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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified Machine Learning Engineer โ Associate (MLA-C01) |
| Exam Number: | MLA-C01 |
| Real Exam Qty: | 65 scored questions + 15 unscored questions |
| Related Certifications: | AWS Certified AI Practitioner AWS Certified DevOps Engineer โ Professional AWS Certified Solutions Architect โ Associate AWS Certified Data Engineer โ Associate |
| Available Languages: | English, Korean, Japanese, Simplified Chinese |
| Certificate Validity Period: | 3 years |
| Exam Duration: | 130 minutes |
| Exam Price: | USD 150 |
| Exam Format: | Multiple response, Matching, Multiple choice, Ordering |
| Passing Score: | 720/1000 |
| 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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NEW QUESTION # 214
Hotspot Question
An ecommerce company is using Amazon SageMaker Clarify Foundation Model Evaluations (FMEval) to evaluate ML models.
Select the correct model evaluation task from the following list for each ecommerce use case.
Each model evaluation task should be selected one time.
- Classification evaluation
- Open-ended generation
- Question answering
- Text summarization
Answer:
Explanation:
NEW QUESTION # 215
An ML engineer is evaluating several ML models and must choose one model to use in production. The cost of false negative predictions by the models is much higher than the cost of false positive predictions.
Which metric finding should the ML engineer prioritize the MOST when choosing the model?
Answer: B
NEW QUESTION # 216
A bank needs to use Amazon SageMaker AI to create an ML model to determine which customers qualify for a new product. The bank must use algorithms that SageMaker AI directly supports. The model must be explainable to the bank ' s regulators.
Which modeling approach will meet these requirements?
Answer: D
Explanation:
Option B is correct because the bank needs a SageMaker-supported algorithm and the resulting model must be explainable for regulators. AWS documentation states that the Amazon SageMaker AI linear learner algorithm is a built-in algorithm that provides a solution for classification and regression problems
. Since determining whether customers qualify for a new product is a classification-style decision problem, linear learner fits the business requirement directly.
The explainability requirement strongly favors a simpler and more interpretable model family. AWS Prescriptive Guidance on model interpretability states that simple models are considered more interpretable because the effects of input features on the output are easier to understand. It specifically gives linear regression as an example where the magnitudes of coefficients provide a clear global feature-importance signal. While the question does not explicitly mention SageMaker Clarify, AWS documentation also says SageMaker offers explainability features to help interpret predictions, which complements inherently interpretable model types such as linear models.
The other options are weaker fits. Object2Vec is mainly for learning embeddings and similarity relationships rather than a straightforward explainable eligibility model. A neural network may work technically, but it is generally less interpretable for regulatory review. k-means is an unsupervised clustering algorithm and is not suitable for predicting whether a customer qualifies for a product. Because the bank wants a directly supported SageMaker algorithm and a model that is easier to explain to regulators, the best AWS-aligned answer is B, linear learner .
NEW QUESTION # 217
An ML engineer is working on an ML model to predict the prices of similarly sized homes. The model will base predictions on several features The ML engineer will use the following feature engineering techniques to estimate the prices of the homes:
* Feature splitting
* Logarithmic transformation
* One-hot encoding
* Standardized distribution
Select the correct feature engineering techniques for the following list of features. Each feature engineering technique should be selected one time or not at all (Select three.)
Answer:
Explanation:
Explanation:
* City (name): One-hot encoding
* Type_year (type of home and year the home was built): Feature splitting
* Size of the building (square feet or square meters): Standardized distribution
* City (name): One-hot encoding
* Why? The "City" is a categorical feature (non-numeric), so one-hot encoding is used to transform it into a numeric format. This encoding creates binary columns for each unique category (e.g., cities like "New York" or "Los Angeles"), which the model can interpret.
* Type_year (type of home and year the home was built): Feature splitting
* Why? "Type_year" combines two pieces of information into one column, which could confuse the model. Feature splitting separates this column into two distinct features: "Type of home" and
"Year built," enabling the model to process each feature independently.
* Size of the building (square feet or square meters): Standardized distribution
* Why? Size is a continuous numerical variable, and standardization (scaling the feature to have a mean of 0 and a standard deviation of 1) ensures that the model treats it fairly compared to other features, avoiding bias from differences in feature scale.
By applying these feature engineering techniques, the ML engineer can ensure that the input data is correctly formatted and optimized for the model to make accurate predictions.
NEW QUESTION # 218
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.
Before the ML engineer trains the model, the ML engineer must resolve the issue of the imbalanced data.
Which solution will meet this requirement with the LEAST operational effort?
Answer: C
NEW QUESTION # 219
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