Question 1

An e commerce company wants to launch a new cloud-based product recommendation feature for its web application. Due to data localization regulations, an sensitive data must not leave its on-premises data center, and the product recommendation model must be trained and tested using nonsensitive data only. transfer to the cloud must use IPsec. The web application is hosted on premises with a PostgreSQL database that contains all the data. The company wants data to be uploaded securely to Amazon S3 each day for model retraining.

How should a machine learning specialist meet these requirements?

Accepted Answer

Correct answer: C

Question 2

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 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?

Accepted Answer

Correct answer: C

Question 3

A company ingests machine learning (ML) data from web advertising clicks into an Amazon S3 data lake. Click data is added to an Amazon Kinesis data stre using the Kinesis Producer Library (KPL). The data is loaded into the S3 data lake from the data stream by using an Amazon Kinesis Data Firehose deliver stream. As the data volume increases, an ML specialist notices that the rate of data ingested into Amazon S3 is relatively constant. There also is an in backlog of data for Kinesis Data Streams and Kinesis Data Firehose to ingest.

Which next step is MOST likely to improve the data ingestion rate into Amazon S3?

Accepted Answer

Correct answer: C

Question 4

A medical imaging company wants to train a computer vision model to detect areas of concern on patients' CT scans. The company has a large collection of unlabeled CT scans that are linked to each patient and stored in an Amazon S3 bucket. The scans must be accessible to authorized users only. A machine learning engineer needs to build a labeling pipeline.

Which set of steps should the engineer take to build the labeling pipeline with the LEAST effort?

Accepted Answer

https://docs.aws.amazon.com/sagemaker/latest/dg/sms-workforce-private.html

Question 5

A web-based company wants to improve its conversion rate on its landing page Using a large historical dataset of customer visits, the company has repeat trained a multi-class deep learning network algorithm on Amazon SageMaker However there is an overfitting problem training data shows 90% accuracy in predictions, while test data shows 70% accuracy only

The company needs to boost the generalization of its model before deploying it into production to maximize conversions of visits to purchases Which action is recommended to provide the HIGHEST accuracy model for the company's test and validation data?

Accepted Answer

If this is a ComputerVision problem augmentation can help and we may consider A an option. However in analyzing customer historic data, there is no easy increase randomization in training. If you go deep into modelling and coding. When you build model with tensorflow/pytorch, most of the time the trainlo already sampling in data in random manner (with shuffle enable). What we usually do to reduce overfitting is by adding dropout.

https://docs.aws.amazon.com/machine-learning/latest/dg/model-fit-underfitting-vs-overfitting.html

Question 6

A Machine Learning Specialist is implementing a full Bayesian network on a dataset that describes public transit in New York City. One of the random var discrete, and represents the number of minutes New Yorkers wait for a bus given that the buses cycle every 10 minutes, with a mean of 3 minutes. Which prior probability distribution should the ML Specialist use for this variable?

Accepted Answer

Correct answer: A

Question 7

A Machine Learning Specialist is creating a new natural language processing application that processes a dataset comprised of 1 million sentences The ai then run Word2Vec to generate embeddings of the sentences and enable different types of predictions

Here is an example from the dataset

- "The quck BROWN FOX jumps over the lazy dog "

Which of the following are the operations the Specialist needs to perform to correctly sanitize and prepare the data in a repeatable manner? (Select THR

Accepted Answer

Correct answer: BCF

Question 8

A large mobile network operating company is building a machine learning model to predict customers who are likely to unsubscribe from the service. The c plans to offer an incentive for these customers as the cost of churn is far greater than the cost of the incentive.

The model produces the following confusion matrix after evaluating on a test dataset of 100 customers: Based on the model evaluation results, why is thi model for production?

n=100

PREDICTED CHURN

Yes

No

ACTUAL Churn Yes

10

4

Actual No

10

76

Accepted Answer

Correct answer: A

Question 9

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A Machine Learning Specialist is using Apache Spark for pre-processing training data As part of the Spark pipeline, the Specialist wants to use Amazon SageMaker for training a model and hosting it Which of the following would the Specialist do to integrate the Spark application with SageMaker? (Select

Accepted Answer

Correct answer: DEF

Question 10

The Chief Editor for a product catalog wants the Research and Development team to build a machine learning system that can be used to detect whether or individuals in a collection of images are wearing the company's retail brand The team has a set of training data

Which machine learning algorithm should the researchers use that BEST meets their requirements?

Accepted Answer

Correct answer: C