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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Deployment and Orchestration of ML Workflows | 22% | - ML pipeline orchestration
|
| Topic 2: Data Preparation for Machine Learning (ML) | 28% | - Data ingestion and collection
|
| Topic 3: ML Model Development | 26% | - Model tuning and evaluation
|
| Topic 4: ML Solution Monitoring, Maintenance, and Security | 24% | - Security and governance
|
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NEW QUESTION # 31
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?
Answer: B
Explanation:
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.
NEW QUESTION # 32
A company needs to run a batch data-processing job on Amazon EC2 instances. The job will run during the weekend and will take 90 minutes to finish running. The processing can handle interruptions. The company will run the job every weekend for the next 6 months.
Which EC2 instance purchasing option will meet these requirements MOST cost-effectively?
Answer: B
NEW QUESTION # 33
A company is training a deep learning model to detect abnormalities in images. The company has limited GPU resources and a large hyperparameter space to explore. The company needs to test different configurations and avoid wasting computation time on poorly performing models that show weak validation accuracy in early epochs.
Which hyperparameter optimization strategy should the company use?
Answer: A
Explanation:
When GPU resources are limited and the hyperparameter search space is large, AWS documentation strongly recommends Bayesian optimization combined with early stopping. Bayesian optimization uses past evaluation results to intelligently select the next set of hyperparameters to test, focusing exploration on promising regions of the search space rather than testing all combinations.
In Amazon SageMaker, Bayesian optimization is the default and recommended strategy for hyperparameter tuning jobs. It significantly reduces the number of training runs required compared to grid or random search, making it highly cost-efficient for deep learning workloads.
Early stopping further improves efficiency by terminating training jobs that show poor validation performance in early epochs. This prevents wasted GPU time on configurations that are unlikely to perform well. AWS explicitly documents early stopping as a key feature for controlling training cost and duration.
Grid search and exhaustive search are computationally expensive and impractical for large hyperparameter spaces. Manual tuning is slow, error-prone, and does not scale.
By combining Bayesian optimization with early stopping, the company can rapidly converge on high- performing hyperparameter configurations while minimizing resource usage.
Therefore, Option B is the correct and AWS-aligned solution.
NEW QUESTION # 34
A company is interested in building a fraud detection model. Currently, the data scientist does not have a sufficient amount of information due to the low number of fraud cases.
Which method is MOST likely to detect the GREATEST number of valid fraud cases?
Answer: A
Explanation:
With datasets that are not fully populated, the Synthetic Minority Over-sampling Technique (SMOTE. adds new information by adding synthetic data points to the minority class. This technique would be the most effective in this scenario.
NEW QUESTION # 35
An advertising company uses AWS Lake Formation to manage a data lake. The data lake contains structured data and unstructured data. The company's ML engineers are assigned to specific advertisement campaigns.
The ML engineers must interact with the data through Amazon Athena and by browsing the data directly in an Amazon S3 bucket. The ML engineers must have access to only the resources that are specific to their assigned advertisement campaigns.
Which solution will meet these requirements in the MOST operationally efficient way?
Answer: C
Explanation:
AWS Lake Formation provides fine-grained access control and simplifies data governance for data lakes. By configuring Lake Formation tags to map ML engineers to their specific campaigns, you can restrict access to both structured and unstructured data in the data lake. This method is operationally efficient, as it centralizes access control management within Lake Formation and ensures consistency across Amazon Athena and S3 bucket access without requiring manual updates to policies or DynamoDB-based custom logic.
NEW QUESTION # 36
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