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Amazon MLA-C01 Exam Overview:

Certification Vendor:	Amazon Web Services (AWS)
Exam Name:	AWS Certified Machine Learning Engineer – Associate (MLA-C01)
Exam Number:	MLA-C01
Passing Score:	720/1000
Available Languages:	Korean, Japanese, English, Simplified Chinese
Exam Format:	Multiple choice, Multiple response, Ordering, Matching
Related Certifications:	AWS Certified DevOps Engineer – Professional AWS Certified Data Engineer – Associate AWS Certified AI Practitioner AWS Certified Solutions Architect – Associate
Exam Price:	USD 150
Real Exam Qty:	65 scored questions + 15 unscored questions
Exam Duration:	130 minutes
Certificate Validity Period:	3 years
Recommended Training:	AWS Skill Builder - ML Engineer Associate Exam Prep Amazon SageMaker Documentation
Exam Registration:	AWS Certification Registration
Sample Questions:	Amazon MLA-C01 Sample Questions
Exam Way:	Online proctored or test center exam
Pre Condition:	Recommended: ~1 year experience with Amazon SageMaker and AWS-based ML or data engineering roles
Official Syllabus URL:	https://aws.amazon.com/certification/certified-machine-learning-engineer-associate/

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Amazon MLA-C01 Exam Syllabus Topics:

Topic	Details
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	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.
Topic 3	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 4	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.

Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q166-Q171):

NEW QUESTION # 166
A company uses Amazon SageMaker Studio to develop an ML model. The company has a single SageMaker Studio domain. An ML engineer needs to implement a solution that provides an automated alert when SageMaker AI compute costs reach a specific threshold.
Which solution will meet these requirements?

A. Add resource tagging by editing the SageMaker AI user profile in the SageMaker AI domain. Configure AWS Budgets to send an alert when the threshold is reached.
B. Add resource tagging by editing the SageMaker AI user profile in the SageMaker AI domain. Configure AWS Cost Explorer to send an alert when the threshold is reached.
C. Add resource tagging by editing each user's IAM profile. Configure AWS Budgets to send an alert when the threshold is reached.
D. Add resource tagging by editing each user's IAM profile. Configure AWS Cost Explorer to send an alert when the threshold is reached.

Answer: A

Explanation:
AWS best practices for cost governance recommend using resource tagging combined with AWS Budgets to track and alert on service-level spending. By adding tags at the SageMaker Studio user profile level, all compute resources launched by users inherit those tags automatically.
AWS Budgets supports threshold-based alerts, unlike AWS Cost Explorer, which is primarily used for historical analysis and visualization. Budgets can trigger notifications via email or Amazon SNS when spending exceeds defined limits.
IAM profiles are unrelated to cost tracking, making options C and D invalid. Therefore, tagging SageMaker user profiles and using AWS Budgets is the correct solution.

NEW QUESTION # 167
An ML engineer is deploying a generative AI model-based customer support agent that uses Amazon SageMaker AI for inference. The customer support agent must respond to customer questions about topics such as shipping policies, refund processes, and account management. The generative AI model generates one token at a time.
Customers report dissatisfaction with how long the customer support agent takes to generate lengthy responses to questions. The ML engineer must apply an inference optimization technique to improve the performance of the customer support agent.
Which solution will meet this requirement?

A. Fast model loading
B. Speculative decoding
C. Compilation
D. Quantization

Answer: B

Explanation:
Option B is correct because AWS documentation states that speculative decoding is specifically a technique to speed up the decoding process of large language models. The question highlights that the model generates one token at a time and that users are unhappy with the time needed to generate lengthy responses. That is exactly the stage of inference targeted by speculative decoding. AWS explains that this method improves latency without compromising the quality of the generated text.
According to AWS, speculative decoding works by using a smaller, faster draft model to generate candidate tokens first. The larger target model then verifies those tokens. AWS further explains that the draft model can generate multiple candidate tokens quickly, and the target model evaluates them in parallel, which speeds up the final response. This directly addresses the problem in the scenario: the customer support agent is slow not because of startup time, but because long answers require many decoding steps.
The other options are not the best fit. Compilation reduces deployment time and auto-scaling latency through ahead-of-time compilation, but AWS documents it primarily as helping model deployment and hardware optimization rather than token-by-token response generation. Quantization reduces hardware requirements and can lower cost, but the docs do not present it as the most direct answer to slow sequential decoding. Fast model loading improves how quickly the model loads onto instances, which helps startup and scale-out time, not long response generation after the model is already serving traffic. Therefore, the best AWS-documented answer is B.

NEW QUESTION # 168
An ML engineer wants to use a set of survey responses as training data for an ML classifier. All the survey responses are either "yes" or "no." The ML engineer needs to convert the responses into a feature that will produce better model training results. The ML engineer must not increase the dimensionality of the dataset.
Which methods will meet these requirements? (Choose two.)

A. Statistical imputation
B. Label encoding
C. One-hot encoding
D. Tokenization
E. Binary encoding

Answer: B,E

Explanation:
Both binary encoding and label encoding convert categorical yes/no responses into numerical values without increasing dimensionality. For example, mapping yes → 1 and no → 0. Unlike one-hot encoding, which would add extra dimensions, these methods keep the dataset compact and effective for training.

NEW QUESTION # 169
A company is using an Amazon S3 bucket to collect data that will be used for ML workflows. The company needs to use AWS Glue DataBrew to clean and normalize the data.
Which solution will meet these requirements?

A. Create a DataBrew dataset by using the S3 path. Clean and normalize the data by using a DataBrew profile job.
B. Create a DataBrew dataset by using the S3 path. Clean and normalize the data by using a DataBrew recipe job.
C. Create a DataBrew dataset by using a JDBC driver to connect to the S3 bucket. Use a profile job.
D. Create a DataBrew dataset by using a JDBC driver to connect to the S3 bucket. Use a recipe job.

Answer: B

Explanation:
AWS Glue DataBrew supports datasets sourced directly from Amazon S3 paths. To clean and normalize data, AWS documentation specifies using DataBrew recipes, which define transformation steps such as standardization, deduplication, and formatting.
A profile job is used only for data analysis and statistics generation, not for transformation. A recipe job applies actual transformations to the data and writes the output to Amazon S3.
JDBC connections are used for relational databases, not S3 buckets. Therefore, options C and D are invalid.
AWS clearly documents that the correct workflow is:
* Create a DataBrew dataset from an S3 path
* Define transformations using a recipe
* Run a recipe job to produce cleaned data
Thus, Option B is the correct and AWS-aligned answer.

NEW QUESTION # 170
A company is using an Amazon Redshift database as its single data source. Some of the data is sensitive.
A data scientist needs to use some of the sensitive data from the database. An ML engineer must give the data scientist access to the data without transforming the source data and without storing anonymized data in the database.
Which solution will meet these requirements with the LEAST implementation effort?

A. Create a materialized view with masking logic on top of the database. Grant the necessary read permissions to the data scientist.
B. Configure dynamic data masking policies to control how sensitive data is shared with the data scientist at query time.
C. Unload the Amazon Redshift data to Amazon S3. Create an AWS Glue job to anonymize the data. Share the dataset with the data scientist.
D. Unload the Amazon Redshift data to Amazon S3. Use Amazon Athena to create schema-on-read with masking logic. Share the view with the data scientist.

Answer: B

NEW QUESTION # 171
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