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| Section | Weight | Objectives |
|---|---|---|
| MLOps and Monitoring | 28% | - Incident response and remediation - CI/CD pipelines for ML - Security and access management for ML - Cost optimization for ML workloads - Model lineage and reproducibility - Model monitoring and drift detection |
| Model Deployment and Inference | 20% | - Inference optimization (latency, throughput) - Model deployment strategies (real-time, batch) - A/B testing and shadow mode deployment - Model versioning and rollback - SageMaker endpoints configuration |
| Model Development | 30% | - Algorithm selection and model architecture - ML frameworks (SageMaker, built-in algorithms) - Hyperparameter optimization - Transfer learning and fine-tuning - Training and validation strategies - Distributed training |
| Data Processing | 22% | - Data validation and quality assessment - Data pipelining with AWS services ( Glue, Data Brew, etc.) - Handling imbalanced data - Data ingestion and transformation - Data preprocessing and feature engineering |
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NEW QUESTION # 49
An ML engineer is analyzing potential biases in a customer dataset before training an ML model. The dataset contains customer age (numeric), product reviews (text), and purchase outcomes (categorical).
Which statistical metrics should the ML engineer use to identify potential biases in the dataset before model training?
Answer: A
Explanation:
Bias detection is a critical step in responsible machine learning and is emphasized in AWS documentation, particularly in Amazon SageMaker Clarify. When analyzing structured datasets that include sensitive or influential attributes such as age, AWS recommends evaluating label distribution fairness and group-based outcome differences before training a model.
The class imbalance metric helps identify whether certain outcomes (for example, purchase vs. no purchase) are overrepresented or underrepresented. Severe imbalance can cause models to favor majority classes, leading to biased predictions. AWS explicitly highlights class imbalance as a key issue to assess during data exploration.
The Difference in Proportions of Labels (DPL) is a fairness metric supported by SageMaker Clarify that measures whether outcome labels are disproportionately distributed across different groups, such as age ranges. DPL compares the proportion of favorable outcomes between groups, making it especially effective for identifying demographic bias in categorical labels.
Options A and D focus on descriptive statistics or correlations, which are useful for data understanding but do not directly measure bias or fairness. Option B partially addresses imbalance and sentiment but sentiment analysis of reviews alone does not quantify demographic bias tied to outcomes.
AWS documentation strongly recommends using group fairness metrics, including DPL, alongside class imbalance checks to identify bias before training. These metrics provide actionable insights into whether a dataset may lead to unfair or skewed model behavior.
Therefore, Option C is the most appropriate and AWS-aligned choice.
NEW QUESTION # 50
A company has an ML model that generates text descriptions based on images that customers upload to the company's website. The images can be up to 50 MB in total size.
An ML engineer decides to store the images in an Amazon S3 bucket. The ML engineer must implement a processing solution that can scale to accommodate changes in demand.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: C
Explanation:
SageMaker Asynchronous Inference is designed for processing large payloads, such as images up to 50 MB, and can handle requests that do not require an immediate response.
It scales automatically based on the demand, minimizing operational overhead while ensuring cost-efficiency.
A script can be used to send inference requests for each image, and the results can be retrieved asynchronously. This approach is ideal for accommodating varying levels of traffic with minimal manual intervention.
NEW QUESTION # 51
A company ingests sales transaction data using Amazon Data Firehose into Amazon OpenSearch Service. The Firehose buffer interval is set to 60 seconds.
The company needs sub-second latency for a real-time OpenSearch dashboard.
Which architectural change will meet this requirement?
Answer: C
Explanation:
Amazon Data Firehose supports near real-time delivery by configuring the buffer interval and buffer size.
AWS documentation states that setting the buffer interval to the minimum (as low as 1 second) enables low- latency ingestion.
By using zero or minimal buffering and tuning PutRecordBatch, data is delivered to OpenSearch almost immediately, enabling sub-second dashboard updates.
DataSync and SQS are not designed for real-time streaming analytics. Increasing the buffer interval worsens latency.
AWS explicitly recommends Firehose with minimal buffering for real-time OpenSearch ingestion.
Therefore, Option A is the correct and AWS-verified solution.
NEW QUESTION # 52
A company is using an ML model to classify motion in videos. The data is stored in MP4 format in Amazon S3. When the company created the model, the company needed 4 months to label all the video frames.
The company needs to retrain the model with an existing training workflow in Amazon SageMaker AI. An ML engineer must implement a solution that decreases the labeling time.
Which solution will meet these requirements?
Answer: A
Explanation:
Option A is correct because Amazon SageMaker Ground Truth is the AWS service specifically designed to build labeled datasets for machine learning, including video classification and video frame labeling use cases. AWS documentation states that Ground Truth can take video files in MP4 format stored in Amazon S3 , automatically connect to that data, and extract video frames for labeling. This directly matches the scenario in the question, where the source data is MP4 in S3 and the company needs to relabel or expand training data for retraining an existing SageMaker workflow.
Ground Truth is also the best choice because AWS documents explain that it can reduce labeling time and cost through automated data labeling (active learning) for supported built-in task types. Instead of relying only on manual human labeling for every item, Ground Truth can use ML-assisted labeling to lower the total amount of human work needed, which is exactly what the company wants after previously spending four months labeling frames.
The other options do not fit as well. JumpStart provides pretrained models and solution templates, but AWS documentation does not position it as the service for running a video-frame labeling operation. Data Wrangler is for data preparation, transformation, and feature engineering, not human labeling of video frames. Amazon A2I with Rekognition is mainly documented for human review workflows such as Rekognition moderation outputs, not as the primary service for large-scale MP4 video-frame annotation used to create training datasets.
NEW QUESTION # 53
A company is using Amazon SageMaker to create ML models. The company's data scientists need fine-grained control of the ML workflows that they orchestrate. The data scientists also need the ability to visualize SageMaker jobs and workflows as a directed acyclic graph (DAG). The data scientists must keep a running history of model discovery experiments and must establish model governance for auditing and compliance verifications.
Which solution will meet these requirements?
Answer: B
NEW QUESTION # 54
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