HELP YOU LEARN STEPS NECESSARY TO PASS THE MLA-C01 EXAM NEW TEST SYLLABUS

Help You Learn Steps Necessary To Pass The MLA-C01 Exam New Test Syllabus

Help You Learn Steps Necessary To Pass The MLA-C01 Exam New Test Syllabus

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q82-Q87):

NEW QUESTION # 82
A company stores historical data in .csv files in Amazon S3. Only some of the rows and columns in the .csv files are populated. The columns are not labeled. An ML engineer needs to prepare and store the data so that the company can use the data to train ML models.
Select and order the correct steps from the following list to perform this task. Each step should be selected one time or not at all. (Select and order three.)
* Create an Amazon SageMaker batch transform job for data cleaning and feature engineering.
* Store the resulting data back in Amazon S3.
* Use Amazon Athena to infer the schemas and available columns.
* Use AWS Glue crawlers to infer the schemas and available columns.
* Use AWS Glue DataBrew for data cleaning and feature engineering.

Answer:

Explanation:

Explanation:
Step 1: Use AWS Glue crawlers to infer the schemas and available columns.Step 2: Use AWS Glue DataBrew for data cleaning and feature engineering.Step 3: Store the resulting data back in Amazon S3.
* Step 1: Use AWS Glue Crawlers to Infer Schemas and Available Columns
* Why?The data is stored in .csv files with unlabeled columns, and Glue Crawlers can scan the raw data in Amazon S3 to automatically infer the schema, including available columns, data types, and any missing or incomplete entries.
* How?Configure AWS Glue Crawlers to point to the S3 bucket containing the .csv files, and run the crawler to extract metadata. The crawler creates a schema in the AWS Glue Data Catalog, which can then be used for subsequent transformations.
* Step 2: Use AWS Glue DataBrew for Data Cleaning and Feature Engineering
* Why?Glue DataBrew is a visual data preparation tool that allows for comprehensive cleaning and transformation of data. It supports imputation of missing values, renaming columns, feature engineering, and more without requiring extensive coding.
* How?Use Glue DataBrew to connect to the inferred schema from Step 1 and perform data cleaning and feature engineering tasks like filling in missing rows/columns, renaming unlabeled columns, and creating derived features.
* Step 3: Store the Resulting Data Back in Amazon S3
* Why?After cleaning and preparing the data, it needs to be saved back to Amazon S3 so that it can be used for training machine learning models.
* How?Configure Glue DataBrew to export the cleaned data to a specific S3 bucket location. This ensures the processed data is readily accessible for ML workflows.
Order Summary:
* Use AWS Glue crawlers to infer schemas and available columns.
* Use AWS Glue DataBrew for data cleaning and feature engineering.
* Store the resulting data back in Amazon S3.
This workflow ensures that the data is prepared efficiently for ML model training while leveraging AWS services for automation and scalability.


NEW QUESTION # 83
An ML engineer trained an ML model on Amazon SageMaker to detect automobile accidents from dosed- circuit TV footage. The ML engineer used SageMaker Data Wrangler to create a training dataset of images of accidents and non-accidents.
The model performed well during training and validation. However, the model is underperforming in production because of variations in the quality of the images from various cameras.
Which solution will improve the model's accuracy in the LEAST amount of time?

  • A. Recreate the training dataset by using the Data Wrangler resize image transform. Crop all images to the same size.
  • B. Recreate the training dataset by using the Data Wrangler corrupt image transform. Specify the impulse noise option.
  • C. Recreate the training dataset by using the Data Wrangler enhance image contrast transform. Specify the Gamma contrast option.
  • D. Collect more images from all the cameras. Use Data Wrangler to prepare a new training dataset.

Answer: B

Explanation:
The model is underperforming in production due to variations in image quality from different cameras. Using the corrupt image transform with the impulse noise option in SageMaker Data Wrangler simulates real-world noise and variations in the training dataset. This approach helps the model become more robust to inconsistencies in image quality, improving its accuracy in production without the need to collect and process new data, thereby saving time.


NEW QUESTION # 84
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.
Before the ML engineer trains the model, the ML engineer must resolve the issue of the imbalanced data.
Which solution will meet this requirement with the LEAST operational effort?

  • A. Use Amazon SageMaker Studio Classic built-in algorithms to process the imbalanced dataset.
  • B. Use AWS Glue DataBrew built-in features to oversample the minority class.
  • C. Use the Amazon SageMaker Data Wrangler balance data operation to oversample the minority class.
  • D. Use Amazon Athena to identify patterns that contribute to the imbalance. Adjust the dataset accordingly.

Answer: C

Explanation:
Problem Description:
* The training dataset has a class imbalance, meaning one class (e.g., fraudulent transactions) has fewer samples compared to the majority class (e.g., non-fraudulent transactions). This imbalance affects the model's ability to learn patterns from the minority class.
Why SageMaker Data Wrangler?
* SageMaker Data Wrangler provides a built-in operation called "Balance Data," which includes oversampling and undersampling techniques to address class imbalances.
* Oversampling the minority class replicates samples of the minority class, ensuring the algorithm receives balanced inputs without significant additional operational overhead.
Steps to Implement:
* Import the dataset into SageMaker Data Wrangler.
* Apply the "Balance Data" operation and configure it to oversample the minority class.
* Export the balanced dataset for training.
Advantages:
* Ease of Use: Minimal configuration is required.
* Integrated Workflow: Works seamlessly with the SageMaker ecosystem for preprocessing and model training.
* Time Efficiency: Reduces manual effort compared to external tools or scripts.


NEW QUESTION # 85
A company runs an Amazon SageMaker domain in a public subnet of a newly created VPC. The network is configured properly, and ML engineers can access the SageMaker domain.
Recently, the company discovered suspicious traffic to the domain from a specific IP address. The company needs to block traffic from the specific IP address.
Which update to the network configuration will meet this requirement?

  • A. Create a security group inbound rule to deny traffic from the specific IP address. Assign the security group to the domain.
  • B. Create a network ACL inbound rule to deny traffic from the specific IP address. Assign the rule to the default network Ad for the subnet where the domain is located.
  • C. Create a VPC route table to deny inbound traffic from the specific IP address. Assign the route table to the domain.
  • D. Create a shadow variant for the domain. Configure SageMaker Inference Recommender to send traffic from the specific IP address to the shadow endpoint.

Answer: B

Explanation:
Network ACLs (Access Control Lists) operate at the subnet level and allow for rules to explicitly deny traffic from specific IP addresses. By creating an inbound rule in the network ACL to deny traffic from the suspicious IP address, the company can block traffic to the Amazon SageMaker domain from that IP. This approach works because network ACLs are evaluated before traffic reaches the security groups, making them effective for blocking traffic at the subnet level.


NEW QUESTION # 86
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 a resource-based IAM policy. Use the Amazon Comprehend ImportModel API operation to copy the model to Account B.
  • B. Create an AWS Site-to-Site VPN connection between Account A and Account # to transfer the model.
  • C. Use Amazon S3 to make a copy of the model. Transfer the copy to Account B.
  • D. Use AWS DataSync to replicate the model from Account A to Account B.

Answer: A

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 # 87
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In promiscuous mode, the sensor receives a copy of the data for analysis, while MLA-C01 the original traffic still makes its way to its ultimate destination, Otherwise, do this on the computer that you're enabling for Remote Desktop access.

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