REAL MLA-C01 EXAM DUMPS | VCE MLA-C01 FILES

Real MLA-C01 Exam Dumps | Vce MLA-C01 Files

Real MLA-C01 Exam Dumps | Vce MLA-C01 Files

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q20-Q25):

NEW QUESTION # 20
A company needs to give its ML engineers appropriate access to training data. The ML engineers must access training data from only their own business group. The ML engineers must not be allowed to access training data from other business groups.
The company uses a single AWS account and stores all the training data in Amazon S3 buckets. All ML model training occurs in Amazon SageMaker.
Which solution will provide the ML engineers with the appropriate access?

  • A. Add cross-origin resource sharing (CORS) policies to the S3 buckets.
  • B. Create IAM policies. Attach the policies to IAM users or IAM roles.
  • C. Configure S3 Object Lock settings for each user.
  • D. Enable S3 bucket versioning.

Answer: B

Explanation:
By creating IAM policies with specific permissions, you can restrict access to Amazon S3 buckets or objects based on the user's business group. These policies can be attached to IAM users or IAM roles associated with the ML engineers, ensuring that each engineer can only access training data belonging to their group. This approach is secure, scalable, and aligns with AWS best practices for access control.


NEW QUESTION # 21
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. Linear regression
  • B. Anomaly detection
  • C. Semantic segmentation
  • D. Logistic regression

Answer: D

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 # 22
An ML engineer needs to process thousands of existing CSV objects and new CSV objects that are uploaded.
The CSV objects are stored in a central Amazon S3 bucket and have the same number of columns. One of the columns is a transaction date. The ML engineer must query the data based on the transaction date.
Which solution will meet these requirements with the LEAST operational overhead?

  • A. Create a new S3 bucket for processed data. Set up S3 replication from the central S3 bucket to the new S3 bucket. Use S3 Object Lambda to query the objects based on transaction date.
  • B. Create a new S3 bucket for processed data. Use Amazon Data Firehose to transfer the data from the central S3 bucket to the new S3 bucket. Configure Firehose to run an AWS Lambda function to query the data based on transaction date.
  • C. Create a new S3 bucket for processed data. Use AWS Glue for Apache Spark to create a job to query the CSV objects based on transaction date. Configure the job to store the results in the new S3 bucket.
    Query the objects from the new S3 bucket.
  • D. Use an Amazon Athena CREATE TABLE AS SELECT (CTAS) statement to create a table based on the transaction date from data in the central S3 bucket. Query the objects from the table.

Answer: D

Explanation:
Scenario:The ML engineer needs a low-overhead solution to query thousands of existing and new CSV objects stored in Amazon S3 based on a transaction date.
Why Athena?
* Serverless:Amazon Athena is a serverless query service that allows direct querying of data stored in S3 using standard SQL, reducing operational overhead.
* Ease of Use:By using the CTAS statement, the engineer can create a table with optimized partitions based on the transaction date. Partitioning improves query performance and minimizes costs by scanning only relevant data.
* Low Operational Overhead:No need to manage or provision additional infrastructure. Athena integrates seamlessly with S3, and CTAS simplifies table creation and optimization.
Steps to Implement:
* Organize Data in S3:Store CSV files in a bucket in a consistent format and directory structure if possible.
* Configure Athena:Use the AWS Management Console or Athena CLI to set up Athena to point to the S3 bucket.
* Run CTAS Statement:
CREATE TABLE processed_data
WITH (
format = 'PARQUET',
external_location = 's3://processed-bucket/',
partitioned_by = ARRAY['transaction_date']
) AS
SELECT *
FROM input_data;
This creates a new table with data partitioned by transaction date.
* Query the Data:Use standard SQL queries to fetch data based on the transaction date.
References:
* Amazon Athena CTAS Documentation
* Partitioning Data in Athena


NEW QUESTION # 23
Case Study
A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.
The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.
The company must implement a manual approval-based workflow to ensure that only approved models can be deployed to production endpoints.
Which solution will meet this requirement?

  • A. Use SageMaker ML Lineage Tracking on the central model registry. Create tracking entities for the approval process.
  • B. Use SageMaker Model Monitor to evaluate the performance of the model and to manage the approval.
  • C. Use SageMaker Pipelines. When a model version is registered, use the AWS SDK to change the approval status to "Approved."
  • D. Use SageMaker Experiments to facilitate the approval process during model registration.

Answer: C

Explanation:
To implement a manual approval-based workflow ensuring that only approved models are deployed to production endpoints, Amazon SageMaker provides integrated tools such asSageMaker Pipelinesand the SageMaker Model Registry.
SageMaker Pipelinesis a robust service for building, automating, and managing end-to-end machine learning workflows. It facilitates the orchestration of various steps in the ML lifecycle, including data preprocessing, model training, evaluation, and deployment. By integrating with theSageMaker Model Registry, it enables seamless tracking and management of model versions and their approval statuses.
Implementation Steps:
* Define the Pipeline:
* Create a SageMaker Pipeline encompassing steps for data preprocessing, model training, evaluation, and registration of the model in the Model Registry.
* Incorporate aCondition Stepto assess model performance metrics. If the model meets predefined criteria, proceed to the next step; otherwise, halt the process.
* Register the Model:
* Utilize theRegisterModelstep to add the trained model to the Model Registry.
* Set the ModelApprovalStatus parameter to PendingManualApproval during registration. This status indicates that the model awaits manual review before deployment.
* Manual Approval Process:
* Notify the designated approver upon model registration. This can be achieved by integrating Amazon EventBridge to monitor registration events and trigger notifications via AWS Lambda functions.
* The approver reviews the model's performance and, if satisfactory, updates the model's status to Approved using the AWS SDK or through the SageMaker Studio interface.
* Deploy the Approved Model:
* Configure the pipeline to automatically deploy models with an Approved status to the production endpoint. This can be managed by adding deployment steps conditioned on the model's approval status.
Advantages of This Approach:
* Automated Workflow:SageMaker Pipelines streamline the ML workflow, reducing manual interventions and potential errors.
* Governance and Compliance:The manual approval step ensures that only thoroughly evaluated models are deployed, aligning with organizational standards.
* Scalability:The solution supports complex ML workflows, making it adaptable to various project requirements.
By implementing this solution, the company can establish a controlled and efficient process for deploying models, ensuring that only approved versions reach production environments.
References:
* Automate the machine learning model approval process with Amazon SageMaker Model Registry and Amazon SageMaker Pipelines
* Update the Approval Status of a Model - Amazon SageMaker


NEW QUESTION # 24
A company has a large collection of chat recordings from customer interactions after a product release. An ML engineer needs to create an ML model to analyze the chat data. The ML engineer needs to determine the success of the product by reviewing customer sentiments about the product.
Which action should the ML engineer take to complete the evaluation in the LEAST amount of time?

  • A. Use Amazon Comprehend to analyze sentiments of the chat conversations.
  • B. Use random forests to classify sentiments of the chat conversations.
  • C. Use Amazon Rekognition to analyze sentiments of the chat conversations.
  • D. Train a Naive Bayes classifier to analyze sentiments of the chat conversations.

Answer: A

Explanation:
Amazon Comprehend is a fully managed natural language processing (NLP) service that includes a built-in sentiment analysis feature. It can quickly and efficiently analyze text data to determine whether the sentiment is positive, negative, neutral, or mixed. Using Amazon Comprehend requires minimal setup and provides accurate results without the need to train and deploy custom models, making it the fastest and most efficient solution for this task.


NEW QUESTION # 25
......

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