MLA-C01 VALID BRAINDUMPS EBOOK & FREE SAMPLE MLA-C01 QUESTIONS

MLA-C01 Valid Braindumps Ebook & Free Sample MLA-C01 Questions

MLA-C01 Valid Braindumps Ebook & Free Sample MLA-C01 Questions

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Tags: MLA-C01 Valid Braindumps Ebook, Free Sample MLA-C01 Questions, MLA-C01 Real Dumps, MLA-C01 Reliable Practice Materials, Exam MLA-C01 Dumps

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Free Sample Amazon MLA-C01 Questions - MLA-C01 Real Dumps

To practice for a AWS Certified Machine Learning Engineer - Associate in the software (free test), you should perform a self-assessment. The Amazon MLA-C01 practice test software keeps track of each previous attempt and highlights the improvements with each attempt. The Amazon MLA-C01 Mock Exam setup can be configured to a particular style & arrive at unique questions.

Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q50-Q55):

NEW QUESTION # 50
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 numerical data into categorical data.
  • B. Use Amazon SageMaker Data Wrangler to transform the categorical data into numerical data.
  • C. Use AWS Glue to transform the numerical data into categorical data.
  • D. Use AWS Glue to transform the categorical data into numerical data.

Answer: B

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 # 51
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 dropout in the layers.
  • B. Enable early stopping on the model.
  • C. Investigate and reduce the sources of model bias.
  • D. Increase the number of layers.
  • E. Increase the number of neurons.

Answer: A,B

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 # 52
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. Create an AWS Site-to-Site VPN connection between Account A and Account # to transfer the model.
  • 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. Use Amazon S3 to make a copy of the model. Transfer the copy to Account B.

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 # 53
A company has historical data that shows whether customers needed long-term support from company staff.
The company needs to develop an ML model to predict whether new customers will require long-term support.
Which modeling approach should the company use to meet this requirement?

  • A. Semantic segmentation
  • B. Logistic regression
  • C. Linear regression
  • D. Anomaly detection

Answer: B

Explanation:
Logistic regression is a suitable modeling approach for this requirement because it is designed for binary classification problems, such as predicting whether a customer will require long-term support ("yes" or "no").
It calculates the probability of a particular class and is widely used for tasks like this where the outcome is categorical.


NEW QUESTION # 54
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. Use PyTorch or TensorFlow with the distributed training option.
  • C. Deploy models by using AWS Lambda functions.
  • D. Use AWS Trainium instances for training.
  • E. Use Amazon SageMaker Debugger to stop training jobs when non-converging conditions are detected.

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 # 55
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