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The AWS Certified Machine Learning Engineer - Associate is ideal whether you're just beginning your career in open source or planning to advance your career. Moreover, the AWS Certified Machine Learning Engineer - Associate also serves as a great stepping stone to earning advanced AWS Certified Machine Learning Engineer - Associate. Success in the MLA-C01 exam is the basic requirement to get the a good job. You get multiple career benefits after cracking the AWS Certified Machine Learning Engineer - Associate. These benefits include skills approval, high-paying jobs, and promotions. Read on to find more important details about the Amazon MLA-C01 Exam Questions.
| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified Machine Learning Engineer - Associate |
| Exam Number: | MLA-C01 |
| Passing Score: | 720 (scaled score 100–1000) |
| Related Certifications: | AWS Certified Machine Learning - Specialty AWS Certified AI Practitioner |
| Real Exam Qty: | 65 (50 scored, 15 unscored) |
| Exam Format: | Case study, Multiple choice, Multiple response, Ordering, Matching |
| Available Languages: | Japanese, Simplified Chinese, English, Korean |
| Exam Price: | 150 USD |
| Exam Duration: | 130 minutes |
| Certificate Validity Period: | 3 years |
| Recommended Training: | AWS Certified Machine Learning Engineer - Associate Official Exam Guide AWS Training and Certification |
| Exam Registration: | AWS Certification Portal Pearson VUE Registration |
| Sample Questions: | Amazon MLA-C01 Sample Questions |
| Exam Way: | Online proctored or onsite at Pearson VUE testing centers |
| Pre Condition: | Recommended: 1+ year hands-on experience with AWS services and machine learning engineering; familiarity with Amazon SageMaker and related ML services. No mandatory prerequisite exams. |
| Official Syllabus URL: | https://docs.aws.amazon.com/aws-certification/latest/machine-learning-engineer-associate-01/machine-learning-engineer-associate-01.html |
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NEW QUESTION # 35
A company uses an NFS-based data store to store data for ML training. Linux-based systems access the data store.
The company needs a hybrid system to make the shared data store accessible to on-premises servers and Amazon SageMaker AI notebooks that will consume the data. File locking is required for the data producers.
Which AWS storage solution will meet these requirements?
Answer: A
Explanation:
Option B is correct because Amazon EFS is a managed NFS-based shared file system that supports Linux clients, hybrid access, and file-system semantics such as file locking . AWS documentation states that Amazon EFS provides strong data consistency and file locking , which is an explicit requirement in the question. AWS also states that you can mount EFS file systems on on-premises data center servers when connected to your Amazon VPC through AWS Direct Connect or Site-to-Site VPN , which directly satisfies the hybrid access requirement.
For the SageMaker side, AWS documentation states that SageMaker AI creates a default Amazon EFS volume for Studio domains, and you can also add a custom Amazon EFS file system so domain users can access it in SageMaker Studio. AWS also notes that SageMaker AI Studio notebooks use Amazon EFS as a persistent storage layer. That makes EFS the most natural AWS storage service for a shared file repository that must be accessible from SageMaker notebooks.
The other choices are weaker. Amazon S3 with Mountpoint is object storage access and is not the best answer for a shared NFS-style data store with file locking requirements. FSx for Lustre is high-performance parallel file storage and can support on-premises bursting, but the question explicitly starts from an NFS- based shared data store, and EFS is AWS's native NFS file service. Amazon EBS cannot be mounted simultaneously across on-premises servers and SageMaker notebooks as a shared, managed hybrid file system. Therefore, the best verified AWS-docs answer is B .
NEW QUESTION # 36
An ML engineer is building a logistic regression model to predict customer churn for subscription services.
The dataset contains two string variables: location and job_seniority_level.
The location variable has 3 distinct values, and the job_seniority_level variable has over 10 distinct values.
The ML engineer must perform preprocessing on the variables.
Which solution will meet this requirement?
Answer: B
Explanation:
Logistic regression requires numeric input features and is sensitive to how categorical variables are encoded.
AWS feature engineering best practices recommend one-hot encoding for low-cardinality categorical variables with no inherent order and ordinal encoding for categorical variables with a meaningful order.
The location feature has only three distinct values and no ordinal relationship, making one-hot encoding the most appropriate method. This prevents the model from inferring a false numerical relationship between locations.
The job_seniority_level feature typically has an inherent order (for example: junior, mid-level, senior, lead).
Even with more than 10 categories, ordinal encoding preserves this natural hierarchy while keeping the feature dimensionality manageable.
Tokenization is used for unstructured text, not structured categorical variables. Standard scaling applies only to continuous numeric features and is not suitable for categorical string variables.
AWS documentation explicitly highlights using one-hot encoding for nominal features and ordinal encoding for ordered categorical features when preparing data for linear models such as logistic regression.
Therefore, Option B is the correct and AWS-aligned solution.
NEW QUESTION # 37
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.
The training dataset includes categorical data and numerical data. The ML engineer must prepare the training dataset to maximize the accuracy of the model.
Which action will meet this requirement with the LEAST operational overhead?
Answer: D
Explanation:
Preparing a training dataset that includes both categorical and numerical data is essential for maximizing the accuracy of a machine learning model. Transforming categorical data into numerical format is a critical step, as most ML algorithms require numerical input.
Why Transform Categorical Data into Numerical Data?
* Model Compatibility: Many ML algorithms cannot process categorical data directly and require numerical representations.
* Improved Performance: Proper encoding of categorical variables can enhance model accuracy and convergence speed.
Why Use Amazon SageMaker Data Wrangler?
Amazon SageMaker Data Wrangler offers a visual interface with over 300 built-in data transformations, including tools for encoding categorical variables.
Implementation Steps:
* Import Data:
* Load the dataset into SageMaker Data Wrangler from sources like Amazon S3 or on-premises databases.
* Identify Categorical Features:
* Use Data Wrangler's data type inference to detect categorical columns.
* Apply Categorical Encoding:
* Choose appropriate encoding techniques (e.g., one-hot encoding or ordinal encoding) from Data Wrangler's transformation options.
* Apply the selected transformation to convert categorical features into numerical format.
* Validate Transformations:
* Review the transformed dataset to ensure accuracy and completeness.
Advantages of Using SageMaker Data Wrangler:
* Ease of Use: Provides a user-friendly interface for data transformation without extensive coding.
* Operational Efficiency: Integrates data preparation steps, reducing the need for multiple tools and minimizing operational overhead.
* Flexibility: Supports various data sources and transformation techniques, accommodating diverse datasets.
By utilizing SageMaker Data Wrangler to transform categorical data into numerical format, the ML engineer can efficiently prepare the dataset, thereby enhancing the model's accuracy with minimal operational overhead.
References:
* Transform Data - Amazon SageMaker
* Prepare ML Data with Amazon SageMaker Data Wrangler
NEW QUESTION # 38
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?
Answer: C
Explanation:
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.
NEW QUESTION # 39
An ML engineer needs to deploy four ML models in an Amazon SageMaker inference pipeline.
The models were built with different frameworks. The ML engineer also needs to give clients the ability to use the invoke_endpoint call to perform inference for each model. Which solution will meet these requirements MOST cost-effectively?
Answer: C
Explanation:
A SageMaker multi-container endpoint allows deployment of multiple models built with different frameworks in a single endpoint. Each container can host a model with its required framework, and clients can use the same invoke_endpoint call while specifying the target container. This meets the requirement for framework diversity and is more cost-effective than running separate single-model endpoints.
NEW QUESTION # 40
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