EXAM MLA-C01 FORMAT | EXAM MLA-C01 PDF

Exam MLA-C01 Format | Exam MLA-C01 PDF

Exam MLA-C01 Format | Exam MLA-C01 PDF

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q41-Q46):

NEW QUESTION # 41
A company is planning to create several ML prediction models. The training data is stored in Amazon S3. The entire dataset is more than 5 ## in size and consists of CSV, JSON, Apache Parquet, and simple text files.
The data must be processed in several consecutive steps. The steps include complex manipulations that can take hours to finish running. Some of the processing involves natural language processing (NLP) transformations. The entire process must be automated.
Which solution will meet these requirements?

  • A. Use Amazon SageMaker Pipelines to create a pipeline of data processing steps. Automate the pipeline by using Amazon EventBridge.
  • B. Process data at each step by using AWS Lambda functions. Automate the process by using AWS Step Functions and Amazon EventBridge.
  • C. Process data at each step by using Amazon SageMaker Data Wrangler. Automate the process by using Data Wrangler jobs.
  • D. Use Amazon SageMaker notebooks for each data processing step. Automate the process by using Amazon EventBridge.

Answer: A

Explanation:
Amazon SageMaker Pipelines is designed for creating, automating, and managing end-to-end ML workflows, including complex data preprocessing tasks. It supports handling large datasets and can integrate with custom steps, such as NLP transformations. By combining SageMaker Pipelines with Amazon EventBridge, the entire workflow can be triggered and automated efficiently, meeting the requirements for scalability, automation, and processing complexity.


NEW QUESTION # 42
A company has historical data that shows whether customers needed long-term support from company staff.
The company needs to develop an ML model to predict whether new customers will require long-term support.
Which modeling approach should the company use to meet this requirement?

  • A. Logistic regression
  • B. Semantic segmentation
  • C. Linear regression
  • D. Anomaly detection

Answer: A

Explanation:
Logistic regression is a suitable modeling approach for this requirement because it is designed for binary classification problems, such as predicting whether a customer will require long-term support ("yes" or "no").
It calculates the probability of a particular class and is widely used for tasks like this where the outcome is categorical.


NEW QUESTION # 43
A company wants to host an ML model on Amazon SageMaker. An ML engineer is configuring a continuous integration and continuous delivery (Cl/CD) pipeline in AWS CodePipeline to deploy the model. The pipeline must run automatically when new training data for the model is uploaded to an Amazon S3 bucket.
Select and order the pipeline's correct steps from the following list. Each step should be selected one time or not at all. (Select and order three.)
* An S3 event notification invokes the pipeline when new data is uploaded.
* S3 Lifecycle rule invokes the pipeline when new data is uploaded.
* SageMaker retrains the model by using the data in the S3 bucket.
* The pipeline deploys the model to a SageMaker endpoint.
* The pipeline deploys the model to SageMaker Model Registry.

Answer:

Explanation:

Explanation:
Step 1: An S3 event notification invokes the pipeline when new data is uploaded.Step 2: SageMaker retrains the model by using the data in the S3 bucket.Step 3: The pipeline deploys the model to a SageMaker endpoint.

* Step 1: An S3 Event Notification Invokes the Pipeline When New Data is Uploaded
* Why?The CI/CD pipeline should be triggered automatically whenever new training data is uploaded to Amazon S3. S3 event notifications can be configured to send events to AWS services like Lambda, which can then invoke AWS CodePipeline.
* How?Configure the S3 bucket to send event notifications (e.g., s3:ObjectCreated:*) to AWS Lambda, which in turn triggers the CodePipeline.
* Step 2: SageMaker Retrains the Model by Using the Data in the S3 Bucket
* Why?The uploaded data is used to retrain the ML model to incorporate new information and maintain performance. This step is critical to updating the model with fresh data.
* How?Define a SageMaker training step in the CI/CD pipeline, which reads the training data from the S3 bucket and retrains the model.
* Step 3: The Pipeline Deploys the Model to a SageMaker Endpoint
* Why?Once retrained, the updated model must be deployed to a SageMaker endpoint to make it available for real-time inference.
* How?Add a deployment step in the CI/CD pipeline, which automates the creation or update of the SageMaker endpoint with the retrained model.
Order Summary:
* An S3 event notification invokes the pipeline when new data is uploaded.
* SageMaker retrains the model by using the data in the S3 bucket.
* The pipeline deploys the model to a SageMaker endpoint.
This configuration ensures an automated, efficient, and scalable CI/CD pipeline for continuous retraining and deployment of the ML model in Amazon SageMaker.


