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Amazon MLA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Deployment and Orchestration of ML Workflows	22%	- Model deployment 1. Real-time and batch inference endpoints 2. Auto scaling and infrastructure configuration - ML pipeline orchestration 1. CI/CD pipelines for ML workflows 2. Workflow automation using AWS services
ML Solution Monitoring, Maintenance, and Security	24%	- Monitoring and observability 1. Model drift detection and performance monitoring 2. Infrastructure and data monitoring - Security and governance 1. Compliance and secure ML system design 2. IAM policies and access control
Data Preparation for Machine Learning (ML)	28%	- Data ingestion and collection 1. Data acquisition from structured and unstructured sources 2. Batch and streaming ingestion using AWS services (e.g., S3, Kinesis, Glue) - Data preprocessing and transformation 1. Data cleaning, normalization, and feature engineering 2. Data validation and quality checks
ML Model Development	26%	- Model tuning and evaluation 1. Performance evaluation and metrics interpretation 2. Hyperparameter tuning - Model selection and training 1. Training models using Amazon SageMaker 2. Choosing appropriate ML algorithms

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q94-Q99):

NEW QUESTION # 94
A company has a large collection of chat recordings from customer interactions after a product release. An ML engineer needs to create an ML model to analyze the chat data. The ML engineer needs to determine the success of the product by reviewing customer sentiments about the product.
Which action should the ML engineer take to complete the evaluation in the LEAST amount of time?

A. Use Amazon Comprehend to analyze sentiments of the chat conversations.
B. Train a Naive Bayes classifier to analyze sentiments of the chat conversations.
C. Use Amazon Rekognition to analyze sentiments of the chat conversations.
D. Use random forests to classify sentiments of the chat conversations.

Answer: A

Explanation:
Amazon Comprehend is a fully managed natural language processing (NLP) service that includes a built-in sentiment analysis feature. It can quickly and efficiently analyze text data to determine whether the sentiment is positive, negative, neutral, or mixed. Using Amazon Comprehend requires minimal setup and provides accurate results without the need to train and deploy custom models, making it the fastest and most efficient solution for this task.

NEW QUESTION # 95
An ML engineer receives datasets that contain missing values, duplicates, and extreme outliers.
The ML engineer must consolidate these datasets into a single data frame and must prepare the data for ML.
Which solution will meet these requirements?

A. Use Amazon SageMaker Ground Truth to import the datasets and to consolidate them into a single data frame. Use the human-in-the-loop capability to prepare the data.
B. Use Amazon SageMaker Data Wrangler to import the datasets and to consolidate them into a single data frame. Use the cleansing and enrichment functionalities to prepare the data.
C. Manually import and merge the datasets. Consolidate the datasets into a single data frame. Use Amazon Q Developer to generate code snippets that will prepare the data.
D. Manually import and merge the datasets. Consolidate the datasets into a single data frame. Use Amazon SageMaker data labeling to prepare the data.

Answer: B

NEW QUESTION # 96
A company has AWS Glue data processing jobs that are orchestrated by an AWS Glue workflow.
The AWS Glue jobs can run on a schedule or can be launched manually.
The company is developing pipelines in Amazon SageMaker Pipelines for ML model development. The pipelines will use the output of the AWS Glue jobs during the data processing phase of model development. An ML engineer needs to implement a solution that integrates the AWS Glue jobs with the pipelines.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use Amazon EventBridge to invoke the pipelines and the AWS Glue jobs in the desired order.
B. Use processing steps in SageMaker Pipelines. Configure inputs that point to the Amazon Resource Names (ARNs) of the AWS Glue jobs.
C. Use AWS Step Functions for orchestration of the pipelines and the AWS Glue jobs.
D. Use Callback steps in SageMaker Pipelines to start the AWS Glue workflow and to stop the pipelines until the AWS Glue jobs finish running.

Answer: D

NEW QUESTION # 97
An ML engineer notices class imbalance in an image classification training job.
What should the ML engineer do to resolve this issue?

A. Transform some of the images in the dataset.
B. Reduce the size of the dataset.
C. Apply random data splitting on the dataset.
D. Apply random oversampling on the dataset.

Answer: D

NEW QUESTION # 98
An ML engineer needs to create data ingestion pipelines and ML model deployment pipelines on AWS. All the raw data is stored in Amazon S3 buckets.
Which solution will meet these requirements?

A. Use Amazon Redshift ML to create the data ingestion pipelines. Use Amazon SageMaker Studio Classic to create the model deployment pipelines.
B. Use AWS Glue to create the data ingestion pipelines. Use Amazon SageMaker Studio Classic to create the model deployment pipelines.
C. Use Amazon Athena to create the data ingestion pipelines. Use an Amazon SageMaker notebook to create the model deployment pipelines.
D. Use Amazon Data Firehose to create the data ingestion pipelines. Use Amazon SageMaker Studio Classic to create the model deployment pipelines.

Answer: B

Explanation:
AWS Glue is a serverless data integration service that is well-suited for creating data ingestion pipelines, especially when raw data is stored in Amazon S3. It can clean, transform, and catalog data, making it accessible for downstream ML tasks.
Amazon SageMaker Studio Classic provides a comprehensive environment for building, training, and deploying ML models. It includes built-in tools and capabilities to create efficient model deployment pipelines with minimal setup.
This combination ensures seamless integration of data ingestion and ML model deployment with minimal operational overhead.

NEW QUESTION # 99
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