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Amazon MLA-C01 Dumps
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Amazon MLA-C01 Frequently Asked Questions
Amazon MLA-C01 Sample Questions
Question # 1
A company uses Amazon SageMaker for its ML workloads. The company's ML engineer receives a 50 MB Apache Parquet data file to build a fraud detection model. The file includes several correlated columns that are not required. What should the ML engineer do to drop the unnecessary columns in the file with the LEAST effort?
A. Download the file to a local workstation. Perform one-hot encoding by using a custom Python script.
B. Create an Apache Spark job that uses a custom processing script on Amazon EMR.
C. Create a SageMaker processing job by calling the SageMaker Python SDK.
D. Create a data flow in SageMaker Data Wrangler. Configure a transform step.
Question # 2
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?
A. Use Amazon Athena to identify patterns that contribute to the imbalance. Adjust the dataset accordingly.
B. Use Amazon SageMaker Studio Classic built-in algorithms to process the imbalanced dataset.
C. Use AWS Glue DataBrew built-in features to oversample the minority class.
D. Use the Amazon SageMaker Data Wrangler balance data operation to oversample the minority class.
Question # 3
A company has deployed an ML model that detects fraudulent credit card transactions in real time in a banking application. The model uses Amazon SageMaker Asynchronous Inference. Consumers are reporting delays in receiving the inference results. An ML engineer needs to implement a solution to improve the inference performance. The solution also must provide a notification when a deviation in model quality occurs. Which solution will meet these requirements?
A. Use SageMaker real-time inference for inference. Use SageMaker Model Monitor for notifications about model quality.
B. Use SageMaker batch transform for inference. Use SageMaker Model Monitor for notifications about model quality.
C. Use SageMaker Serverless Inference for inference. Use SageMaker Inference Recommender for notifications about model quality.
D. Keep using SageMaker Asynchronous Inference for inference. Use SageMaker Inference Recommender for notifications about model quality.
Question # 4
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. Anomaly detection
B. Linear regression
C. Logistic regression
D. Semantic segmentation
Question # 5
A company is creating an application that will recommend products for customers to purchase. The application will make API calls to Amazon Q Business. The company must ensure that responses from Amazon Q Business do not include the name of the company's main competitor. Which solution will meet this requirement?
A. Configure the competitor's name as a blocked phrase in Amazon Q Business.
B. Configure an Amazon Q Business retriever to exclude the competitor’s name.
C. Configure an Amazon Kendra retriever for Amazon Q Business to build indexes that exclude the competitor's name.
D. Configure document attribute boosting in Amazon Q Business to deprioritize the competitor's name.
Question # 6
An ML engineer is training a simple neural network model. The ML engineer tracks the performance of the model over time on a validation dataset. The model's performance improves substantially at first and then degrades after a specific number of epochs.Which solutions will mitigate this problem? (Choose two.)
A. Enable early stopping on the model.
B. Increase dropout in the layers.
C. Increase the number of layers.
D. Increase the number of neurons.
E. Investigate and reduce the sources of model bias.
Question # 7
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. Process data at each step by using Amazon SageMaker Data Wrangler. Automate the process by using Data Wrangler jobs.
B. Use Amazon SageMaker notebooks for each data processing step. Automate the process by using Amazon EventBridge.
C. Process data at each step by using AWS Lambda functions. Automate the process by using AWS Step Functions and Amazon EventBridge.
D. Use Amazon SageMaker Pipelines to create a pipeline of data processing steps. Automate the pipeline by using Amazon EventBridge.
Question # 8
An ML engineer needs to use AWS CloudFormation to create an ML model that an Amazon SageMaker endpoint will host. Which resource should the ML engineer declare in the CloudFormation template to meet this requirement?
A. AWS::SageMaker::Model
B. AWS::SageMaker::Endpoint
C. AWS::SageMaker::NotebookInstance
D. AWS::SageMaker::Pipeline
Question # 9
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. Area Under the ROC Curve (AUC)
C. F1 score
D. Mean absolute error (MAE)
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