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Amazon Web Services Updated MLS-C01 Exam Questions and Answers by zayaan

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Amazon Web Services MLS-C01 Exam Overview :

Exam Name: AWS Certified Machine Learning - Specialty
Exam Code: MLS-C01 Dumps
Vendor: Amazon Web Services Certification: AWS Certified Specialty
Questions: 307 Q&A's Shared By: zayaan
Question 48

A retail company wants to update its customer support system. The company wants to implement automatic routing of customer claims to different queues to prioritize the claims by category.

Currently, an operator manually performs the category assignment and routing. After the operator classifies and routes the claim, the company stores the claim’s record in a central database. The claim’s record includes the claim’s category.

The company has no data science team or experience in the field of machine learning (ML). The company’s small development team needs a solution that requires no ML expertise.

Which solution meets these requirements?

Options:

A.

Export the database to a .csv file with two columns: claim_label and claim_text. Use the Amazon SageMaker Object2Vec algorithm and the .csv file to train a model. Use SageMaker to deploy the model to an inference endpoint. Develop a service in the application to use the inference endpoint to process incoming claims, predict the labels, and route the claims to the appropriate queue.

B.

Export the database to a .csv file with one column: claim_text. Use the Amazon SageMaker Latent Dirichlet Allocation (LDA) algorithm and the .csv file to train a model. Use the LDA algorithm to detect labels automatically. Use SageMaker to deploy the model to an inference endpoint. Develop a service in the application to use the inference endpoint to process incoming claims, predict the labels, and route the claims to the appropriate queue.

C.

Use Amazon Textract to process the database and automatically detect two columns: claim_label and claim_text. Use Amazon Comprehend custom classification and the extracted information to train the custom classifier. Develop a service in the application to use the Amazon Comprehend API to process incoming claims, predict the labels, and route the claims to the appropriate queue.

D.

Export the database to a .csv file with two columns: claim_label and claim_text. Use Amazon Comprehend custom classification and the .csv file to train the custom classifier. Develop a service in the application to use the Amazon Comprehend API to process incoming claims, predict the labels, and route the claims to the appropriate queue.

Discussion
Question 49

A manufacturing company has structured and unstructured data stored in an Amazon S3 bucket A Machine Learning Specialist wants to use SQL to run queries on this data. Which solution requires the LEAST effort to be able to query this data?

Options:

A.

Use AWS Data Pipeline to transform the data and Amazon RDS to run queries.

B.

Use AWS Glue to catalogue the data and Amazon Athena to run queries

C.

Use AWS Batch to run ETL on the data and Amazon Aurora to run the quenes

D.

Use AWS Lambda to transform the data and Amazon Kinesis Data Analytics to run queries

Discussion
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Question 50

A Data Scientist is training a multilayer perception (MLP) on a dataset with multiple classes. The target class of interest is unique compared to the other classes within the dataset, but it does not achieve and acceptable ecall metric. The Data Scientist has already tried varying the number and size of the MLP’s hidden layers,

which has not significantly improved the results. A solution to improve recall must be implemented as quickly as possible.

Which techniques should be used to meet these requirements?

Options:

A.

Gather more data using Amazon Mechanical Turk and then retrain

B.

Train an anomaly detection model instead of an MLP

C.

Train an XGBoost model instead of an MLP

D.

Add class weights to the MLP’s loss function and then retrain

Discussion
Question 51

A credit card company wants to identify fraudulent transactions in real time. A data scientist builds a machine learning model for this purpose. The transactional data is captured and stored in Amazon S3. The historic data is already labeled with two classes: fraud (positive) and fair transactions (negative). The data scientist removes all the missing data and builds a classifier by using the XGBoost algorithm in Amazon SageMaker. The model produces the following results:

• True positive rate (TPR): 0.700

• False negative rate (FNR): 0.300

• True negative rate (TNR): 0.977

• False positive rate (FPR): 0.023

• Overall accuracy: 0.949

Which solution should the data scientist use to improve the performance of the model?

Options:

A.

Apply the Synthetic Minority Oversampling Technique (SMOTE) on the minority class in the training dataset. Retrain the model with the updated training data.

B.

Apply the Synthetic Minority Oversampling Technique (SMOTE) on the majority class in the training dataset. Retrain the model with the updated training data.

C.

Undersample the minority class.

D.

Oversample the majority class.

Discussion
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