NEW QUESTION # 44
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. F1 score
  • B. Area Under the ROC Curve (AUC)
  • C. Mean absolute error (MAE)
  • D. Accuracy

Answer: C

Explanation:
When predicting continuous variables, such as apartment prices, it's essential to evaluate the model's performance using appropriate regression metrics. The Mean Absolute Error (MAE) is a widely used metric for this purpose.
Understanding Mean Absolute Error (MAE):
MAE measures the average magnitude of errors in a set of predictions, without considering their direction. It calculates the average absolute difference between predicted values and actual values, providing a straightforward interpretation of prediction accuracy.
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Advantages of MAE:
* Interpretability:MAE is expressed in the same units as the target variable, making it easy to understand.
* Robustness to Outliers:Unlike metrics that square the errors (e.g., Mean Squared Error), MAE does not disproportionately penalize larger errors, making it more robust to outliers.
Comparison with Other Metrics:
* Accuracy, AUC, F1 Score:These metrics are designed for classification tasks, where the goal is to predict discrete labels. They are not suitable for regression problems involving continuous target variables.
* Mean Squared Error (MSE):While MSE also measures prediction errors, it squares the differences, giving more weight to larger errors. This can be useful in certain contexts but may be sensitive to outliers.
Conclusion:
For evaluating the performance of a model predicting apartment prices-a continuous variable-MAE is an appropriate and effective metric. It provides a clear indication of the average prediction error in the same units as the target variable, facilitating straightforward interpretation and comparison.
References:
* Regression Metrics - GeeksforGeeks
* Evaluation Metrics for Your Regression Model - Analytics Vidhya
* Regression Metrics for Machine Learning - Machine Learning Mastery


NEW QUESTION # 45
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.
After the data is aggregated, the ML engineer must implement a solution to automatically detect anomalies in the data and to visualize the result.
Which solution will meet these requirements?

  • A. Use Amazon Athena to automatically detect the anomalies and to visualize the result.
  • B. Use AWS Batch to automatically detect the anomalies. Use Amazon QuickSight to visualize the result.
  • C. Use Amazon Redshift Spectrum to automatically detect the anomalies. Use Amazon QuickSight to visualize the result.
  • D. Use Amazon SageMaker Data Wrangler to automatically detect the anomalies and to visualize the result.

Answer: D

Explanation:
Amazon SageMaker Data Wrangler is a comprehensive tool that streamlines the process of data preparation and offers built-in capabilities for anomaly detection and visualization.
Key Features of SageMaker Data Wrangler:
* Data Importation: Connects seamlessly to various data sources, including Amazon S3 and on- premises databases, facilitating the aggregation of transaction logs, customer profiles, and MySQL tables.
* Anomaly Detection: Provides built-in analyses to detect anomalies in time series data, enabling the identification of outliers that may indicate fraudulent activities.
* Visualization: Offers a suite of visualization tools, such as histograms and scatter plots, to help understand data distributions and relationships, which are crucial for feature engineering and model development.
Implementation Steps:
* Data Aggregation:
* Import data from Amazon S3 and on-premises MySQL databases into SageMaker Data Wrangler.
* Utilize Data Wrangler's data flow interface to combine and preprocess datasets, ensuring a unified dataset for analysis.
* Anomaly Detection:
* Apply the anomaly detection analysis feature to identify outliers in the dataset.
* Configure parameters such as the anomaly threshold to fine-tune the detection sensitivity.
* Visualization:
* Use built-in visualization tools to create charts and graphs that depict data distributions and highlight anomalies.
* Interpret these visualizations to gain insights into potential fraud patterns and feature interdependencies.
Advantages of Using SageMaker Data Wrangler:
* Integrated Workflow: Combines data preparation, anomaly detection, and visualization within a single interface, streamlining the ML development process.
* Operational Efficiency: Reduces the need for multiple tools and complex integrations, thereby minimizing operational overhead.
* Scalability: Handles large datasets efficiently, making it suitable for extensive transaction logs and customer profiles.
By leveraging SageMaker Data Wrangler, the ML engineer can effectively detect anomalies and visualize results, facilitating the development of a robust fraud detection model.
References:
* Analyze and Visualize - Amazon SageMaker
* Transform Data - Amazon SageMaker


NEW QUESTION # 46
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