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[General] MLA-C01 Reliable Test Sample & MLA-C01 Customizable Exam Mode

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【General】 MLA-C01 Reliable Test Sample & MLA-C01 Customizable Exam Mode

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Amazon MLA-C01 Exam Syllabus Topics:
TopicDetails
Topic 1
  • 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.
Topic 2
  • 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 3
  • 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 4
  • 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.

Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q134-Q139):NEW QUESTION # 134
A company needs to use Amazon SageMaker to train a model on more than 300 GB of data. The training data is composed of files that are 200 MB in size. The data is stored in Amazon S3 Standard storage and feeds a dashboard tool. Which SageMaker training ingestion mechanism is the MOST cost-effective solution for this scenario?
  • A. Amazon FSx for Lustre file system
  • B. Amazon S3 in fast file mode while using S3 Express One Zone
  • C. Amazon S3 in fast file mode without using S3 Express One Zone
  • D. Amazon Elastic File System (Amazon EFS) file system
Answer: C
Explanation:
For large-scale training data already stored in Amazon S3, the most cost-effective solution is to use SageMaker's S3 fast file mode without S3 Express One Zone. Fast file mode enables streaming directly from S3 without duplicating the dataset onto local storage, reducing startup time and storage cost. Using S3 Express One Zone would increase cost, so standard fast file mode is the most economical choice.

NEW QUESTION # 135
A company has developed a new ML model. The company requires online model validation on 10% of the traffic before the company fully releases the model in production. The company uses an Amazon SageMaker endpoint behind an Application Load Balancer (ALB) to serve the model.
Which solution will set up the required online validation with the LEAST operational overhead?
  • A. Create a new SageMaker endpoint. Use production variants to add the new model to the new endpoint.
    Monitor the number of invocations by using Amazon CloudWatch.
  • B. Configure the ALB to route 10% of the traffic to the new model at the existing SageMaker endpoint.Monitor the number of invocations by using AWS CloudTrail.
  • C. Use production variants to add the new model to the existing SageMaker endpoint. Set the variant weight to 1 for the new model. Monitor the number of invocations by using Amazon CloudWatch.
  • D. Use production variants to add the new model to the existing SageMaker endpoint. Set the variant weight to 0.1 for the new model. Monitor the number of invocations by using Amazon CloudWatch.
Answer: D
Explanation:
Scenario:The company wants to perform online validation of a new ML model on 10% of the traffic before fully deploying the model in production. The setup must have minimal operational overhead.
Why Use SageMaker Production Variants?
* Built-In Traffic Splitting:Amazon SageMaker endpoints support production variants, allowing multiple models to run on a single endpoint. You can direct a percentage of incoming traffic to each variant by adjusting the variant weights.
* Ease of Management:Using production variants eliminates the need for additional infrastructure like separate endpoints or custom ALB configurations.
* Monitoring with CloudWatch:SageMaker automatically integrates with CloudWatch, enabling real- time monitoring of model performance and invocation metrics.
Steps to Implement:
* Deploy the New Model as a Production Variant:
* Update the existing SageMaker endpoint to include the new model as a production variant. This can be done via the SageMaker console, CLI, or SDK.
Example SDK Code:
import boto3
sm_client = boto3.client('sagemaker')
response = sm_client.update_endpoint_weights_and_capacities(
EndpointName='existing-endpoint-name',
DesiredWeightsAndCapacities=[
{'VariantName': 'current-model', 'DesiredWeight': 0.9},
{'VariantName': 'new-model', 'DesiredWeight': 0.1}
]
)
* Set the Variant Weight:
* Assign a weight of 0.1 to the new model and 0.9 to the existing model. This ensures 10% of traffic goes to the new model while the remaining 90% continues to use the current model.
* Monitor the Performance:
* Use Amazon CloudWatch metrics, such as InvocationCount and ModelLatency, to monitor the traffic and performance of each variant.
* Validate the Results:
* Analyze the performance of the new model based on metrics like accuracy, latency, and failure rates.
Why Not the Other Options?
* Option B:Setting the weight to 1 directs all traffic to the new model, which does not meet the requirement of splitting traffic for validation.
* Option C:Creating a new endpoint introduces additional operational overhead for traffic routing and monitoring, which is unnecessary given SageMaker's built-in production variant capability.
* Option D:Configuring the ALB to route traffic requires manual setup and lacks SageMaker's seamless variant monitoring and traffic splitting features.
Conclusion:Using production variants with a weight of 0.1 for the new model on the existing SageMaker endpoint provides the required traffic split for online validation with minimal operational overhead.
References:
* Amazon SageMaker Endpoints
* SageMaker Production Variants
* Monitoring SageMaker Endpoints with CloudWatch

NEW QUESTION # 136
A company wants to predict the success of advertising campaigns by considering the color scheme of each advertisement. An ML engineer is preparing data for a neural network model. The dataset includes color information as categorical data.
Which technique for feature engineering should the ML engineer use for the model?
  • A. One-hot encode the color categories to transform the color scheme feature into a binary matrix.
  • B. Perform dimensionality reduction on the color categories.
  • C. Apply label encoding to the color categories. Automatically assign each color a unique integer.
  • D. Implement padding to ensure that all color feature vectors have the same length.
Answer: A
Explanation:
One-hot encodingis the appropriate technique for transforming categorical data, such as color information, into a format suitable for input to a neural network. This technique creates a binary vector representation where each unique category (color) is represented as a separate binary column, ensuring that the model does not infer ordinal relationships between categories. This approach preserves the categorical nature of the data and avoids introducing unintended biases.

NEW QUESTION # 137
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

NEW QUESTION # 138
A company is planning to use Amazon SageMaker to make classification ratings that are based on images. The company has 6 GB of training data that is stored on an Amazon FSx for NetApp ONTAP system virtual machine (SVM). The SVM is in the same VPC as SageMaker.
An ML engineer must make the training data accessible for ML models that are in the SageMaker environment.
Which solution will meet these requirements?
  • A. Create an Amazon S3 bucket. Use Mountpoint for Amazon S3 to link the S3 bucket to the FSx for ONTAP file system.
  • B. Create a catalog connection from SageMaker Data Wrangler to the FSx for ONTAP file system.
  • C. Create a direct connection from SageMaker Data Wrangler to the FSx for ONTAP file system.
  • D. Mount the FSx for ONTAP file system as a volume to the SageMaker Instance.
Answer: D

NEW QUESTION # 139
......
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