure data integrity, and address biases or compliance issues, which are crucial for preparing high-quality datasets in fraud analysis contexts.
Topic 2	Deployment and Orchestration of ML Workflows: This section of the exam measures skills of Forensic Data Analysts and focuses on deploying machine learning models into production environments. It covers choosing the right infrastructure, managing containers, automating scaling, and orchestrating workflows through CI CD pipelines. Candidates must be able to build and script environments that support consistent deployment and efficient retraining cycles in real-world fraud detection systems.
Topic 3	ML Model Development: This section of the exam measures skills of Fraud Examiners and covers choosing and training machine learning models to solve business problems such as fraud detection. It includes selecting algorithms, using built-in or custom models, tuning parameters, and evaluating performance with standard metrics. The domain emphasizes refining models to avoid overfitting and maintaining version control to support ongoing investigations and audit trails.
Topic 4	ML Solution Monitoring, Maintenance, and Security: This section of the exam measures skills of Fraud Examiners and assesses the ability to monitor machine learning models, manage infrastructure costs, and apply security best practices. It includes setting up model performance tracking, detecting drift, and using AWS tools for logging and alerts. Candidates are also tested on configuring access controls, auditing environments, and maintaining compliance in sensitive data environments like financial fraud detection.

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q15-Q20):

NEW QUESTION # 15
A company has a binary classification model in production. An ML engineer needs to develop a new version of the model.
The new model version must maximize correct predictions of positive labels and negative labels. The ML engineer must use a metric to recalibrate the model to meet these requirements.
Which metric should the ML engineer use for the model recalibration?

A. Accuracy
B. Recall
C. Specificity
D. Precision

Answer: A

Explanation:
Accuracy measures the proportion of correctly predicted labels (both positive and negative) out of the total predictions. It is the appropriate metric when the goal is to maximize the correct predictions of both positive and negative labels. However, it assumes that the classes are balanced; if the classes are imbalanced, other metrics like precision, recall, or specificity may be more relevant depending on the specific needs.

NEW QUESTION # 16
A company uses a hybrid cloud environment. A model that is deployed on premises uses data in Amazon 53 to provide customers with a live conversational engine.
The model is using sensitive data. An ML engineer needs to implement a solution to identify and remove the sensitive data.
Which solution will meet these requirements with the LEAST operational overhead?

A. Deploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster that uses AWS Fargate. Create an AWS Batch job to identify and remove the sensitive data.
B. Use Amazon Comprehend to identify the sensitive data. Launch Amazon EC2 instances to remove the sensitive data.
C. Deploy the model on Amazon SageMaker. Create a set of AWS Lambda functions to identify and remove the sensitive data.
D. Use Amazon Macie to identify the sensitive data. Create a set of AWS Lambda functions to remove the sensitive data.

Answer: D

Explanation:
Amazon Macie is a fully managed data security and privacy service that uses machine learning to discover and classify sensitive data in Amazon S3. It is purpose-built to identify sensitive data with minimal operational overhead. After identifying the sensitive data, you can use AWS Lambda functions to automate the process of removing or redacting the sensitive data, ensuring efficiency and integration with the hybrid cloud environment. This solution requires the least development effort and aligns with the requirement to handle sensitive data effectively.

NEW QUESTION # 17
An ML engineer is using Amazon SageMaker to train a deep learning model that requires distributed training.
After some training attempts, the ML engineer observes that the instances are not performing as expected. The ML engineer identifies communication overhead between the training instances.
What should the ML engineer do to MINIMIZE the communication overhead between the instances?

A. Place the instances in the same VPC subnet. Store the data in the same AWS Region but in a different Availability Zone from where the instances are deployed.
B. Place the instances in the same VPC subnet. Store the data in a different AWS Region from where the instances are deployed.
C. Place the instances in the same VPC subnet. Store the data in the same AWS Region and Availability Zone where the instances are deployed.
D. Place the instances in the same VPC subnet but in different Availability Zones. Store the data in a different AWS Region from where the instances are deployed.

Answer: C

Explanation:
To minimize communication overhead during distributed training:
1. Same VPC Subnet: Ensures low-latency communication between training instances by keeping the network traffic within a single subnet.
2. Same AWS Region and Availability Zone: Reduces network latency further because cross-AZ communication incurs additional latency and costs.
3. Data in the Same Region and AZ: Ensures that the training data is accessed with minimal latency, improving performance during training.
This configuration optimizes communication efficiency and minimizes overhead.

NEW QUESTION # 18
An ML engineer trained an ML model on Amazon SageMaker to detect automobile accidents from dosed- circuit TV footage. The ML engineer used SageMaker Data Wrangler to create a training dataset of images of accidents and non-accidents.
The model performed well during training and validation. However, the model is underperforming in production because of variations in the quality of the images from various cameras.
Which solution will improve the model's accuracy in the LEAST amount of time?

A. Collect more images from all the cameras. Use Data Wrangler to prepare a new training dataset.
B. Recreate the training dataset by using the Data Wrangler corrupt image transform. Specify the impulse noise option.
C. Recreate the training dataset by using the Data Wrangler enhance image contrast transform. Specify the Gamma contrast option.
D. Recreate the training dataset by using the Data Wrangler resize image transform. Crop all images to the same size.

Answer: B

Explanation:
The model is underperforming in production due to variations in image quality from different cameras. Using the corrupt image transform with the impulse noise option in SageMaker Data Wrangler simulates real-world noise and variations in the training dataset. This approach helps the model become more robust to inconsistencies in image quality, improving its accuracy in production without the need to collect and process new data, thereby saving time.

NEW QUESTION # 19
A company is running ML models on premises by using custom Python scripts and proprietary datasets. The company is using PyTorch. The model building requires unique domain knowledge. The company needs to move the models to AWS.
Which solution will meet these requirements with the LEAST effort?

A. Use SageMaker script mode and premade images for ML frameworks.
B. Build a container on AWS that includes custom packages and a choice of ML frameworks.
C. Purchase similar production models through AWS Marketplace.
D. Use SageMaker built-in algorithms to train the proprietary datasets.

Answer: A

Explanation:
SageMaker script mode allows you to bring existing custom Python scripts and run them on AWS with minimal changes. SageMaker provides prebuilt containers for ML frameworks like PyTorch, simplifying the migration process. This approach enables the company to leverage their existing Python scripts and domain knowledge while benefiting from the scalability and managed environment of SageMaker. It requires the least effort compared to building custom containers or retraining models from scratch.

NEW QUESTION # 20
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Reliable 100% MLA-C01 Correct Answers | 100% Free Exam MLA-C01 Success

Amazon MLA-C01 Exam Syllabus Topics:

MLA-C01 Exam 100% Correct Answers & High-quality Exam MLA-C01 Success Pass Success

Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q15-Q20):

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