MLA-C01 Exam Dumps - AWS Certified Machine Learning Engineer - Associate

For cross-account Amazon S3 access, AWS requires two components:

An S3 bucket policy in the owning account (Account A) that grants access to a principal in another account

An IAM role policy in the consuming account (Account B) that allows the service to access the bucket

Amazon SageMaker training jobs assume an IAM role in the account where the job runs—in this case, Account B. Therefore, the IAM policy must be attached to the SageMaker execution role in Account B.

The S3 bucket policy must reside in Account A because bucket policies are owned and enforced by the bucket owner. This policy explicitly allows the IAM role from Account B to read the training data.

Any other combination fails either because the policy is in the wrong account or because the role is not the one used by SageMaker.

AWS documentation clearly describes this pattern as the correct way to grant cross-account access for SageMaker training jobs.

Therefore, Option B is the correct and AWS-aligned solution.

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.

Shadow testing allows you to send a copy of live production traffic to a shadow variant of the new model while keeping the existing production model unaffected. This enables you to evaluate the performance of the new model in real-time with live data without impacting end users. SageMaker endpoints support this setup by allowing traffic mirroring to the shadow variant, making it an ideal solution for assessing the new model's performance.

Amazon FSx for NetApp ONTAP allows mounting the file system as a network-attached storage (NAS) volume. Since the FSx for ONTAP file system and SageMaker instance are in the same VPC, you can directly mount the file system to the SageMaker instance. This approach ensures efficient access to the 6 TB of training data without the need to duplicate or transfer the data, meeting the requirements with minimal complexity and operational overhead.

AWS recommends SageMaker shadow testing as the safest way to validate a new model version using real production traffic without impacting production responses. A shadow variant receives a copy of inference requests but does not return predictions to end users. This completely eliminates the risk of exposing incorrect predictions.

Options A and B are canary deployments. While useful, they still allow v2 to return responses to real users, which violates the requirement to prevent disruption. Option C sends 100% of traffic to v2 externally and is risky.

Shadow variants are explicitly designed to minimize risk while using real data, making them the AWS best practice for pre-production validation.

Therefore, Option D is correct.

This scenario clearly describes an overfitting problem. The neural network performs well on training data but fails to generalize to evaluation data. According to AWS Machine Learning and deep learning best practices, overfitting occurs when a model learns noise or overly specific patterns in the training data instead of generalizable relationships.

L1 and L2 regularization are well-established techniques to combat overfitting. They work by penalizing large weight values during optimization, effectively constraining the model’s complexity. L1 regularization encourages sparsity, while L2 regularization (weight decay) smooths the weight distribution. AWS documentation recommends regularization as a primary method for improving generalization when a model shows a large gap between training and evaluation performance.

Option B is incorrect because removing dropout layers would increase overfitting, not reduce it. Dropout is itself a regularization technique.

Option C would likely worsen overfitting, as training for more epochs allows the model to memorize the training data even further.

Option D increases model complexity, which again exacerbates overfitting rather than resolving it.

Therefore, penalizing large weights using L1 or L2 regularization is the correct solution.

In AWS networking, security groups are stateful and allow-only, meaning they cannot explicitly deny traffic. As a result, Option A is invalid. Network ACLs (NACLs), on the other hand, are stateless and support both allow and deny rules, making them the correct mechanism for blocking traffic from specific IP addresses.

Because the SageMaker AI domain is deployed in a public subnet, inbound traffic reaches the subnet before it reaches the resource. AWS documentation states that NACLs are evaluated at the subnet level and are ideal for implementing IP-based blocking rules.

Route tables control routing paths, not traffic filtering, so Option D is incorrect. Option C is unrelated to network security and does not block traffic.

AWS best practices clearly recommend using network ACL deny rules when an explicit block is required for a specific IP address at the subnet boundary.

Therefore, Option B is the correct and AWS-aligned solution.

The requirement has two key constraints: detect class imbalance and balance classes without losing any existing data. AWS provides Amazon SageMaker Clarify as the native tool to detect pre-training bias, including class imbalance (CI). CI measures differences in label distributions between classes, and a CI value greater than 0 indicates imbalance.

Once imbalance is detected, the engineer must rebalance the dataset without discarding data. Random undersampling would remove samples from the majority class, violating the requirement. Instead, oversampling is required. SMOTE (Synthetic Minority Oversampling Technique) creates synthetic samples for the minority class, preserving all original data while improving class balance.

Amazon SageMaker Data Wrangler natively supports SMOTE, making it the correct AWS-managed tool for this preprocessing task.

Options C and D are incorrect because SageMaker JumpStart is used for pretrained models and solutions, not for bias detection reporting. Option A is incorrect because it uses undersampling and misinterprets CI = 0 (which actually indicates no imbalance).

Therefore, detecting imbalance with SageMaker Clarify and correcting it using SMOTE in Data Wrangler is the correct solution.