MLA-C01 PDF FILES - MLA-C01 LATEST EXAM EXPERIENCE

MLA-C01 Pdf Files - MLA-C01 Latest Exam Experience

MLA-C01 Pdf Files - MLA-C01 Latest Exam Experience

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q41-Q46):

NEW QUESTION # 41
A company is gathering audio, video, and text data in various languages. The company needs to use a large language model (LLM) to summarize the gathered data that is in Spanish.
Which solution will meet these requirements in the LEAST amount of time?

  • A. Use Amazon Rekognition and Amazon Translate to convert the data into English text. Use Amazon Bedrock with the Anthropic Claude model to summarize the text.
  • B. Use Amazon Comprehend and Amazon Translate to convert the data into English text. Use Amazon Bedrock with the Stable Diffusion model to summarize the text.
  • C. Use Amazon Transcribe and Amazon Translate to convert the data into English text. Use Amazon Bedrock with the Jurassic model to summarize the text.
  • D. Train and deploy a model in Amazon SageMaker to convert the data into English text. Train and deploy an LLM in SageMaker to summarize the text.

Answer: C

Explanation:
Amazon Transcribeis well-suited for converting audio data into text, including Spanish.
Amazon Translatecan efficiently translate Spanish text into English if needed.
Amazon Bedrock, with theJurassic model, is designed for tasks like text summarization and can handle large language models (LLMs) seamlessly. This combination provides a low-code, managed solution to process audio, video, and text data with minimal time and effort.


NEW QUESTION # 42
A company has a team of data scientists who use Amazon SageMaker notebook instances to test ML models.
When the data scientists need new permissions, the company attaches the permissions to each individual role that was created during the creation of the SageMaker notebook instance.
The company needs to centralize management of the team's permissions.
Which solution will meet this requirement?

  • A. Create a single IAM group. Add the data scientists to the group. Associate the group with each notebook instance that the team uses.
  • B. Create a single IAM group. Add the data scientists to the group. Create an IAM role. Attach the AdministratorAccess AWS managed IAM policy to the role. Associate the role with the group.Associate the group with each notebook instance that the team uses.
  • C. Create a single IAM role that has the necessary permissions. Attach the role to each notebook instance that the team uses.
  • D. Create a single IAM user. Attach the AdministratorAccess AWS managed IAM policy to the user.
    Configure each notebook instance to use the IAM user.

Answer: C

Explanation:
Managing permissions for multiple Amazon SageMaker notebook instances can become complex when handled individually. To centralize and streamline permission management, AWS recommends creating a single IAM role with the necessary permissions and attaching this role to each notebook instance used by the data science team.
Steps to Implement the Solution:
* Create a Single IAM Role with Necessary Permissions:
* Define an IAM role that encompasses all permissions required by the data scientists for their tasks. This includes permissions for SageMaker operations and any other AWS services they interact with.
* AWS provides managed policies like AmazonSageMakerFullAccess that can be attached to the role to grant comprehensive SageMaker permissions.(IAM Policies for SageMaker)
* Attach the IAM Role to Each Notebook Instance:
* When creating or updating a SageMaker notebook instance, specify the IAM role created in the previous step. This ensures that all notebook instances operate under a consistent set of permissions.
* In the SageMaker console, during the notebook instance setup, you can choose an existing IAM role to associate with the instance.(Creating SageMaker Workspaces) Benefits of This Approach:
* Centralized Permission Management:By using a single IAM role, you simplify the process of updating permissions. Changes to the role's policies automatically propagate to all associated notebook instances, ensuring consistent access control.
* Adherence to Best Practices:AWS recommends using IAM roles to manage permissions for applications running on services like SageMaker. This approach avoids the need to manage individual user permissions separately.(IAM Best Practices for SageMaker) Alternative Options and Their Drawbacks:
* Option B:Creating a single IAM group and adding data scientists to it does not directly associate the group with notebook instances. IAM groups are used to manage user permissions, not to assign roles to AWS resources like notebook instances.
* Option C:Using a single IAM user with the AdministratorAccess policy is not recommended due to security risks associated with granting broad permissions and the challenges in managing shared user credentials.
* Option D:Associating an IAM group with a role and then with notebook instances is not a valid approach, as IAM groups cannot be directly associated with AWS resources.
Conclusion:Option A is the most effective solution to centralize and manage permissions for SageMaker notebook instances, aligning with AWS best practices for IAM role management.
References:
* AWS Documentation: IAM Policies for SageMaker
* AWS Documentation: Creating SageMaker Workspaces
* AWS Documentation: IAM Best Practices for SageMaker


NEW QUESTION # 43
An ML engineer is developing a fraud detection model by using the Amazon SageMaker XGBoost algorithm.
The model classifies transactions as either fraudulent or legitimate.
During testing, the model excels at identifying fraud in the training dataset. However, the model is inefficient at identifying fraud in new and unseen transactions.
What should the ML engineer do to improve the fraud detection for new transactions?

  • A. Increase the learning rate.
  • B. Remove some irrelevant features from the training dataset.
  • C. Increase the value of the max_depth hyperparameter.
  • D. Decrease the value of the max_depth hyperparameter.

Answer: D

Explanation:
A high max_depth value in XGBoost can lead to overfitting, where the model learns the training dataset too well but fails to generalize to new and unseen data. By decreasing the max_depth, the model becomes less complex, reducing overfitting and improving its ability to detect fraud in new transactions. This adjustment helps the model focus on general patterns rather than memorizing specific details in the training data.


NEW QUESTION # 44
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 categorical data into numerical data.
  • C. Use Amazon SageMaker Data Wrangler to transform the numerical data into categorical data.
  • D. Use AWS Glue 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 # 45
A company is using Amazon SageMaker and millions of files to train an ML model. Each file is several megabytes in size. The files are stored in an Amazon S3 bucket. The company needs to improve training performance.
Which solution will meet these requirements in the LEAST amount of time?

  • A. Create an Amazon Elastic File System (Amazon EFS) file system. Transfer the existing data to the file system. Adjust the training job to read from the file system.
  • B. Transfer the data to a new S3 bucket that provides S3 Express One Zone storage. Adjust the training job to use the new S3 bucket.
  • C. Create an Amazon ElastiCache (Redis OSS) cluster. Link the Redis OSS cluster to the existing S3 bucket. Stream the data from the Redis OSS cluster directly to the training job.
  • D. Create an Amazon FSx for Lustre file system. Link the file system to the existing S3 bucket. Adjust the training job to read from the file system.

Answer: D

Explanation:
Amazon FSx for Lustre is designed for high-performance workloads like ML training. It provides fast, low- latency access to data by linking directly to the existing S3 bucket and caching frequently accessed files locally. This significantly improves training performance compared to directly accessing millions of files from S3. It requires minimal changes to the training job and avoids the overhead of transferring or restructuring data, making it the fastest and most efficient solution.


NEW QUESTION # 46
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