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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: ML Model Development | 26% | - Model tuning and evaluation
|
| Topic 2: Data Preparation for Machine Learning (ML) | 28% | - Data ingestion and collection
|
| Topic 3: ML Solution Monitoring, Maintenance, and Security | 24% | - Monitoring and observability
|
| Topic 4: Deployment and Orchestration of ML Workflows | 22% | - Model deployment
|
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NEW QUESTION # 182
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 # 183
A company wants to launch a new internal generative AI interface to answer user questions. The interface will be based on a popular open source large language model (LLM). Which combination of steps will deploy the interface with the LEAST operational overhead? (Choose two.)
Answer: B,E
Explanation:
The least operational overhead comes from using Amazon SageMaker JumpStart to quickly deploy the open source LLM without needing to manage infrastructure, and building a lightweight frontend HTML interface with API Gateway WebSocket API and Lambda to handle user interactions efficiently. This avoids the manual setup of EC2 or unrelated services like QuickSight or Lex.
NEW QUESTION # 184
A company has an ML model that is deployed to an Amazon SageMaker AI endpoint for real-time inference.
The company needs to deploy a new model. The company must compare the new model's performance to the currently deployed model's performance before shifting all traffic to the new model.
Which solution will meet these requirements with the LEAST operational effort?
Answer: C
Explanation:
AWS recommends shadow testing to evaluate a new model against a production model with minimal operational overhead. Using production variants on a single SageMaker endpoint allows traffic to be routed to multiple models without managing additional endpoints.
With a shadow variant, the new model receives a copy of live traffic but does not affect production responses.
Performance metrics such as latency, accuracy, and error rates can be compared directly against the current model using Amazon CloudWatch metrics. This approach is natively supported by Amazon SageMaker Endpoints.
Options A, B, and D introduce unnecessary complexity by requiring additional endpoints, traffic routing infrastructure, or custom code.
Therefore, deploying the new model as a shadow variant on the same endpoint is the most efficient solution.
NEW QUESTION # 185
A company is developing an ML model for a customer. The training data is stored in an Amazon S3 bucket in the customer's AWS account (Account A). The company runs Amazon SageMaker AI training jobs in a separate AWS account (Account B).
The company defines an S3 bucket policy and an IAM policy to allow reads to the S3 bucket.
Which additional steps will meet the cross-account access requirement?
Answer: C
Explanation:
For cross-account Amazon S3 access, AWS requires two components:
* An S3 bucket policy in the owning account (Account A) that grants access to a principal in another account
* An IAM role policy in the consuming account (Account B) that allows the service to access the bucket Amazon SageMaker training jobs assume an IAM role in the account where the job runs-in this case, Account B. Therefore, the IAM policy must be attached to the SageMaker execution role in Account B.
The S3 bucket policy must reside in Account A because bucket policies are owned and enforced by the bucket owner. This policy explicitly allows the IAM role from Account B to read the training data.
Any other combination fails either because the policy is in the wrong account or because the role is not the one used by SageMaker.
AWS documentation clearly describes this pattern as the correct way to grant cross-account access for SageMaker training jobs.
Therefore, Option B is the correct and AWS-aligned solution.
NEW QUESTION # 186
A company needs to use Retrieval Augmented Generation (RAG) to supplement an open source large language model (LLM) that runs on Amazon Bedrock. The company's data for RAG is a set of documents in an Amazon S3 bucket. The documents consist of .csv files and .docx files.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: B
NEW QUESTION # 187
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