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| Section | Weight | Objectives |
|---|---|---|
| Data Preparation for Machine Learning (ML) | 28% | - Data preprocessing and transformation
|
| ML Model Development | 26% | - Model tuning and evaluation
|
| ML Solution Monitoring, Maintenance, and Security | 24% | - Security and governance
|
| Deployment and Orchestration of ML Workflows | 22% | - ML pipeline orchestration
|
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NEW QUESTION # 212
A company needs to use Amazon SageMaker to train a model on more than 300 GB of data. The training data is composed of files that are 200 MB in size. The data is stored in Amazon S3 Standard storage and feeds a dashboard tool. Which SageMaker training ingestion mechanism is the MOST cost-effective solution for this scenario?
Answer: D
Explanation:
For large-scale training data already stored in Amazon S3, the most cost-effective solution is to use SageMaker's S3 fast file mode without S3 Express One Zone. Fast file mode enables streaming directly from S3 without duplicating the dataset onto local storage, reducing startup time and storage cost. Using S3 Express One Zone would increase cost, so standard fast file mode is the most economical choice.
NEW QUESTION # 213
A company has historical data that shows whether customers needed long-term support from company staff. The company needs to develop an ML model to predict whether new customers will require long-term support.
Which modeling approach should the company use to meet this requirement?
Answer: A
NEW QUESTION # 214
A company needs to combine data from multiple sources. The company must use Amazon Redshift Serverless to query an AWS Glue Data Catalog database and underlying data that is stored in an Amazon S3 bucket.
Select and order the correct steps from the following list to meet these requirements. Select each step one time or not at all. (Select and order three.)
* Attach the IAM role to the Redshift cluster.
* Attach the IAM role to the Redshift namespace.
* Create an external database in Amazon Redshift to point to the Data Catalog schema.
* Create an external schema in Amazon Redshift to point to the Data Catalog database.
* Create an IAM role for Amazon Redshift to use to access only the S3 bucket that contains underlying data.
* Create an IAM role for Amazon Redshift to use to access the Data Catalog and the S3 bucket that contains underlying data.
Answer:
Explanation:
Explanation:
Step 1
Create an IAM role for Amazon Redshift to use to access the Data Catalog and the S3 bucket that contains underlying data.
This role must include:
Permissions for AWS Glue Data Catalog (e.g., glue:GetDatabase, glue:GetTables) Permissions for the Amazon S3 bucket that stores the underlying data Step 2 Attach the IAM role to the Redshift namespace.
Redshift Serverless uses a namespace, not a cluster, so the role must be associated with the namespace to allow Redshift to assume it when querying external data.
Step 3
Create an external schema in Amazon Redshift to point to the Data Catalog database.
The external schema maps Redshift to the Glue Data Catalog database so Redshift can query the tables stored in S3.
NEW QUESTION # 215
An ML engineer is training an ML model to identify medical patients for disease screening. The tabular dataset for training contains 50,000 patient records: 1,000 with the disease and 49,000 without the disease.
The ML engineer splits the dataset into a training dataset, a validation dataset, and a test dataset.
What should the ML engineer do to transform the data and make the data suitable for training?
Answer: B
Explanation:
This dataset shows severe class imbalance, with only 2% of records representing patients with the disease.
AWS ML best practices recommend correcting imbalance only in the training dataset, while keeping validation and test sets representative of real-world distributions.
Synthetic Minority Oversampling Technique (SMOTE) generates synthetic samples of the minority class by interpolating between existing minority examples. This improves the model's ability to learn disease-related patterns without discarding data.
PCA is a dimensionality reduction method, not an oversampling technique. Oversampling the majority class worsens imbalance. Altering the test dataset would invalidate evaluation results.
Therefore, applying SMOTE to the training dataset is the correct approach.
NEW QUESTION # 216
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 # 217
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