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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Deployment and Orchestration of ML Workflows | 22% | - ML pipeline orchestration
|
| Topic 2: Data Preparation for Machine Learning (ML) | 28% | - Data ingestion and collection
|
| Topic 3: ML Solution Monitoring, Maintenance, and Security | 24% | - Security and governance
|
| Topic 4: ML Model Development | 26% | - Model tuning and evaluation
|
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NEW QUESTION # 32
An ML engineer is using Amazon SageMaker to train a deep learning model that requires distributed training. After some training attempts, the ML engineer observes that the instances are not performing as expected. The ML engineer identifies communication overhead between the training instances.
What should the ML engineer do to MINIMIZE the communication overhead between the instances?
Answer: B
NEW QUESTION # 33
A company's ML engineer is creating a classification model. The ML engineer explores the dataset and notices a column named day_of_week. The column contains the following values: Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, and Sunday.
Which technique should the ML engineer use to convert this column's data to binary values?
Answer: C
Explanation:
The day_of_week feature is a categorical variable with a small, fixed number of unique values and no inherent ordinal relationship. AWS machine learning best practices strongly recommend one-hot encoding for this type of categorical data when preparing features for classification models.
One-hot encoding converts each unique category into a separate binary feature (0 or 1). For example,
"Monday" becomes a column where Monday = 1 and all other days = 0. This ensures that the ML model does not incorrectly assume a numeric or ordered relationship between categories.
Option B (label encoding) assigns integer values to categories (e.g., Monday = 1, Tuesday = 2). AWS documentation cautions against this approach for nominal data because models may incorrectly infer ordinal meaning, leading to biased or inaccurate predictions.
Option A (binary encoding) is typically used for high-cardinality categorical features to reduce dimensionality. With only seven categories, AWS recommends one-hot encoding for clarity and interpretability.
Option D (tokenization) is used for text processing, such as NLP tasks, and is not appropriate for structured categorical features.
AWS SageMaker feature engineering guidelines emphasize that one-hot encoding is the preferred method for low-cardinality categorical variables in classification models, especially when using algorithms such as logistic regression, neural networks, and tree-based models.
Therefore, Option C is the correct and AWS-aligned choice.
NEW QUESTION # 34
An ML engineer needs to deploy ML models to get inferences from large datasets in an asynchronous manner. The ML engineer also needs to implement scheduled monitoring of the data quality of the models.
The ML engineer must receive alerts when changes in data quality occur.
Which solution will meet these requirements?
Answer: A
Explanation:
Amazon SageMaker batch transform is ideal for obtaining inferences from large datasets in an asynchronous manner, as it processes data in batches rather than requiring real-time inputs.
SageMaker Model Monitor allows scheduled monitoring of data quality, detecting shifts in input data characteristics, and generating alerts when changes in data quality occur.
This solution provides a fully managed, efficient way to handle both asynchronous inference and data quality monitoring with minimal operational overhead.
NEW QUESTION # 35
A company wants to develop an ML model by using tabular data from its customers. The data contains meaningful ordered features with sensitive information that should not be discarded. An ML engineer must ensure that the sensitive data is masked before another team starts to build the model.
Which solution will meet these requirements?
Answer: A
NEW QUESTION # 36
A company has a team of data scientists who use Amazon SageMaker notebook instances to test ML models.
When the data scientists need new permissions, the company attaches the permissions to each individual role that was created during the creation of the SageMaker notebook instance.
The company needs to centralize management of the team's permissions.
Which solution will meet this requirement?
Answer: C
Explanation:
Managing permissions for multiple Amazon SageMaker notebook instances can become complex when handled individually. To centralize and streamline permission management, AWS recommends creating a single IAM role with the necessary permissions and attaching this role to each notebook instance used by the data science team.
Steps to Implement the Solution:
* Create a Single IAM Role with Necessary Permissions:
* Define an IAM role that encompasses all permissions required by the data scientists for their tasks. This includes permissions for SageMaker operations and any other AWS services they interact with.
* AWS provides managed policies like AmazonSageMakerFullAccess that can be attached to the role to grant comprehensive SageMaker permissions.(IAM Policies for SageMaker)
* Attach the IAM Role to Each Notebook Instance:
* When creating or updating a SageMaker notebook instance, specify the IAM role created in the previous step. This ensures that all notebook instances operate under a consistent set of permissions.
* In the SageMaker console, during the notebook instance setup, you can choose an existing IAM role to associate with the instance.(Creating SageMaker Workspaces) Benefits of This Approach:
* Centralized Permission Management:By using a single IAM role, you simplify the process of updating permissions. Changes to the role's policies automatically propagate to all associated notebook instances, ensuring consistent access control.
* Adherence to Best Practices:AWS recommends using IAM roles to manage permissions for applications running on services like SageMaker. This approach avoids the need to manage individual user permissions separately.(IAM Best Practices for SageMaker) Alternative Options and Their Drawbacks:
* Option B:Creating a single IAM group and adding data scientists to it does not directly associate the group with notebook instances. IAM groups are used to manage user permissions, not to assign roles to AWS resources like notebook instances.
* Option C:Using a single IAM user with the AdministratorAccess policy is not recommended due to security risks associated with granting broad permissions and the challenges in managing shared user credentials.
* Option D:Associating an IAM group with a role and then with notebook instances is not a valid approach, as IAM groups cannot be directly associated with AWS resources.
Conclusion:Option A is the most effective solution to centralize and manage permissions for SageMaker notebook instances, aligning with AWS best practices for IAM role management.
References:
* AWS Documentation: IAM Policies for SageMaker
* AWS Documentation: Creating SageMaker Workspaces
* AWS Documentation: IAM Best Practices for SageMaker
NEW QUESTION # 37
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