Free Amazon MLS-C01 Exam Dumps Questions & Answers

A Machine Learning Specialist is required to build a supervised image-recognition model to identify a cat.
The ML Specialist performs some tests and records the following results for a neural network-based image classifier:
Total number of images available = 1,000 Test set images = 100 (constant test set) The ML Specialist notices that, in over 75% of the misclassified images, the cats were held upside down by their owners.
Which techniques can be used by the ML Specialist to improve this specific test error?

• A.  Increase the training data by adding variation in rotation for training images.
• B.  Increase the dropout rate for the second-to-last layer.
• C.  Increase the number of epochs for model training.
• D.  Increase the number of layers for the neural network.

A machine learning (ML) specialist is developing a model for a company. The model will classify and predict sequences of objects that are displayed in a video. The ML specialist decides to use a hybrid architecture that consists of a convolutional neural network (CNN) followed by a classifier three-layer recurrent neural network (RNN).
The company developed a similar model previously but trained the model to classify a different set of objects.
The ML specialist wants to save time by using the previously trained model and adapting the model for the current use case and set of objects.
Which combination of steps will accomplish this goal with the LEAST amount of effort? (Select TWO.)

• A.  Reinitialize the weights of the last layer of the RNN. Retrain the entire model on the prediction task by using the new set of objects.
• B.  Reinitialize the weights of the entire network. Retrain the entire network on the prediction task by using the new set of objects.
• C.  Reinitialize the weights of the entire CNN. Retrain the CNN on the classification task by using the new set of objects.
• D.  Reinitialize the weights of the last fully connected layer of the CNN. Retrain the CNN on the classification task by using the new set of objects.
• E.  Reinitialize the weights of the entire RNN. Retrain the entire model on the prediction task by using the new set of objects.

A monitoring service generates 1 TB of scale metrics record data every minute A Research team performs queries on this data using Amazon Athena The queries run slowly due to the large volume of data, and the team requires better performance How should the records be stored in Amazon S3 to improve query performance?

• A.  RecordIO
• B.  CSV files
• C.  Compressed JSON
• D.  Parquet files

A company is building a predictive maintenance model for its warehouse equipment. The model must predict the probability of failure of all machines in the warehouse. The company has collected 10.000 event samples within 3 months. The event samples include 100 failure cases that are evenly distributed across 50 different machine types.
How should the company prepare the data for the model to improve the model's accuracy?

• A.  Oversample the failure cases by using the Synthetic Minority Oversampling Technique (SMOTE).
• B.  Undersample the non-failure events. Stratify the non-failure events by machine type.
• C.  Undersample the non-failure events by using the Synthetic Minority Oversampling Technique (SMOTE).
• D.  Adjust the class weight to account for each machine type.

A retail company wants to combine its customer orders with the product description data from its product catalog. The structure and format of the records in each dataset is different. A data analyst tried to use a spreadsheet to combine the datasets, but the effort resulted in duplicate records and records that were not properly combined. The company needs a solution that it can use to combine similar records from the two datasets and remove any duplicates.
Which solution will meet these requirements?

• A.  Create AWS Glue crawlers for reading and populating the AWS Glue Data Catalog. Use the FindMatches transform to cleanse the data.
• B.  Use an AWS Lambda function to process the data. Use two arrays to compare equal strings in the fields from the two datasets and remove any duplicates.
• C.  Create an AWS Lake Formation custom transform. Run a transformation for matching products from the Lake Formation console to cleanse the data automatically.
• D.  Create AWS Glue crawlers for reading and populating the AWS Glue Data Catalog. Call the AWS Glue SearchTables API operation to perform a fuzzy-matching search on the two datasets, and cleanse the data accordingly.