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| Section | Weight | Objectives |
|---|---|---|
| Model Deployment and Inference | 20% | - Model versioning and rollback - Inference optimization (latency, throughput) - A/B testing and shadow mode deployment - SageMaker endpoints configuration - Model deployment strategies (real-time, batch) |
| Model Development | 30% | - Distributed training - Algorithm selection and model architecture - Training and validation strategies - ML frameworks (SageMaker, built-in algorithms) - Transfer learning and fine-tuning - Hyperparameter optimization |
| MLOps and Monitoring | 28% | - Security and access management for ML - Model lineage and reproducibility - Incident response and remediation - CI/CD pipelines for ML - Cost optimization for ML workloads - Model monitoring and drift detection |
| Data Processing | 22% | - Data pipelining with AWS services ( Glue, Data Brew, etc.) - Data validation and quality assessment - Data ingestion and transformation - Handling imbalanced data - Data preprocessing and feature engineering |
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NEW QUESTION # 222
A financial company receives a high volume of real-time market data streams from an external provider. The streams consist of thousands of JSON records every second.
The company needs to implement a scalable solution on AWS to identify anomalous data points.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: A
Explanation:
The key requirements are real-time processing, high throughput, and minimal operational overhead. Amazon Kinesis Data Streams is designed for ingesting thousands of events per second with low latency.
For anomaly detection on streaming data, Amazon Managed Service for Apache Flink provides a built-in Random Cut Forest (RCF) function. RCF is an unsupervised anomaly detection algorithm that works well on numerical streaming data and does not require labeled training data.
This fully managed combination eliminates the need to deploy or maintain SageMaker endpoints, EC2 instances, or custom ML pipelines. Options B and C introduce unnecessary infrastructure and model management overhead. Option D is batch-oriented and unsuitable for real-time anomaly detection.
Therefore, using Kinesis Data Streams with Flink's built-in Random Cut Forest is the most scalable and low- overhead solution.
NEW QUESTION # 223
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: D
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 # 224
A company is planning to create several ML prediction models. The training data is stored in Amazon S3. The entire dataset is more than 5 ## in size and consists of CSV, JSON, Apache Parquet, and simple text files.
The data must be processed in several consecutive steps. The steps include complex manipulations that can take hours to finish running. Some of the processing involves natural language processing (NLP) transformations. The entire process must be automated.
Which solution will meet these requirements?
Answer: B
Explanation:
Amazon SageMaker Pipelines is designed for creating, automating, and managing end-to-end ML workflows, including complex data preprocessing tasks. It supports handling large datasets and can integrate with custom steps, such as NLP transformations. By combining SageMaker Pipelines with Amazon EventBridge, the entire workflow can be triggered and automated efficiently, meeting the requirements for scalability, automation, and processing complexity.
NEW QUESTION # 225
A company is using an AWS Lambda function to monitor the metrics from an ML model. An ML engineer needs to implement a solution to send an email message when the metrics breach a threshold.
Which solution will meet this requirement?
Answer: B
NEW QUESTION # 226
A company has AWS Glue data processing jobs that are orchestrated by an AWS Glue workflow.
The AWS Glue jobs can run on a schedule or can be launched manually.
The company is developing pipelines in Amazon SageMaker Pipelines for ML model development. The pipelines will use the output of the AWS Glue jobs during the data processing phase of model development. An ML engineer needs to implement a solution that integrates the AWS Glue jobs with the pipelines.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: C
NEW QUESTION # 227
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