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[Hardware] Valid MLA-C01 Mock Test - Question MLA-C01 Explanations

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【Hardware】 Valid MLA-C01 Mock Test - Question MLA-C01 Explanations

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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
  • 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 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.

Free download Amazon certification MLA-C01 exam questions and answersThe prominent benefits of AWS Certified Machine Learning Engineer - Associate certification exam are validation of skills, updated knowledge, more career opportunities, instant rise in salary, and advancement of the career. Obviously, every serious professional wants to gain all these advantages. With the Amazon MLA-C01 Certification Exam, you can achieve this goal nicely and quickly.
Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q128-Q133):NEW QUESTION # 128
A company has an ML model that uses historical transaction data to predict customer behavior.
An ML engineer is optimizing the model in Amazon SageMaker to enhance the model's predictive accuracy. The ML engineer must examine the input data and the resulting predictions to identify trends that could skew the model's performance across different demographics.
Which solution will provide this level of analysis?
  • A. Use SageMaker Clarify to evaluate the model and training data for underlying patterns that might affect accuracy.
  • B. Create AWS Glue DataBrew recipes to correct the data based on statistics from the model output.
  • C. Create AWS Lambda functions to automate data pre-processing and to ensure consistent quality of input data for the model.
  • D. Use Amazon CloudWatch to monitor network metrics and CPU metrics for resource optimization during model training.
Answer: A

NEW QUESTION # 129
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.
The training dataset includes categorical data and numerical data. The ML engineer must prepare the training dataset to maximize the accuracy of the model.
Which action will meet this requirement with the LEAST operational overhead?
  • A. Use Amazon SageMaker Data Wrangler to transform the categorical data into numerical data.
  • B. Use AWS Glue to transform the numerical data into categorical data.
  • C. Use AWS Glue to transform the categorical data into numerical data.
  • D. Use Amazon SageMaker Data Wrangler to transform the numerical data into categorical data.
Answer: A
Explanation:
Preparing a training dataset that includes both categorical and numerical data is essential for maximizing the accuracy of a machine learning model. Transforming categorical data into numerical format is a critical step, as most ML algorithms require numerical input.
Why Transform Categorical Data into Numerical Data?
* Model Compatibility: Many ML algorithms cannot process categorical data directly and require numerical representations.
* Improved Performance: Proper encoding of categorical variables can enhance model accuracy and convergence speed.
Why Use Amazon SageMaker Data Wrangler?
Amazon SageMaker Data Wrangler offers a visual interface with over 300 built-in data transformations, including tools for encoding categorical variables.
Implementation Steps:
* Import Data:
* Load the dataset into SageMaker Data Wrangler from sources like Amazon S3 or on-premises databases.
* Identify Categorical Features:
* Use Data Wrangler's data type inference to detect categorical columns.
* Apply Categorical Encoding:
* Choose appropriate encoding techniques (e.g., one-hot encoding or ordinal encoding) from Data Wrangler's transformation options.
* Apply the selected transformation to convert categorical features into numerical format.
* Validate Transformations:
* Review the transformed dataset to ensure accuracy and completeness.
Advantages of Using SageMaker Data Wrangler:
* Ease of Use: Provides a user-friendly interface for data transformation without extensive coding.
* Operational Efficiency: Integrates data preparation steps, reducing the need for multiple tools and minimizing operational overhead.
* Flexibility: Supports various data sources and transformation techniques, accommodating diverse datasets.
By utilizing SageMaker Data Wrangler to transform categorical data into numerical format, the ML engineer can efficiently prepare the dataset, thereby enhancing the model's accuracy with minimal operational overhead.
References:
* Transform Data - Amazon SageMaker
* Prepare ML Data with Amazon SageMaker Data Wrangler

NEW QUESTION # 130
An ML engineer needs to deploy ML models to get inferences from large datasets in an asynchronous manner. The ML engineer also needs to implement scheduled monitoring of the data quality of the models. The ML engineer must receive alerts when changes in data quality occur.
Which solution will meet these requirements?
  • A. Deploy the models by using scheduled AWS Batch jobs. Use AWS CloudTrail to monitor the data quality and to send alerts.
  • B. Deploy the models by using Amazon SageMaker batch transform. Use SageMaker Model Monitor to monitor the data quality and to send alerts.
  • C. Deploy the models by using Amazon Elastic Container Service (Amazon ECS) on AWS Fargate.
    Use Amazon EventBridge to monitor the data quality and to send alerts.
  • D. Deploy the models by using scheduled AWS Glue jobs. Use Amazon CloudWatch alarms to monitor the data quality and to send alerts.
Answer: B

NEW QUESTION # 131
A machine learning engineer is preparing a data frame for a supervised learning task with the Amazon SageMaker Linear Learner algorithm. The ML engineer notices the target label classes are highly imbalanced and multiple feature columns contain missing values. The proportion of missing values across the entire data frame is less than 5%.
What should the ML engineer do to minimize bias due to missing values?
  • A. Delete observations that contain missing values because these represent less than 5% of the data.
  • B. Replace each missing value by the mean or median across non-missing values in same row.
  • C. Replace each missing value by the mean or median across non-missing values in the same column.
  • D. For each feature, approximate the missing values using supervised learning based on other features.
Answer: D
Explanation:
Use supervised learning to predict missing values based on the values of other features. Different supervised learning approaches might have different performances, but any properly implemented supervised learning approach should provide the same or better approximation than mean or median approximation, as proposed in responses A and C. Supervised learning applied to the imputation of missing values is an active field of research.

NEW QUESTION # 132
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 value of the target_precision hyperparameter.
  • D. Increase the number of training epochs.
Answer: C
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 # 133
......
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