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[Hardware] MLA-C01 Free Dump Download & MLA-C01 Test Cram Pdf

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【Hardware】 MLA-C01 Free Dump Download & MLA-C01 Test Cram Pdf

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Amazon MLA-C01 Exam Syllabus Topics:
TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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 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.

Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q36-Q41):NEW QUESTION # 36
A company is building a deep learning model on Amazon SageMaker. The company uses a large amount of data as the training dataset. The company needs to optimize the model's hyperparameters to minimize the loss function on the validation dataset.
Which hyperparameter tuning strategy will accomplish this goal with the LEAST computation time?
  • A. Grid search
  • B. Bayesian optimization
  • C. Random search
  • D. Hyperbaric!
Answer: D
Explanation:
Hyperband is a hyperparameter tuning strategy designed to minimize computation time by adaptively allocating resources to promising configurations and terminating underperforming ones early. It efficiently balances exploration and exploitation, making it ideal for large datasets and deep learning models where training can be computationally expensive.

NEW QUESTION # 37
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. Increase the number of training epochs.
  • B. Increase the value of the target_precision hyperparameter.
  • C. Set the value of the weight decay hyperparameter to zero.
  • D. Change the value of the predictorjype hyperparameter to regressor.
Answer: B

NEW QUESTION # 38
A medical company ingests streams of data from devices that monitor patients' vital signs. The company uses Amazon SageMaker and plans to prepare ML models to predict adverse events for patients. The dataset is large with thousands of features.
An ML engineer needs to run several hundred training iterations with different sets of features, different algorithms, and many potential parameters. The ML engineer must implement a solution to log the characteristics and results of each training iteration.
Which solution will meet these requirements with the LEAST implementation effort?
  • A. Use Amazon CloudWatch to create custom metrics for the characteristics of each iteration.
  • B. Use the SageMaker Model Registry to track the characteristics and results of each iteration.
  • C. Use SageMaker Experiments to track the characteristics and results of each iteration.
  • D. Write the characteristics of each iteration to logs in Amazon S3. Use AWS Glue and Amazon Athena to search the logs.
Answer: C
Explanation:
SageMaker Experiments is specifically designed to track and organize ML experiments, including characteristics such as features, algorithms, parameters, and results. It provides experiment tracking with minimal implementation effort, making it the best fit for logging and comparing multiple training iterations.

NEW QUESTION # 39
An ML engineer is evaluating several ML models and must choose one model to use in production. The cost of false negative predictions by the models is much higher than the cost of false positive predictions.
Which metric finding should the ML engineer prioritize the MOST when choosing the model?
  • A. Low precision
  • B. Low recall
  • C. High precision
  • D. High recall
Answer: D

NEW QUESTION # 40
A company is using Amazon SageMaker to create ML models. The company's data scientists need fine- grained control of the ML workflows that they orchestrate. The data scientists also need the ability to visualize SageMaker jobs and workflows as a directed acyclic graph (DAG). The data scientists must keep a running history of model discovery experiments and must establish model governance for auditing and compliance verifications.
Which solution will meet these requirements?
  • A. Use SageMaker Pipelines and its integration with SageMaker Studio to manage the entire ML workflows. Use SageMaker ML Lineage Tracking for the running history of experiments and for auditing and compliance verifications.
  • B. Use SageMaker Pipelines and its integration with SageMaker Experiments to manage the entire ML workflows. Use SageMaker Experiments for the running history of experiments and for auditing and compliance verifications.
  • C. Use AWS CodePipeline and its integration with SageMaker Studio to manage the entire ML workflows. Use SageMaker ML Lineage Tracking for the running history of experiments and for auditing and compliance verifications.
  • D. Use AWS CodePipeline and its integration with SageMaker Experiments to manage the entire ML workflows. Use SageMaker Experiments for the running history of experiments and for auditing and compliance verifications.
Answer: A
Explanation:
SageMaker Pipelines provides a directed acyclic graph (DAG) view for managing and visualizing ML workflows with fine-grained control. It integrates seamlessly with SageMaker Studio, offering an intuitive interface for workflow orchestration.
SageMaker ML Lineage Tracking keeps a running history of experiments and tracks the lineage of datasets, models, and training jobs. This feature supports model governance, auditing, and compliance verification requirements.

NEW QUESTION # 41
......
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