Free Amazon MLA-C01 Exam Dumps Questions & Answers

A company runs an Amazon SageMaker AI domain in a public subnet of a newly created VPC. The network is configured properly, and ML engineers can access the SageMaker AI 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 shadow variant for the domain. Configure SageMaker Inference Recommender to send traffic from the specific IP address to the shadow endpoint.
• B.  Create a security group inbound rule to deny traffic from the specific IP address. Assign the security group to the domain.
• C.  Create a network ACL inbound rule to deny traffic from the specific IP address. Assign the rule to the default network ACL for the subnet where the domain is located.
• D.  Create a VPC route table to deny inbound traffic from the specific IP address. Assign the route table to the domain.

An ML engineer is working on an ML model to predict the prices of similarly sized homes. The model will base predictions on several features The ML engineer will use the following feature engineering techniques to estimate the prices of the homes:
* Feature splitting
* Logarithmic transformation
* One-hot encoding
* Standardized distribution
Select the correct feature engineering techniques for the following list of features. Each feature engineering technique should be selected one time or not at all (Select three.)

Correct Answer:

Explanation:
City (name): One-hot encoding
Type_year (type of home and year the home was built): Feature splitting Size of the building (square feet or square meters): Standardized distribution City (name): One-hot encoding Why? The " City " is a categorical feature (non-numeric), so one-hot encoding is used to transform it into a numeric format. This encoding creates binary columns for each unique category (e.g., cities like " New York " or " Los Angeles " ), which the model can interpret.
Type_year (type of home and year the home was built): Feature splitting Why? " Type_year " combines two pieces of information into one column, which could confuse the model.
Feature splitting separates this column into two distinct features: " Type of home " and " Year built, " enabling the model to process each feature independently.
Size of the building (square feet or square meters): Standardized distribution Why? Size is a continuous numerical variable, and standardization (scaling the feature to have a mean of 0 and a standard deviation of 1) ensures that the model treats it fairly compared to other features, avoiding bias from differences in feature scale.
By applying these feature engineering techniques, the ML engineer can ensure that the input data is correctly formatted and optimized for the model to make accurate predictions.

A company is running ML models on premises by using custom Python scripts and proprietary datasets. The company is using PyTorch. The model building requires unique domain knowledge. The company needs to move the models to AWS.
Which solution will meet these requirements with the LEAST development effort?

• A.  Build a container on AWS that includes custom packages and a choice of ML frameworks.
• B.  Use SageMaker AI built-in algorithms to train the proprietary datasets.
• C.  Use SageMaker AI script mode and premade images for ML frameworks.
• D.  Purchase similar production models through AWS Marketplace.

A company has multiple models that are hosted on Amazon SageMaker Al. The models need to be re-trained.
The requirements for each model are different, so the company needs to choose different deployment strategies to transfer all requests to a new model.
Select the correct strategy from the following list for each requirement. Select each strategy one time. (Select THREE.)
. Canary traffic shifting
. Linear traffic shifting guardrail
. All at once traffic shifting

Correct Answer:

Explanation:
1## Simultaneous calls to the endpoint must reach models with the same configuration Correct strategy: All at once traffic shifting Why:
"All at once" replaces the old model with the new model immediately. After the switch, all concurrent requests hit only the new model configuration, guaranteeing configuration consistency across simultaneous calls.
2## The new model must receive only a fraction of the requests for validation before receiving all the traffic Correct strategy: Canary traffic shifting Why:
Canary deployments route a small percentage of traffic (for example, 5% or 10%) to the new model first. This allows validation of correctness and performance before shifting 100% of traffic.
3## Traffic to the new model must increase gradually to ensure that pipelines that rely on the endpoint do not fail because of changes in latency Correct strategy: Linear traffic shifting guardrail Why:
Linear traffic shifting gradually increases traffic in equal increments over time and includes guardrails (such as CloudWatch alarms) to automatically roll back if latency or errors exceed thresholds.

A company is using Amazon SageMaker AI to develop a credit risk assessment model. During model validation, the company finds that the model achieves 82% accuracy on the validation data. However, the model achieved 99% accuracy on the training data. The company needs to address the model accuracy issue before deployment.
Which solution will meet this requirement?

• A.  Augment the training dataset. Remove duplicate records from the training dataset. Implement stratified sampling.
• B.  Add more dense layers to increase model complexity. Implement batch normalization. Use early stopping during training.
• C.  Use principal component analysis (PCA) to reduce the feature dimensionality. Decrease model layers.
  Implement cross-entropy loss functions.
• D.  Implement dropout layers. Use L1 or L2 regularization. Perform k-fold cross-validation.