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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: ML Solution Monitoring, Maintenance, and Security | 24% | - Optimize and maintain workloads
|
| Topic 2: Deployment and Orchestration of ML Workflows | 22% | - Automate and orchestrate ML pipelines
|
| Topic 3: Data Preparation for Machine Learning | 28% | - Ingest and store data
|
| Topic 4: ML Model Development | 26% | - Train, tune, and refine models
|
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NEW QUESTION # 44
An ML engineer needs to use Amazon SageMaker Feature Store to create and manage features to train a model.
Select and order the steps from the following list to create and use the features in Feature Store. Each step should be selected one time. (Select and order three.)
* Access the store to build datasets for training.
* Create a feature group.
* Ingest the records.
Answer:
Explanation:
Explanation:
Step 1: Create a feature group.
Step 2: Ingest the records.
Step 3: Access the store to build datasets for training.
* Step 1: Create a Feature Group
* Why? A feature group is the foundational unit in SageMaker Feature Store, where features are defined, stored, and organized. Creating a feature group specifies the schema (name, data type) for the features and the primary keys for data identification.
* How? Use the SageMaker Python SDK or AWS CLI to define the feature group by specifying its name, schema, and S3 storage location for offline access.
* Step 2: Ingest the Records
* Why? After creating the feature group, the raw data must be ingested into the Feature Store. This step populates the feature group with data, making it available for both real-time and offline use.
* How? Use the SageMaker SDK or AWS CLI to batch-ingest historical data or stream new records into the feature group. Ensure the records conform to the feature group schema.
* Step 3: Access the Store to Build Datasets for Training
* Why? Once the features are stored, they can be accessed to create training datasets. These datasets combine relevant features into a single format for machine learning model training.
* How? Use the SageMaker Python SDK to query the offline store or retrieve real-time features using the online store API. The offline store is typically used for batch training, while the online store is used for inference.
Order Summary:
* Create a feature group.
* Ingest the records.
* Access the store to build datasets for training.
This process ensures the features are properly managed, ingested, and accessible for model training using Amazon SageMaker Feature Store.
NEW QUESTION # 45
An ML engineer is developing a fraud detection model by using the Amazon SageMaker XGBoost algorithm. The model classifies transactions as either fraudulent or legitimate.
During testing, the model excels at identifying fraud in the training dataset. However, the model is inefficient at identifying fraud in new and unseen transactions.
What should the ML engineer do to improve the fraud detection for new transactions?
Answer: C
NEW QUESTION # 46
A company is developing an ML model to predict customer satisfaction. The company needs to use survey feedback and the past satisfaction level of customers to predict the future satisfaction level of customers.
The dataset includes a column named Feedback that contains long text responses. The dataset also includes a column named Satisfaction Level that contains three distinct values for past customer satisfaction: High, Medium, and Low. The company must apply encoding methods to transform the data in each column.
Which solution will meet these requirements?
Answer: D
Explanation:
Different data types require different encoding strategies. The Feedback column contains long, unstructured text responses. According to AWS ML documentation, text data must first be converted into tokens before it can be vectorized using techniques such as embeddings or bag-of-words. Tokenization is the correct preprocessing step for textual features.
The Satisfaction Level column is categorical but has a natural ordering (Low < Medium < High). AWS best practices recommend ordinal encoding for such ordered categorical variables because it preserves the inherent ranking information.
Option A is incorrect because one-hot encoding is not suitable for free-form text and would create an unmanageable number of features. Option B has the same issue for the Feedback column. Option C incorrectly applies label encoding to text and binary encoding to a three-class ordinal variable.
Therefore, tokenization for text data and ordinal encoding for satisfaction levels is the correct solution.
NEW QUESTION # 47
An ML engineer is designing an AI-powered traffic management system. The system must use near real-time inference to predict congestion and prevent collisions.
The system must also use batch processing to perform historical analysis of predictions over several hours to improve the model. The inference endpoints must scale automatically to meet demand.
Which combination of solutions will meet these requirements? (Select TWO.)
Answer: B,C
Explanation:
For near real-time predictions, AWS documentation recommends Amazon SageMaker real-time inference endpoints. These endpoints support automatic scaling based on metrics such as ConcurrentInvocationsPerInstance, ensuring low latency and high availability during traffic spikes.
For long-running historical analysis, SageMaker Processing jobs are the appropriate solution. Processing jobs are designed for batch workloads, can run for hours, and integrate cleanly with SageMaker pipelines.
Scheduling them with Amazon EventBridge provides a fully managed, scalable, and serverless solution.
AWS Lambda is unsuitable for multi-hour workloads. EC2 Auto Scaling adds unnecessary infrastructure management overhead.
Therefore, Options A and C together meet all requirements and align with AWS best practices.
NEW QUESTION # 48
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 # 49
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