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Amazon MLA-C01 Exam Overview:

Certification Vendor:Amazon Web Services (AWS)
Exam Name:AWS Certified Machine Learning Engineer - Associate
Exam Number:MLA-C01
Exam Format:Multiple response, Multiple choice
Passing Score:1000-2000 scaled score (passing score approximately 720)
Related Certifications:AWS Certified Solutions Architect - Associate
AWS Certified Developer - Associate
AWS Certified Data Engineer - Associate
Real Exam Qty:85
Available Languages:Simplified Chinese, English, Korean, Japanese, Traditional Chinese
Exam Duration:170 minutes
Exam Price:150 USD
Certificate Validity Period:3 years
Sample Questions:Amazon MLA-C01 Sample Questions
Exam Way:Online proctored (Pearson VUE) or in-person testing center
Pre Condition:Recommended: 2+ years of ML engineering experience, familiarity with AWS ML services
Official Syllabus URL:https://aws.amazon.com/certification/certified-machine-learning-engineer-associate/

>> MLA-C01공부문제 <<

MLA-C01완벽한 덤프공부자료, MLA-C01덤프문제은행

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Amazon MLA-C01 시험요강:

주제소개
주제 1
  • 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.
주제 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.
주제 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.
주제 4
  • 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.

최신 AWS Certified Associate MLA-C01 무료샘플문제 (Q192-Q197):

질문 # 192
A company has a Retrieval Augmented Generation (RAG) application that uses a vector database to store embeddings of documents. The company must migrate the application to AWS and must implement a solution that provides semantic search of text files. The company has already migrated the text repository to an Amazon S3 bucket.
Which solution will meet these requirements?

정답：B


질문 # 193
A company has significantly increased the amount of data that is stored as .csv files in an Amazon S3 bucket.
Data transformation scripts and queries are now taking much longer than they used to take.
An ML engineer must implement a solution to optimize the data for query performance.
Which solution will meet this requirement with the LEAST operational overhead?

정답：C

설명：
AWS documentation strongly recommends using columnar storage formats to optimize analytical query performance on large datasets stored in Amazon S3. Apache Parquet is a columnar, compressed, and splittable file format that significantly improves query speed and reduces I/O compared to row-based formats such as CSV.
By using AWS Glue to convert CSV files into Parquet format, the company can achieve faster query execution with minimal operational overhead. Glue is fully managed, serverless, and integrates natively with S3, Amazon Athena, and Amazon Redshift Spectrum.
Option A does not improve query efficiency; splitting files still leaves the data in an inefficient row-based format. Option B may reduce data size but does not address the fundamental inefficiency of CSV for analytics. Option D introduces significant operational overhead because Amazon EMR requires cluster provisioning, scaling, and maintenance.
Therefore, converting CSV files to Apache Parquet using AWS Glue ETL is the most efficient and low- maintenance solution.


질문 # 194
An ML engineering team is spread across multiple locations. When the lead ML engineer opens an Amazon SageMaker AI notebook, the ML engineer does not see the latest merged notebook made by other team members from a Git repository.
The lead ML engineer must see the latest SageMaker AI notebook updates.
Which solution will meet this requirement?

정답：C


질문 # 195
A company has an Amazon S3 bucket that contains 1 TB of files from different sources. The S3 bucket contains the following file types in the same S3 folder: CSV, JSON, XLSX, and Apache Parquet.
An ML engineer must implement a solution that uses AWS Glue DataBrew to process the data.
The ML engineer also must store the final output in Amazon S3 so that AWS Glue can consume the output in the future.
Which solution will meet these requirements?

정답：D


질문 # 196
An ML engineering team has a data processing pipeline that ingests sensor data from IoT devices into an Amazon S3 bucket. The pipeline then processes the data by using AWS Glue extract, transform, and load (ETL) jobs for ML modeling. The team noticed throttling errors in the ETL jobs. The data ingestion process has also been slower than normal.
What is the cause of the problem?

정답：D

설명：
The correct answer is A. The AWS Glue service quotas have been reached. AWS Glue is commonly used in ML data preparation pipelines to perform ETL operations before model training or feature engineering. When AWS Glue workloads exceed account-level or Region-level service quotas, jobs can be delayed, queued, throttled, or fail. AWS documentation states that AWS Glue has service quotas, also called limits, for resources and operations in each account and Region. These include quotas for concurrent compute capacity measured in DPUs, crawlers, jobs, triggers, queued job runs, and other Glue resources.
The key clue in the question is "throttling errors in the ETL jobs." AWS specifically explains that Glue API requests are throttled on a per-account, per-Region basis to maintain service performance. If the team is running many jobs, processing a large amount of sensor data, or starting too many Glue job runs concurrently, the workload can exceed the permitted quota and cause throttling.
Option B is less likely because insufficient network bandwidth between IoT devices and the AWS Region would mainly explain slower ingestion into S3, but it would not directly explain AWS Glue ETL throttling errors. Option C might cause poor job performance, but lack of parallel optimization usually causes long runtimes, not service-level throttling. Option D is incorrect because missing Amazon S3 permissions would typically cause access-denied or authorization failures, not throttling. Therefore, the best cause is that the AWS Glue service quotas, such as concurrent job runs, API rate limits, or DPU capacity limits, have been reached.


질문 # 197
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