An ecommerce company wants to use machine learning (ML) to monitor fraudulent transactions on its website. The company is using Amazon SageMaker to research, train, deploy, and monitor the ML models.
The historical transactions data is in a .csv file that is stored in Amazon S3. The data contains features such as the user's IP address, navigation time, average time on each page, and the number of clicks for each session. There is no label in the data to indicate if a transaction is anomalous.
Which models should the company use in combination to detect anomalous transactions? (Choose two.)
A. IP Insights
B. K-nearest neighbors (k-NN)
C. Linear learner with a logistic function
D. Random Cut Forest (RCF)
E. XGBoost
A healthcare company is using an Amazon SageMaker notebook instance to develop machine learning (ML) models. The company's data scientists will need to be able to access datasets stored in Amazon S3 to train the models. Due to regulatory requirements, access to the data from instances and services used for training must not be transmitted over the internet.
Which combination of steps should an ML specialist take to provide this access? (Choose two.)
A. Configure the SageMaker notebook instance to be launched with a VPC attached and internet access disabled.
B. Create and configure a VPN tunnel between SageMaker and Amazon S3.
C. Create and configure an S3 VPC endpoint Attach it to the VPC.
D. Create an S3 bucket policy that allows traffic from the VPC and denies traffic from the internet.
E. Deploy AWS Transit Gateway Attach the S3 bucket and the SageMaker instance to the gateway.
A machine learning (ML) specialist at a retail company is forecasting sales for one of the company's stores. The ML specialist is using data from the past 10 years. The company has provided a dataset that includes the total amount of money in sales each day for the store. Approximately 5% of the days are missing sales data.
The ML specialist builds a simple forecasting model with the dataset and discovers that the model performs poorly. The performance is poor around the time of seasonal events, when the model consistently predicts sales figures that are too low or too high.
Which actions should the ML specialist take to try to improve the model's performance? (Choose two.)
A. Add information about the store's sales periods to the dataset.
B. Aggregate sales figures from stores in the same proximity.
C. Apply smoothing to correct for seasonal variation.
D. Change the forecast frequency from daily to weekly.
E. Replace missing values in the dataset by using linear interpolation.
A newspaper publisher has a table of customer data that consists of several numerical and categorical features, such as age and education history, as well as subscription status. The company wants to build a targeted marketing model for predicting the subscription status based on the table data.
Which Amazon SageMaker built-in algorithm should be used to model the targeted marketing?
A. Random Cut Forest (RCF)
B. XGBoost
C. Neural Topic Model (NTM)
D. DeepAR forecasting
A retail company collects customer comments about its products from social media, the company website, and customer call logs. A team of data scientists and engineers wants to find common topics and determine which products the
customers are referring to in their comments. The team is using natural language processing (NLP) to build a model to help with this classification.
Each product can be classified into multiple categories that the company defines. These categories are related but are not mutually exclusive. For example, if there is mention of "Sample Yogurt" in the document of customer comments, then
"Sample Yogurt" should be classified as "yogurt," "snack," and "dairy product."
The team is using Amazon Comprehend to train the model and must complete the project as soon as possible.
Which functionality of Amazon Comprehend should the team use to meet these requirements?
A. Custom classification with multi-class mode
B. Custom classification with multi-label mode
C. Custom entity recognition
D. Built-in models
A data engineer is using AWS Glue to create optimized, secure datasets in Amazon S3. The data science team wants the ability to access the ETL scripts directly from Amazon SageMaker notebooks within a VPC. After this setup is complete, the data science team wants the ability to run the AWS Glue job and invoke the SageMaker training job.
Which combination of steps should the data engineer take to meet these requirements? (Choose three.)
A. Create a SageMaker development endpoint in the data science team's VPC.
B. Create an AWS Glue development endpoint in the data science team's VPC.
C. Create SageMaker notebooks by using the AWS Glue development endpoint.
D. Create SageMaker notebooks by using the SageMaker console.
E. Attach a decryption policy to the SageMaker notebooks.
F. Create an IAM policy and an IAM role for the SageMaker notebooks.
A data engineer needs to provide a team of data scientists with the appropriate dataset to run machine learning training jobs. The data will be stored in Amazon S3. The data engineer is obtaining the data from an Amazon Redshift database and is using join queries to extract a single tabular dataset. A portion of the schema is as follows:
1.
TransactionTimestamp (Timestamp)
2.
CardName (Varchar)
3.
CardNo (Varchar)
The data engineer must provide the data so that any row with a CardNo value of NULL is removed. Also, the TransactionTimestamp column must be separated into a TransactionDate column and a TransactionTime column. Finally, the CardName column must be renamed to NameOnCard. The data will be extracted on a monthly basis and will be loaded into an S3 bucket. The solution must minimize the effort that is needed to set up infrastructure for the ingestion and transformation. The solution also must be automated and
must minimize the load on the Amazon Redshift cluster. Which solution meets these requirements?
A. Set up an Amazon EMR cluster. Create an Apache Spark job to read the data from the Amazon Redshift cluster and transform the data. Load the data into the S3 bucket. Schedule the job to run monthly.
B. Set up an Amazon EC2 instance with a SQL client tool, such as SQL Workbench/J, to query the data from the Amazon Redshift cluster directly Export the resulting dataset into a file. Upload the file into the S3 bucket. Perform these tasks monthly.
C. Set up an AWS Glue job that has the Amazon Redshift cluster as the source and the S3 bucket as the destination. Use the built-in transforms Filter, Map, and RenameField to perform the required transformations. Schedule the job to run monthly.
D. Use Amazon Redshift Spectrum to run a query that writes the data directly to the S3 bucket. Create an AWS Lambda function to run the query monthly.
A machine learning (ML) specialist wants to bring a custom training algorithm to Amazon SageMaker. The ML specialist implements the algorithm in a Docker container that is supported by SageMaker. How should the ML specialist package the Docker container so that SageMaker can launch the training correctly?
A. Specify the server argument in the ENTRYPOINT instruction in the Dockerfile.
B. Specify the training program in the ENTRYPOINT instruction in the Dockerfile.
C. Include the path to the training data in the docker build command when packaging the container.
D. Use a COPY instruction in the Dockerfile to copy the training program to the /opt/ml/train directory.
A data scientist is reviewing customer comments about a company's products. The data scientist needs to present an initial exploratory analysis by using charts and a word cloud. The data scientist must use feature engineering techniques to prepare this analysis before starting a natural language processing (NLP) model.
Which combination of feature engineering techniques should the data scientist use to meet these requirements? (Choose two.)
A. Named entity recognition
B. Coreference
C. Stemming
D. Term frequency-inverse document frequency (TF-IDF)
E. Sentiment analysis
A data scientist is evaluating a GluonTS on Amazon SageMaker DeepAR model. The evaluation metrics on the test set indicate that the coverage score is 0.489 and 0.889 at the 0.5 and 0.9 quantiles, respectively. What can the data scientist reasonably conclude about the distributional forecast related to the test set?
A. The coverage scores indicate that the distributional forecast is poorly calibrated. These scores should be approximately equal to each other at all quantiles.
B. The coverage scores indicate that the distributional forecast is poorly calibrated. These scores should peak at the median and be lower at the tails.
C. The coverage scores indicate that the distributional forecast is correctly calibrated. These scores should always fall below the quantile itself.
D. The coverage scores indicate that the distributional forecast is correctly calibrated. These scores should be approximately equal to the quantile itself.
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