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| Section | Weight | Objectives |
|---|---|---|
| Model Deployment and Inference | 20% | - Inference optimization (latency, throughput) - A/B testing and shadow mode deployment - Model versioning and rollback - SageMaker endpoints configuration - Model deployment strategies (real-time, batch) |
| Data Processing | 22% | - Handling imbalanced data - Data pipelining with AWS services ( Glue, Data Brew, etc.) - Data ingestion and transformation - Data validation and quality assessment - Data preprocessing and feature engineering |
| MLOps and Monitoring | 28% | - Model lineage and reproducibility - Incident response and remediation - CI/CD pipelines for ML - Security and access management for ML - Model monitoring and drift detection - Cost optimization for ML workloads |
| Model Development | 30% | - Training and validation strategies - Algorithm selection and model architecture - Distributed training - Hyperparameter optimization - Transfer learning and fine-tuning - ML frameworks (SageMaker, built-in algorithms) |
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NEW QUESTION # 61
An ML engineer is working on an ML model to predict the prices of similarly sized homes. The model will base predictions on several features The ML engineer will use the following feature engineering techniques to estimate the prices of the homes:
* Feature splitting
* Logarithmic transformation
* One-hot encoding
* Standardized distribution
Select the correct feature engineering techniques for the following list of features. Each feature engineering technique should be selected one time or not at all (Select three.)
Answer:
Explanation:
Explanation:
* City (name): One-hot encoding
* Type_year (type of home and year the home was built): Feature splitting
* Size of the building (square feet or square meters): Standardized distribution
* City (name): One-hot encoding
* Why? The "City" is a categorical feature (non-numeric), so one-hot encoding is used to transform it into a numeric format. This encoding creates binary columns for each unique category (e.g., cities like "New York" or "Los Angeles"), which the model can interpret.
* Type_year (type of home and year the home was built): Feature splitting
* Why? "Type_year" combines two pieces of information into one column, which could confuse the model. Feature splitting separates this column into two distinct features: "Type of home" and
"Year built," enabling the model to process each feature independently.
* Size of the building (square feet or square meters): Standardized distribution
* Why? Size is a continuous numerical variable, and standardization (scaling the feature to have a mean of 0 and a standard deviation of 1) ensures that the model treats it fairly compared to other features, avoiding bias from differences in feature scale.
By applying these feature engineering techniques, the ML engineer can ensure that the input data is correctly formatted and optimized for the model to make accurate predictions.
NEW QUESTION # 62
An airline company deploys ML models to one dozen Amazon SageMaker Al inference endpoints. The inference endpoints must be able to handle different types of workloads in a cost-effective way.
Select the correct inference option from the following list to handle each type of workload. Select each inference option one time. (Select FOUR.)
* Asynchronous inference
* Batch inference
* Real-time inference
* Serverless inference
Answer:
Explanation:
* Provide flight departure, arrival, and delay information, and provide updates for low-latency workloads# Real-time inference
* Advertise holiday travel promotional deals to millions of users in multiple markets before holiday seasons for spiky workloads# Serverless inference
* Generate quarterly and annual flight reports and insights for trend analysis of large datasets# Batch inference
* Generate online image and audio stories for passengers to watch or listen to while waiting at an airport# Asynchronous inference
* The correct mapping depends on latency requirement, traffic pattern, payload size, processing duration, and whether the workload needs a persistent endpoint.
* Real-time inference is the right choice for flight departure, arrival, and delay updates because this is an online user-facing workload that requires low latency. AWS states that SageMaker real-time inference is ideal for online inference workloads with low-latency or high-throughput requirements and uses a persistent fully managed endpoint. That fits flight status information because passengers and airline systems expect immediate responses.
* Serverless inference is the best choice for holiday promotional deals because this traffic is spiky, seasonal, and unpredictable. AWS describes SageMaker Serverless Inference as suitable for intermittent or unpredictable traffic patterns. It is cost-effective because SageMaker manages the infrastructure and scales down when there are no requests, so the company does not pay for idle endpoint capacity.
* Batch inference is correct for quarterly and annual flight reports because this workload analyzes large datasets offline and does not need an always-running endpoint. AWS says SageMaker batch transform is used to get inferences from large datasets and when a persistent endpoint is not required. Reports and trend analysis are scheduled, non-real-time analytics workloads, so batch inference is the most cost- effective option.
* Asynchronous inference is the right choice for generating online image and audio stories. These requests can have larger payloads and longer processing times than normal low-latency API calls. AWS states that SageMaker Asynchronous Inference queues incoming requests and is ideal for large payloads, long processing times, and near-real-time latency requirements. Image and audio generation can take seconds or minutes, so asynchronous inference is more appropriate than real-time inference.
NEW QUESTION # 63
An ML engineer needs to create data ingestion pipelines and ML model deployment pipelines on AWS. All the raw data is stored in Amazon S3 buckets.
Which solution will meet these requirements?
Answer: C
Explanation:
AWS Glue is a serverless data integration service that is well-suited for creating data ingestion pipelines, especially when raw data is stored in Amazon S3. It can clean, transform, and catalog data, making it accessible for downstream ML tasks.
Amazon SageMaker Studio Classic provides a comprehensive environment for building, training, and deploying ML models. It includes built-in tools and capabilities to create efficient model deployment pipelines with minimal setup.
This combination ensures seamless integration of data ingestion and ML model deployment with minimal operational overhead.
NEW QUESTION # 64
A company is developing ML models by using PyTorch and TensorFlow estimators with Amazon SageMaker AI. An ML engineer configures the SageMaker AI estimator and now needs to initiate a training job that uses a training dataset.
Which SageMaker AI SDK method can initiate the training job?
Answer: D
Explanation:
In the Amazon SageMaker Python SDK, the fit() method is used to start a training job after an estimator has been configured. AWS documentation explicitly states that once an estimator (such as PyTorch or TensorFlow) is defined with parameters like instance type, framework version, and hyperparameters, the fit() method is responsible for launching the training process.
The fit() method accepts the training data location (commonly an Amazon S3 URI) and initiates the managed training job on SageMaker infrastructure. SageMaker then provisions the required compute resources, stages the data, executes the training script, and stores model artifacts in Amazon S3.
The create_model() method is used after training to create a SageMaker model object from trained artifacts.
The deploy() method deploys a trained model to an endpoint for inference. The predict() method is used only after deployment to request predictions from an endpoint.
AWS documentation clearly separates these lifecycle steps and identifies fit() as the correct method to initiate training.
Therefore, Option A is the correct and AWS-verified answer.
NEW QUESTION # 65
An ML engineer receives datasets that contain missing values, duplicates, and extreme outliers. The ML engineer must consolidate these datasets into a single data frame and must prepare the data for ML.
Which solution will meet these requirements?
Answer: B
Explanation:
Amazon SageMakerData Wranglerprovides a comprehensive solution for importing, consolidating, and preparing datasets for ML. It offers tools to handle missing values, duplicates, and outliers through its built- incleansingandenrichmentfunctionalities, allowing the ML engineer to efficiently prepare the data in a single environment with minimal manual effort.
NEW QUESTION # 66
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