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Amazon MLA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Model Development	30%	- Transfer learning and fine-tuning - Algorithm selection and model architecture - Hyperparameter optimization - Distributed training - Training and validation strategies - ML frameworks (SageMaker, built-in algorithms)
Model Deployment and Inference	20%	- Model versioning and rollback - Inference optimization (latency, throughput) - SageMaker endpoints configuration - Model deployment strategies (real-time, batch) - A/B testing and shadow mode deployment
MLOps and Monitoring	28%	- Cost optimization for ML workloads - Incident response and remediation - Model monitoring and drift detection - Model lineage and reproducibility - Security and access management for ML - CI/CD pipelines for ML
Data Processing	22%	- Data ingestion and transformation - Handling imbalanced data - Data preprocessing and feature engineering - Data validation and quality assessment - Data pipelining with AWS services ( Glue, Data Brew, etc.)

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q228-Q233):

NEW QUESTION # 228
An ML engineer needs to merge and transform data from two sources to retrain an existing ML model. One data source consists of .csv files that are stored in an Amazon S3 bucket. Each .csv file consists of millions of records. The other data source is an Amazon Aurora DB cluster.
The result of the merge process must be written to a second S3 bucket. The ML engineer needs to perform this merge-and-transform task every week.
Which solution will meet these requirements with the LEAST operational overhead?

A. Create a weekly AWS Glue job that uses the Apache Spark engine. Use DynamicFrame native operations to merge and transform the data.
B. Create an AWS Batch job that runs Apache Spark code on Amazon EC2 instances every week.Configure the Spark code to save the data from the EC2 instances to the second S3 bucket.
C. Create an AWS Lambda function that runs Apache Spark code every week to merge and transform the data. Configure the Lambda function to connect to the initial S3 bucket and the DB cluster.
D. Create a transient Amazon EMR cluster every week. Use the cluster to run an Apache Spark job to merge and transform the data.

Answer: A

NEW QUESTION # 229
An ML engineer is training a simple neural network model. The ML engineer tracks the performance of the model over time on a validation dataset. The model's performance improves substantially at first and then degrades after a specific number of epochs.
Which solutions will mitigate this problem? (Choose two.)

A. Investigate and reduce the sources of model bias.
B. Increase dropout in the layers.
C. Increase the number of neurons.
D. Enable early stopping on the model.
E. Increase the number of layers.

Answer: B,D

Explanation:
Early stopping halts training once the performance on the validation dataset stops improving. This prevents the model from overfitting, which is likely the cause of performance degradation after a certain number of epochs.
Dropout is a regularization technique that randomly deactivates neurons during training, reducing overfitting by forcing the model to generalize better. Increasing dropout can help mitigate the problem of performance degradation due to overfitting.

NEW QUESTION # 230
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 ML engineer needs to use an Amazon SageMaker built-in algorithm to train the model.
Which algorithm should the ML engineer use to meet this requirement?

A. #-means clustering
B. Linear learner
C. LightGBM
D. Neural Topic Model (NTM)

Answer: B

Explanation:
Why Linear Learner?
* SageMaker'sLinear Learneralgorithm is well-suited for binary classification problems such as fraud detection. It handles class imbalance effectively by incorporating built-in options forweight balancing across classes.
* Linear Learner can capture patterns in the data while being computationally efficient.
Key Features of Linear Learner:
* Automatically weights minority and majority classes.
* Supports both classification and regression tasks.
* Handles interdependencies among features effectively through gradient optimization.
Steps to Implement:
* Use the SageMaker Python SDK to set up a training job with the Linear Learner algorithm.
* Configure the hyperparameters to enable balanced class weights.
* Train the model with the balanced dataset created using SageMaker Data Wrangler.

NEW QUESTION # 231
A company has collected customer comments on its products, rating them as safe or unsafe, using decision trees. The training dataset has the following features: id, date, full review, full review summary, and a binary safe/unsafe tag. During training, any data sample with missing features was dropped. In a few instances, the test set was found to be missing the full review text field.
For this use case, which is the most effective course of action to address test data samples with missing features?

A. Use an algorithm that handles missing data better than decision trees.
B. Copy the summary text fields and use them to fill in the missing full review text fields, and then run through the test set.
C. Drop the test samples with missing full review text fields, and then run through the test set.
D. Generate synthetic data to fill in the fields that are missing data, and then run through the test set.

Answer: B

Explanation:
In this case, a full review summary usually contains the most descriptive phrases of the entire review and is a valid stand-in for the missing full review text field.

NEW QUESTION # 232
An ML engineer needs to use an Amazon EMR cluster to process large volumes of data in batches. Any data loss is unacceptable.
Which instance purchasing option will meet these requirements MOST cost-effectively?

A. Run the primary node, core nodes, and task nodes on On-Demand Instances.
B. Run the primary node, core nodes, and task nodes on Spot Instances.
C. Run the primary node and core nodes on On-Demand Instances. Run the task nodes on Spot Instances.
D. Run the primary node on an On-Demand Instance. Run the core nodes and task nodes on Spot Instances.

Answer: C

NEW QUESTION # 233
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