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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Preparation for Machine Learning (ML) | 28% | - Data preprocessing and transformation
|
| Topic 2: ML Solution Monitoring, Maintenance, and Security | 24% | - Security and governance
|
| Topic 3: ML Model Development | 26% | - Model tuning and evaluation
|
| Topic 4: Deployment and Orchestration of ML Workflows | 22% | - Model deployment
|
>> Standard MLA-C01 Answers <<
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NEW QUESTION # 118
A company wants to use large language models (LLMs) supported by Amazon Bedrock to develop a chat interface for internal technical documentation.
The documentation consists of dozens of text files totaling several megabytes and is updated frequently.
Which solution will meet these requirements MOST cost-effectively?
Answer: D
Explanation:
AWS recommends Retrieval Augmented Generation (RAG) using Amazon Bedrock knowledge bases as the most cost-effective solution for incorporating frequently updated documents into LLM-powered applications.
A Bedrock knowledge base allows the company to store documents in Amazon S3, index them using vector embeddings, and retrieve relevant context dynamically at inference time. This approach eliminates the need for retraining or fine-tuning the model when documents change.
Training or fine-tuning an LLM is expensive, time-consuming, and unnecessary for frequently changing data.
Bedrock guardrails are designed for safety and policy enforcement, not knowledge integration.
AWS documentation explicitly states that knowledge bases are the preferred method for dynamic, updatable enterprise content in chat applications.
Therefore, Option D is the correct and most cost-effective solution.
NEW QUESTION # 119
An ML engineer is setting up an Amazon SageMaker AI pipeline for an ML model. The pipeline must automatically initiate a re-training job if any data drift is detected.
How should the ML engineer set up the pipeline to meet this requirement?
Answer: D
Explanation:
The correct answer is C. Use SageMaker Model Monitor to detect data drift. Use an AWS Lambda function to automate the re-training job.
Amazon SageMaker Model Monitor is the AWS-recommended solution for automatically detecting data drift in ML pipelines. Data drift occurs when the statistical properties of input features change over time, potentially reducing model accuracy. Model Monitor continuously analyzes incoming data and compares it to the baseline training dataset to identify deviations. It can track both feature distributions and prediction quality metrics.
Once Model Monitor detects data drift, it can trigger automated workflows using AWS Lambda or Amazon EventBridge. An AWS Lambda function can initiate a SageMaker training job to re-train the model using updated datasets, ensuring the ML model remains accurate and reliable. This setup fully automates the response to drift events, meeting the requirement of automatically initiating re-training.
Option A (AWS Glue) is designed for ETL processes but does not natively detect ML-specific data drift.
Option B (Amazon Managed Service for Apache Flink) can process streaming data but does not provide native drift detection for ML pipelines. Option D (Amazon QuickSight anomaly detection) focuses on business intelligence and visual anomaly detection, not automated re-training workflows for ML models.
By integrating SageMaker Model Monitor with Lambda, the ML engineer can maintain model performance proactively, implement automated re-training, and align with AWS best practices for ML solution monitoring, maintenance, and security. This approach ensures continuous validation of model inputs and outputs, reduces operational overhead, and prevents degradation in production ML performance due to unseen data changes.
NEW QUESTION # 120
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?
Answer: C
Explanation:
Option A is correct because the issue is that the lead ML engineer's local notebook environment does not yet contain the latest merged changes from the shared Git repository. In Git, the operation used to fetch remote updates and merge them into the local branch is git pull . AWS documentation for SageMaker notebook collaboration explains that Git repositories can be associated with SageMaker notebook instances so distributed teams can share notebooks and collaborate in a source-control environment.
AWS documentation is even more explicit in the SageMaker Git operations guide: to fetch notebooks committed by other users , you should do a pull from the project repository . That is exactly the situation described in the question. Other team members have already merged their notebook changes into the repository, and the lead engineer needs the latest notebook updates in the local SageMaker notebook environment. Therefore, running !git pull origin master is the correct command among the available choices.
The other options do not solve the problem. git commit records local changes in the user's own repository history; it does not download new remote content. git push origin master uploads local commits to the remote repository; it is used when you want to send your own changes outward, not retrieve teammates' updates. git branch only lists or manages branches and does not synchronize notebook content. Because the requirement is to see the latest merged notebook made by others , the necessary operation is a pull from the remote repository. So the best verified answer is A .
NEW QUESTION # 121
A company wants to predict the success of advertising campaigns by considering the color scheme of each advertisement. An ML engineer is preparing data for a neural network model.
The dataset includes color information as categorical data.
Which technique for feature engineering should the ML engineer use for the model?
Answer: A
NEW QUESTION # 122
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.)
Answer: D,E
Explanation:
Both binary encoding and label encoding convert categorical yes/no responses into numerical values without increasing dimensionality. For example, mapping yes โ 1 and no โ 0. Unlike one-hot encoding, which would add extra dimensions, these methods keep the dataset compact and effective for training.
NEW QUESTION # 123
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