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Amazon MLA-C01 시험요강:

주제	소개
주제 1	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 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.
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 (Q203-Q208):

질문 # 203
An ML engineer needs to use an ML model to predict the price of apartments in a specific location.
Which metric should the ML engineer use to evaluate the model's performance?

• A. Accuracy
• B. Mean absolute error (MAE)
• C. Area Under the ROC Curve (AUC)
• D. F1 score

정답：B

 

질문 # 204
A government agency is conducting a national census to assess program needs by area and city. The census form collects approximately 500 responses from each citizen. The agency needs to analyze the data to extract meaningful insights. The agency wants to reduce the dimensions of the high-dimensional data to uncover hidden patterns.
Which solution will meet these requirements?

• A. Use the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm in Amazon SageMaker AI.
• B. Use the principal component analysis (PCA) algorithm in Amazon SageMaker AI.
• C. Use the Random Cut Forest (RCF) algorithm in Amazon SageMaker AI.
• D. Use the k-means algorithm in Amazon SageMaker AI.

정답：B

설명：
The primary requirement is dimensionality reduction on high-dimensional structured data to uncover hidden patterns. Principal Component Analysis (PCA) is a linear dimensionality reduction technique specifically designed for this purpose and is available as a built-in algorithm in Amazon SageMaker.
PCA transforms the original features into a smaller set of orthogonal components that preserve the maximum possible variance. This makes PCA ideal for large tabular datasets such as census data, where hundreds of correlated variables are common.
t-SNE (Option B) is mainly used for visualization in very low dimensions (2D or 3D) and does not scale well for large datasets or production analysis. k-means (Option C) is a clustering algorithm, not a dimensionality reduction method. Random Cut Forest (Option D) is used for anomaly detection.
Therefore, PCA is the correct and AWS-recommended solution.

 

질문 # 205
An ML engineer is developing a neural network to run on new user data. The dataset has dozens of floating- point features. The dataset is stored as CSV objects in an Amazon S3 bucket. Most objects and columns are missing at least one value. All features are relatively uniform except for a small number of extreme outliers.
The ML engineer wants to use Amazon SageMaker Data Wrangler to handle missing values before passing the dataset to the neural network.
Which solution will provide the MOST complete data?

• A. Drop samples that are missing values.
• B. Impute missing values with the mean value.
• C. Drop columns that are missing values.
• D. Impute missing values with the median value.

정답：D

설명：
The primary goal is to produce the most complete dataset while handling missing values and extreme outliers appropriately. Dropping samples (Option A) or columns (Option D) would reduce data completeness and potentially remove valuable information, which contradicts the requirement.
Imputation is therefore the correct approach. Between mean and median imputation, AWS ML best practices recommend using the median when features contain outliers. The mean is sensitive to extreme values and can be skewed significantly, leading to imputed values that are not representative of the typical data distribution.
In contrast, the median is robust to outliers, making it a better statistical estimator for central tendency in such datasets.
Amazon SageMaker Data Wrangler supports median imputation as a built-in transformation, enabling ML engineers to handle missing values consistently across large tabular datasets without custom code. This approach preserves all rows and columns while minimizing distortion caused by extreme values, which is particularly important for neural networks that are sensitive to input distributions.
Therefore, imputing missing values with the median value provides the most complete and statistically appropriate dataset for training.

 

질문 # 206
An ML engineer has trained a neural network by using stochastic gradient descent (SGD). The neural network performs poorly on the test set. The values for training loss and validation loss remain high and show an oscillating pattern. The values decrease for a few epochs and then increase for a few epochs before repeating the same cycle.
What should the ML engineer do to improve the training process?

• A. Increase the learning rate.
• B. Increase the size of the test set.
• C. Decrease the learning rate.
• D. Introduce early stopping.

정답：C

설명：
An oscillating loss pattern during training with stochastic gradient descent (SGD) is a strong indicator that the learning rate is too high. When the learning rate is excessive, the optimizer takes overly large steps during gradient updates, causing the model to repeatedly overshoot the optimal minimum of the loss function. This results in unstable convergence behavior, where training and validation loss decrease briefly and then increase again in a repeating cycle.
AWS Machine Learning documentation and general deep learning best practices recommend reducing the learning rate when training loss and validation loss both remain high and fluctuate rather than steadily decreasing. Lowering the learning rate allows the optimizer to take smaller, more precise steps toward the minimum, leading to smoother convergence and improved generalization on the test dataset.
Option A, early stopping, is used primarily to prevent overfitting when validation loss increases while training loss continues to decrease. In this scenario, both losses remain high and unstable, indicating an optimization issue rather than overfitting.
Option B is incorrect because increasing the test set size does not affect the training dynamics or convergence behavior of the model.
Option C would worsen the problem, as increasing the learning rate would further amplify oscillations and instability.
Therefore, decreasing the learning rate is the correct corrective action to stabilize SGD training and improve model performance.

 

질문 # 207
An ML engineer needs to use AWS services to identify and extract meaningful unique keywords from documents.
Which solution will meet these requirements with the LEAST operational overhead?

• A. Use the Natural Language Toolkit (NLTK) library on Amazon EC2 instances for text pre- processing. Use the Latent Dirichlet Allocation (LDA) algorithm to identify and extract relevant keywords.
• B. Use Amazon Comprehend custom entity recognition and key phrase extraction to identify and extract relevant keywords.
• C. Use Amazon SageMaker and the BlazingText algorithm. Apply custom pre-processing steps for stemming and removal of stop words. Calculate term frequency-inverse document frequency (TF- IDF) scores to identify and extract relevant keywords.
• D. Store the documents in an Amazon S3 bucket. Create AWS Lambda functions to process the documents and to run Python scripts for stemming and removal of stop words. Use bigram and trigram techniques to identify and extract relevant keywords.

정답：B

 

질문 # 208
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