Valid MLA-C01 Exam Notes - MLA-C01 Exam Review

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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	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 3	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.
Topic 4	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.

>> Valid MLA-C01 Exam Notes <<

Amazon MLA-C01 Exam Review - Exam MLA-C01 Lab Questions

Our AWS Certified Machine Learning Engineer - Associate (MLA-C01) exam questions are being offered in three easy-to-use and compatible formats. This MLA-C01 exam dumps formats offer a user-friendly interface and are compatible with all devices, operating systems, and browsers. The TroytecDumps AWS Certified Machine Learning Engineer - Associate (MLA-C01) PDF questions file contains real and valid Amazon MLA-C01 exam questions that assist you in MLA-C01 exam dumps preparation and boost the candidate's confidence to pass the challenging AWS Certified Machine Learning Engineer - Associate (MLA-C01) exam easily.

Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q22-Q27):

NEW QUESTION # 22
A company wants to improve the sustainability of its ML operations.
Which actions will reduce the energy usage and computational resources that are associated with the company's training jobs? (Choose two.)

A. Use Amazon SageMaker Ground Truth for data labeling.
B. Deploy models by using AWS Lambda functions.
C. Use PyTorch or TensorFlow with the distributed training option.
D. Use Amazon SageMaker Debugger to stop training jobs when non-converging conditions are detected.
E. Use AWS Trainium instances for training.

Answer: D,E

Explanation:
SageMaker Debuggercan identify when a training job is not converging or is stuck in a non-productive state.
By stopping these jobs early, unnecessary energy and computational resources are conserved, improving sustainability.
AWS Trainiuminstances are purpose-built for ML training and are optimized for energy efficiency and cost- effectiveness. They use less energy per training task compared to general-purpose instances, making them a sustainable choice.

NEW QUESTION # 23
A company is using ML to predict the presence of a specific weed in a farmer's field. The company is using the Amazon SageMaker linear learner built-in algorithm with a value of multiclass_dassifier for the predictorjype hyperparameter.
What should the company do to MINIMIZE false positives?

A. Change the value of the predictorjype hyperparameter to regressor.
B. Set the value of the weight decay hyperparameter to zero.
C. Increase the number of training epochs.
D. Increase the value of the target_precision hyperparameter.

Answer: D

Explanation:
Thetarget_precisionhyperparameter in the Amazon SageMaker linear learner controls the trade-off between precision and recall for the model. Increasing the target_precision prioritizes minimizing false positives by making the model more cautious in its predictions. This approach is effective for use cases where false positives have higher consequences than false negatives.

NEW QUESTION # 24
An ML engineer is training a simple neural network model. The ML engineer tracks the performance of the model over time on a validation dataset. The model's performance improves substantially at first and then degrades after a specific number of epochs.
Which solutions will mitigate this problem? (Choose two.)

A. Increase the number of neurons.
B. Investigate and reduce the sources of model bias.
C. Enable early stopping on the model.
D. Increase dropout in the layers.
E. Increase the number of layers.

Answer: C,D

Explanation:
Early stopping halts training once the performance on the validation dataset stops improving. This prevents the model from overfitting, which is likely the cause of performance degradation after a certain number of epochs.
Dropout is a regularization technique that randomly deactivates neurons during training, reducing overfitting by forcing the model to generalize better. Increasing dropout can help mitigate the problem of performance degradation due to overfitting.

NEW QUESTION # 25
An ML engineer has an Amazon Comprehend custom model in Account A in the us-east-1 Region. The ML engineer needs to copy the model to Account # in the same Region.
Which solution will meet this requirement with the LEAST development effort?

A. Use Amazon S3 to make a copy of the model. Transfer the copy to Account B.
B. Use AWS DataSync to replicate the model from Account A to Account B.
C. Create a resource-based IAM policy. Use the Amazon Comprehend ImportModel API operation to copy the model to Account B.
D. Create an AWS Site-to-Site VPN connection between Account A and Account # to transfer the model.

Answer: C

Explanation:
Amazon Comprehend provides the ImportModel API operation, which allows you to copy a custom model between AWS accounts. By creating a resource-based IAM policy on the model in Account A, you can grant Account B the necessary permissions to access and import the model. This approach requires minimal development effort and is the AWS-recommended method for sharing custom models across accounts.

NEW QUESTION # 26
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. Create a custom tag for each of the three categories. Add the tags to the model packages in the SageMaker Model Registry.
B. Use SageMaker ML Lineage Tracking to automatically identify and tag which model groups should contain the models.
C. Create a model group for each category. Move the existing models into these category model groups.
D. Create a Model Registry collection for each of the three categories. Move the existing model groups into the collections.

Answer: A

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 # 27
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VALID MLA-C01 EXAM NOTES - MLA-C01 EXAM REVIEW

Valid MLA-C01 Exam Notes - MLA-C01 Exam Review