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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Processing | 22% | - Handling imbalanced data - Data pipelining with AWS services ( Glue, Data Brew, etc.) - Data validation and quality assessment - Data ingestion and transformation - Data preprocessing and feature engineering |
| Topic 2: Model Development | 30% | - Distributed training - Transfer learning and fine-tuning - Algorithm selection and model architecture - Training and validation strategies - Hyperparameter optimization - ML frameworks (SageMaker, built-in algorithms) |
| Topic 3: Model Deployment and Inference | 20% | - Model versioning and rollback - SageMaker endpoints configuration - Inference optimization (latency, throughput) - Model deployment strategies (real-time, batch) - A/B testing and shadow mode deployment |
| Topic 4: MLOps and Monitoring | 28% | - Cost optimization for ML workloads - Model monitoring and drift detection - Model lineage and reproducibility - CI/CD pipelines for ML - Security and access management for ML - Incident response and remediation |
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NEW QUESTION # 102
A company has built more than 50 models and deployed the models on Amazon SageMaker Al as real-time inference endpoints. The company needs to reduce the costs of the SageMaker Al inference endpoints. The company used the same ML framework to build the models. The company's customers require low-latency access to the models.
Select and order the correct steps from the following list to reduce the cost of inference and keep latency low.
Select each
step one time or not at all. (Select and order FIVE.)
Create an endpoint configuration that references a multi-model container.
. Create a SageMaker Al model with multi-model endpoints enabled.
. Deploy a real-time inference endpoint by using the endpoint configuration.
. Deploy a serverless inference endpoint configuration by using the endpoint configuration.
Spread the existing models to multiple different Amazon S3 bucket paths.
. Upload the existing models to the same Amazon S3 bucket path.
. Update the models to use the new endpoint ID. Pass the model IDs to the new endpoint.
Answer:
Explanation:
Explanation:
Step 1
Upload the existing models to the same Amazon S3 bucket path.
Multi-model endpoints require all models to be stored under a single S3 prefix so SageMaker can dynamically load them on demand.
Step 2
Create a SageMaker AI model with multi-model endpoints enabled.
This creates a SageMaker model resource that uses a multi-model-capable container (for example, XGBoost, PyTorch, or TensorFlow MME-compatible containers).
Step 3
Create an endpoint configuration that references a multi-model container.
The endpoint configuration defines:
* Instance type
* Initial instance count
* The multi-model container reference
Step 4
Deploy a real-time inference endpoint by using the endpoint configuration.
Real-time endpoints ensure low-latency inference, which is a strict customer requirement.
Step 5
Update the models to use the new endpoint ID. Pass the model IDs to the new endpoint.
Each inference request specifies a model ID so SageMaker knows which model to load from S3.
NEW QUESTION # 103
A company deployed an ML model that uses the XGBoost algorithm to predict product failures.
The model is hosted on an Amazon SageMaker endpoint and is trained on normal operating data.
An AWS Lambda function provides the predictions to the company's application.
An ML engineer must implement a solution that uses incoming live data to detect decreased model accuracy over time.
Which solution will meet these requirements?
Answer: D
NEW QUESTION # 104
A company is using ML to predict the presence of a specific weed in a farmer's field. The company is using the Amazon SageMaker linear learner built-in algorithm with a value of multiclass_dassifier for the predictorjype hyperparameter.
What should the company do to MINIMIZE false positives?
Answer: B
Explanation:
Thetarget_precisionhyperparameter in the Amazon SageMaker linear learner controls the trade-off between precision and recall for the model. Increasing the target_precision prioritizes minimizing false positives by making the model more cautious in its predictions. This approach is effective for use cases where false positives have higher consequences than false negatives.
NEW QUESTION # 105
An ML engineer is building a logistic regression model to predict customer churn for subscription services.
The dataset contains two string variables: location and job_seniority_level.
The location variable has 3 distinct values, and the job_seniority_level variable has over 10 distinct values.
The ML engineer must perform preprocessing on the variables.
Which solution will meet this requirement?
Answer: D
Explanation:
Logistic regression requires numeric input features and is sensitive to how categorical variables are encoded.
AWS feature engineering best practices recommend one-hot encoding for low-cardinality categorical variables with no inherent order and ordinal encoding for categorical variables with a meaningful order.
The location feature has only three distinct values and no ordinal relationship, making one-hot encoding the most appropriate method. This prevents the model from inferring a false numerical relationship between locations.
The job_seniority_level feature typically has an inherent order (for example: junior, mid-level, senior, lead).
Even with more than 10 categories, ordinal encoding preserves this natural hierarchy while keeping the feature dimensionality manageable.
Tokenization is used for unstructured text, not structured categorical variables. Standard scaling applies only to continuous numeric features and is not suitable for categorical string variables.
AWS documentation explicitly highlights using one-hot encoding for nominal features and ordinal encoding for ordered categorical features when preparing data for linear models such as logistic regression.
Therefore, Option B is the correct and AWS-aligned solution.
NEW QUESTION # 106
A company has an ML model that uses historical transaction data to predict customer behavior.
An ML engineer is optimizing the model in Amazon SageMaker to enhance the model's predictive accuracy. The ML engineer must examine the input data and the resulting predictions to identify trends that could skew the model's performance across different demographics.
Which solution will provide this level of analysis?
Answer: A
NEW QUESTION # 107
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