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Amazon MLA-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • ML Model Development: This section of the exam measures skills of Fraud Examiners and covers choosing and training machine learning models to solve business problems such as fraud detection. It includes selecting algorithms, using built-in or custom models, tuning parameters, and evaluating performance with standard metrics. The domain emphasizes refining models to avoid overfitting and maintaining version control to support ongoing investigations and audit trails.
Topic 2
  • Data Preparation for Machine Learning (ML): This section of the exam measures skills of Forensic Data Analysts and covers collecting, storing, and preparing data for machine learning. It focuses on understanding different data formats, ingestion methods, and AWS tools used to process and transform data. Candidates are expected to clean and engineer features, ensure data integrity, and address biases or compliance issues, which are crucial for preparing high-quality datasets in fraud analysis contexts.
Topic 3
  • Deployment and Orchestration of ML Workflows: This section of the exam measures skills of Forensic Data Analysts and focuses on deploying machine learning models into production environments. It covers choosing the right infrastructure, managing containers, automating scaling, and orchestrating workflows through CI
  • CD pipelines. Candidates must be able to build and script environments that support consistent deployment and efficient retraining cycles in real-world fraud detection systems.
Topic 4
  • ML Solution Monitoring, Maintenance, and Security: This section of the exam measures skills of Fraud Examiners and assesses the ability to monitor machine learning models, manage infrastructure costs, and apply security best practices. It includes setting up model performance tracking, detecting drift, and using AWS tools for logging and alerts. Candidates are also tested on configuring access controls, auditing environments, and maintaining compliance in sensitive data environments like financial fraud detection.

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q140-Q145):

NEW QUESTION # 140
A term frequency-inverse document frequency (tf-idf) matrix using both unigrams and bigrams is built from a text corpus consisting of the following two sentences:
1. Please call the number below.
2. Please do not call us.
What are the dimensions of the tf-idf matrix?

Answer: D

Explanation:
There are 2 sentences, 8 unique unigrams, and 8 unique bigrams, so the result would be (2,16).
The phrases are "Please call the number below" and "Please do not call us." Each word individually (unigram) is "Please," "call," "the," "number," "below," "do," "not," and "us." The unique bigrams are "Please call," "call the," "the number," "number below," "Please do," "do not,"
"not call," and "call us."


NEW QUESTION # 141
A credit card company has a fraud detection model in production on an Amazon SageMaker endpoint. The company develops a new version of the model. The company needs to assess the new model's performance by using live data and without affecting production end users.
Which solution will meet these requirements?

Answer: A

Explanation:
Shadow testing allows you to send a copy of live production traffic to a shadow variant of the new model while keeping the existing production model unaffected. This enables you to evaluate the performance of the new model in real-time with live data without impacting end users. SageMaker endpoints support this setup by allowing traffic mirroring to the shadow variant, making it an ideal solution for assessing the new model's performance.


NEW QUESTION # 142
A company has a conversational AI assistant that sends requests through Amazon Bedrock to an Anthropic Claude large language model (LLM). Users report that when they ask similar questions multiple times, they sometimes receive different answers. An ML engineer needs to improve the responses to be more consistent and less random.
Which solution will meet these requirements?

Answer: A

Explanation:
Thetemperatureparameter controls the randomness in the model's responses. Lowering the temperature makes the model produce more deterministic and consistent answers.
Thetop_kparameter limits the number of tokens considered for generating the next word. Reducing top_k further constrains the model's options, ensuring more predictable responses.
By decreasing both parameters, the responses become more focused and consistent, reducing variability in similar queries.


NEW QUESTION # 143
A company needs to analyze a large dataset that is stored in Amazon S3 in Apache Parquet format. The company wants to use one-hot encoding for some of the columns.
The company needs a no-code solution to transform the data. The solution must store the transformed data back to the same S3 bucket for model training.
Which solution will meet these requirements?

Answer: A

Explanation:
AWS Glue DataBrew is specifically designed to provide no-code and low-code data preparation for analytics and machine learning. It supports common file formats such as Apache Parquet and integrates directly with Amazon S3.
Using DataBrew, users can visually create recipes that apply transformations such as one-hot encoding without writing any code. Once the recipe is defined, a DataBrew job can be run to process the dataset and store the transformed output back into Amazon S3.
Options B, C, and D all require writing SQL or code, which violates the no-code requirement. AWS documentation clearly identifies DataBrew as the correct service for interactive, visual data transformation at scale.
Therefore, Option A is the correct solution.


NEW QUESTION # 144
An ML engineer needs to process thousands of existing CSV objects and new CSV objects that are uploaded.
The CSV objects are stored in a central Amazon S3 bucket and have the same number of columns. One of the columns is a transaction date. The ML engineer must query the data based on the transaction date.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: C

Explanation:
Scenario:The ML engineer needs a low-overhead solution to query thousands of existing and new CSV objects stored in Amazon S3 based on a transaction date.
Why Athena?
* Serverless:Amazon Athena is a serverless query service that allows direct querying of data stored in S3 using standard SQL, reducing operational overhead.
* Ease of Use:By using the CTAS statement, the engineer can create a table with optimized partitions based on the transaction date. Partitioning improves query performance and minimizes costs by scanning only relevant data.
* Low Operational Overhead:No need to manage or provision additional infrastructure. Athena integrates seamlessly with S3, and CTAS simplifies table creation and optimization.
Steps to Implement:
* Organize Data in S3:Store CSV files in a bucket in a consistent format and directory structure if possible.
* Configure Athena:Use the AWS Management Console or Athena CLI to set up Athena to point to the S3 bucket.
* Run CTAS Statement:
CREATE TABLE processed_data
WITH (
format = 'PARQUET',
external_location = 's3://processed-bucket/',
partitioned_by = ARRAY['transaction_date']
) AS
SELECT *
FROM input_data;
This creates a new table with data partitioned by transaction date.
* Query the Data:Use standard SQL queries to fetch data based on the transaction date.
References:
* Amazon Athena CTAS Documentation
* Partitioning Data in Athena


NEW QUESTION # 145
......

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