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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Processing | 22% | - Data validation and quality assessment - Data pipelining with AWS services ( Glue, Data Brew, etc.) - Data preprocessing and feature engineering - Handling imbalanced data - Data ingestion and transformation |
| Topic 2: Model Deployment and Inference | 20% | - A/B testing and shadow mode deployment - Inference optimization (latency, throughput) - Model versioning and rollback - SageMaker endpoints configuration - Model deployment strategies (real-time, batch) |
| Topic 3: MLOps and Monitoring | 28% | - Model lineage and reproducibility - CI/CD pipelines for ML - Model monitoring and drift detection - Cost optimization for ML workloads - Security and access management for ML - Incident response and remediation |
| Topic 4: Model Development | 30% | - Transfer learning and fine-tuning - Hyperparameter optimization - Training and validation strategies - ML frameworks (SageMaker, built-in algorithms) - Algorithm selection and model architecture - Distributed training |
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NEW QUESTION # 32
A company uses AWS CodePipeline to orchestrate a continuous integration and continuous delivery (CI/CD) pipeline for ML models and applications.
Select and order the steps from the following list to describe a CI/CD process for a successful deployment.
Select each step one time. (Select and order FIVE.)
. CodePipeline deploys ML models and applications to production.
CodePipeline detects code changes and starts to build automatically.
. Human approval is provided after testing is successful.
. The company builds and deploys ML models and applications to staging servers for testing.
. The company commits code changes or new training datasets to a Git repository.
Answer:
Explanation:
Explanation:
Step 1:
The company commits code changes or new training datasets to a Git repository.
This is the trigger point. A source code or data change initiates the CI/CD pipeline.
Step 2:
CodePipeline detects code changes and starts to build automatically.
CodePipeline monitors the Git repository (for example, AWS CodeCommit, GitHub, or Bitbucket) and automatically triggers the pipeline when changes are detected.
Step 3:
The company builds and deploys ML models and applications to staging servers for testing.
The pipeline runs build, training, and test stages (often using AWS CodeBuild and SageMaker) and deploys artifacts to a staging or test environment for validation.
Step 4:
Human approval is provided after testing is successful.
A manual approval action is a best practice for ML workflows to ensure governance, compliance, and quality checks before production deployment.
Step 5:
CodePipeline deploys ML models and applications to production.
After approval, the pipeline automatically deploys the validated model or application to the production environment.
NEW QUESTION # 33
A company stores time-series data about user clicks in an Amazon S3 bucket. The raw data consists of millions of rows of user activity every day. ML engineers access the data to develop their ML models.
The ML engineers need to generate daily reports and analyze click trends over the past 3 days by using Amazon Athena. The company must retain the data for 30 days before archiving the data.
Which solution will provide the HIGHEST performance for data retrieval?
Answer: C
Explanation:
Partitioning the time-series data by date prefix in the S3 bucket significantly improves query performance in Amazon Athena by reducing the amount of data that needs to be scanned during queries. This allows the ML engineers to efficiently analyze trends over specific time periods, such as the past 3 days. Applying S3 Lifecycle policies to archive partitions older than 30 days to S3 Glacier FlexibleRetrieval ensures cost- effective data retention and storage management while maintaining high performance for recent data retrieval.
NEW QUESTION # 34
Case study
An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.
The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data.
The training dataset includes categorical data and numerical data. The ML engineer must prepare the training dataset to maximize the accuracy of the model.
Which action will meet this requirement with the LEAST operational overhead?
Answer: A
Explanation:
Preparing a training dataset that includes both categorical and numerical data is essential for maximizing the accuracy of a machine learning model. Transforming categorical data into numerical format is a critical step, as most ML algorithms require numerical input.
Why Transform Categorical Data into Numerical Data?
* Model Compatibility: Many ML algorithms cannot process categorical data directly and require numerical representations.
* Improved Performance: Proper encoding of categorical variables can enhance model accuracy and convergence speed.
Why Use Amazon SageMaker Data Wrangler?
Amazon SageMaker Data Wrangler offers a visual interface with over 300 built-in data transformations, including tools for encoding categorical variables.
Implementation Steps:
* Import Data:
* Load the dataset into SageMaker Data Wrangler from sources like Amazon S3 or on-premises databases.
* Identify Categorical Features:
* Use Data Wrangler's data type inference to detect categorical columns.
* Apply Categorical Encoding:
* Choose appropriate encoding techniques (e.g., one-hot encoding or ordinal encoding) from Data Wrangler's transformation options.
* Apply the selected transformation to convert categorical features into numerical format.
* Validate Transformations:
* Review the transformed dataset to ensure accuracy and completeness.
Advantages of Using SageMaker Data Wrangler:
* Ease of Use: Provides a user-friendly interface for data transformation without extensive coding.
* Operational Efficiency: Integrates data preparation steps, reducing the need for multiple tools and minimizing operational overhead.
* Flexibility: Supports various data sources and transformation techniques, accommodating diverse datasets.
By utilizing SageMaker Data Wrangler to transform categorical data into numerical format, the ML engineer can efficiently prepare the dataset, thereby enhancing the model's accuracy with minimal operational overhead.
Transform Data - Amazon SageMaker
Prepare ML Data with Amazon SageMaker Data Wrangler
NEW QUESTION # 35
Hotspot Question
A company needs to train an ML model that will use historical transaction data to predict customer behavior.
Select the correct AWS service from the following list to perform each task on the data. Each service should be selected one time or not at all. (Select three.)
- Amazon Athena
- AWS Glue
- Amazon Kinesis Data Streams
- Amazon S3
Answer:
Explanation:
NEW QUESTION # 36
A company needs to give its ML engineers appropriate access to training data. The ML engineers must access training data from only their own business group. The ML engineers must not be allowed to access training data from other business groups.
The company uses a single AWS account and stores all the training data in Amazon S3 buckets. All ML model training occurs in Amazon SageMaker.
Which solution will provide the ML engineers with the appropriate access?
Answer: D
Explanation:
By creating IAM policies with specific permissions, you can restrict access to Amazon S3 buckets or objects based on the user's business group. These policies can be attached to IAM users or IAM roles associated with the ML engineers, ensuring that each engineer can only access training data belonging to their group. This approach is secure, scalable, and aligns with AWS best practices for access control.
NEW QUESTION # 37
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