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A car company is developing a machine learning solution to detect whether a car is present in an image. The image dataset consists of one million images. Each image in the dataset is 200 pixels in height by 200 pixels in width. Each image is labeled as either having a car or not having a car.

Which architecture is MOST likely to produce a model that detects whether a car is present in an image with the highest accuracy?

A.

Use a deep convolutional neural network (CNN) classifier with the images as input. Include a linear output layer that outputs the probability that an image contains a car.

B.

Use a deep convolutional neural network (CNN) classifier with the images as input. Include a softmax output layer that outputs the probability that an image contains a car.

C.

Use a deep multilayer perceptron (MLP) classifier with the images as input. Include a linear output layer that outputs the probability that an image contains a car.

D.

Use a deep multilayer perceptron (MLP) classifier with the images as input. Include a softmax output layer that outputs the probability that an image contains a car.

While reviewing the histogram for residuals on regression evaluation data a Machine Learning Specialist notices that the residuals do not form a zero-centered bell shape as shown What does this mean?

A.

The model might have prediction errors over a range of target values.

B.

The dataset cannot be accurately represented using the regression model

C.

There are too many variables in the model

D.

The model is predicting its target values perfectly.

A Data Scientist is building a model to predict customer churn using a dataset of 100 continuous numerical

features. The Marketing team has not provided any insight about which features are relevant for churn

prediction. The Marketing team wants to interpret the model and see the direct impact of relevant features on

the model outcome. While training a logistic regression model, the Data Scientist observes that there is a wide

gap between the training and validation set accuracy.

Which methods can the Data Scientist use to improve the model performance and satisfy the Marketing team’s

needs? (Choose two.)

A.

Add L1 regularization to the classifier

B.

Add features to the dataset

C.

Perform recursive feature elimination

D.

Perform t-distributed stochastic neighbor embedding (t-SNE)

E.

Perform linear discriminant analysis

A data scientist has a dataset of machine part images stored in Amazon Elastic File System (Amazon EFS). The data scientist needs to use Amazon SageMaker to create and train an image classification machine learning model based on this dataset. Because of budget and time constraints, management wants the data scientist to create and train a model with the least number of steps and integration work required.

How should the data scientist meet these requirements?

A.

Mount the EFS file system to a SageMaker notebook and run a script that copies the data to an Amazon FSx for Lustre file system. Run the SageMaker training job with the FSx for Lustre file system as the data source.

B.

Launch a transient Amazon EMR cluster. Configure steps to mount the EFS file system and copy the data to an Amazon S3 bucket by using S3DistCp. Run the SageMaker training job with Amazon S3 as the data source.

C.

Mount the EFS file system to an Amazon EC2 instance and use the AWS CLI to copy the data to an Amazon S3 bucket. Run the SageMaker training job with Amazon S3 as the data source.

D.

Run a SageMaker training job with an EFS file system as the data source.

A Data Engineer needs to build a model using a dataset containing customer credit card information.

How can the Data Engineer ensure the data remains encrypted and the credit card information is secure?

A.

Use a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMakerinstance in a VPC. Use the SageMaker DeepAR algorithm to randomize the credit card numbers.

B.

Use an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automaticallydiscard credit card numbers and insert fake credit card numbers.

C.

Use an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMakerinstance in a VPC. Use the SageMaker principal component analysis (PCA) algorithm to reduce the lengthof the credit card numbers.

D.

Use AWS KMS to encrypt the data on Amazon S3 and Amazon SageMaker, and redact the credit card numbers from the customer data with AWS Glue.

A chemical company has developed several machine learning (ML) solutions to identify chemical process abnormalities. The time series values of independent variables and the labels are available for the past 2 years and are sufficient to accurately model the problem.

The regular operation label is marked as 0. The abnormal operation label is marked as 1 . Process abnormalities have a significant negative effect on the companys profits. The company must avoid these abnormalities.

Which metrics will indicate an ML solution that will provide the GREATEST probability of detecting an abnormality?

A.

Precision = 0.91 Recall = 0.6

B.

Precision = 0.61 Recall = 0.98

C.

Precision = 0.7 Recall = 0.9

D.

Precision = 0.98 Recall = 0.8

A machine learning (ML) specialist wants to create a data preparation job that uses a PySpark script with complex window aggregation operations to create data for training and testing. The ML specialist needs to evaluate the impact of the number of features and the sample count on model performance.

Which approach should the ML specialist use to determine the ideal data transformations for the model?

A.

Add an Amazon SageMaker Debugger hook to the script to capture key metrics. Run the script as an AWS Glue job.

B.

Add an Amazon SageMaker Experiments tracker to the script to capture key metrics. Run the script as an AWS Glue job.

C.

Add an Amazon SageMaker Debugger hook to the script to capture key parameters. Run the script as a SageMaker processing job.

D.

Add an Amazon SageMaker Experiments tracker to the script to capture key parameters. Run the script as a SageMaker processing job.

A Machine Learning Specialist is working for an online retailer that wants to run analytics on every customer visit, processed through a machine learning pipeline. The data needs to be ingested by Amazon Kinesis Data Streams at up to 100 transactions per second, and the JSON data blob is 100 KB in size.

What is the MINIMUM number of shards in Kinesis Data Streams the Specialist should use to successfully ingest this data?

A.

1 shards

B.

10 shards

C.

100 shards

D.

1,000 shards

A data scientist is working on a forecast problem by using a dataset that consists of .csv files that are stored in Amazon S3. The files contain a timestamp variable in the following format:

March 1st, 2020, 08:14pm -

There is a hypothesis about seasonal differences in the dependent variable. This number could be higher or lower for weekdays because some days and hours present varying values, so the day of the week, month, or hour could be an important factor. As a result, the data scientist needs to transform the timestamp into weekdays, month, and day as three separate variables to conduct an analysis.

Which solution requires the LEAST operational overhead to create a new dataset with the added features?

A.

Create an Amazon EMR cluster. Develop PySpark code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3.

B.

Create a processing job in Amazon SageMaker. Develop Python code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3.

C.

Create a new flow in Amazon SageMaker Data Wrangler. Import the S3 file, use the Featurize date/time transform to generate the new variables, and save the dataset as a new file in Amazon S3.

D.

Create an AWS Glue job. Develop code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3.

A company that runs an online library is implementing a chatbot using Amazon Lex to provide book recommendations based on category. This intent is fulfilled by an AWS Lambda function that queries an Amazon DynamoDB table for a list of book titles, given a particular category. For testing, there are only three categories implemented as the custom slot types: "comedy," "adventure,” and "documentary.”

A machine learning (ML) specialist notices that sometimes the request cannot be fulfilled because Amazon Lex cannot understand the category spoken by users with utterances such as "funny," "fun," and "humor." The ML specialist needs to fix the problem without changing the Lambda code or data in DynamoDB.

How should the ML specialist fix the problem?

A.

Add the unrecognized words in the enumeration values list as new values in the slot type.

B.

Create a new custom slot type, add the unrecognized words to this slot type as enumeration values, and use this slot type for the slot.

C.

Use the AMAZON.SearchQuery built-in slot types for custom searches in the database.

D.

Add the unrecognized words as synonyms in the custom slot type.