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NEW QUESTION 33
A retail company is using Amazon Personalize to provide personalized product recommendations for its customers during a marketing campaign. The company sees a significant increase in sales of recommended items to existing customers immediately after deploying a new solution version, but these sales decrease a short time after deployment. Only historical data from before the marketing campaign is available for training.
How should a data scientist adjust the solution?
- A. Use the event tracker in Amazon Personalize to include real-time user interactions.
- B. Implement a new solution using the built-in factorization machines (FM) algorithm in Amazon SageMaker.
- C. Add event type and event value fields to the interactions dataset in Amazon Personalize.
- D. Add user metadata and use the HRNN-Metadata recipe in Amazon Personalize.
Answer: C
NEW QUESTION 34
You work for a large technology company that wants to modernize their contact center. You have been asked to develop a solution to classify incoming calls by product so that requests can be more quickly routed to the correct support team. You have already transcribed the calls using the Speech-to-Text API. You want to minimize data preprocessing and development time. How should you build the model?
- A. Use the Al Platform Training built-in algorithms to create a custom model
- B. Use AutoML Natural Language to extract custom entities for classification
- C. Build a custom model to identify the product keywords from the transcribed calls, and then run the keywords through a classification algorithm
- D. Use the Cloud Natural Language API to extract custom entities for classification
Answer: A
NEW QUESTION 35
You manage a team of data scientists who use a cloud-based backend system to submit training jobs. This system has become very difficult to administer, and you want to use a managed service instead. The data scientists you work with use many different frameworks, including Keras, PyTorch, theano, scikit-learn, and custom libraries. What should you do?
- A. Configure Kubeflow to run on Google Kubernetes Engine and submit training jobs through TFJob.
- B. Set up Slurm workload manager to receive jobs that can be scheduled to run on your cloud infrastructure.
- C. Create a library of VM images on Compute Engine, and publish these images on a centralized repository.
- D. Use the Vertex AI Training to submit training jobs using any framework.
Answer: B
NEW QUESTION 36
A financial services company is building a robust serverless data lake on Amazon S3. The data lake should be flexible and meet the following requirements:
* Support querying old and new data on Amazon S3 through Amazon Athena and Amazon Redshift Spectrum.
* Support event-driven ETL pipelines
* Provide a quick and easy way to understand metadata
Which approach meets these requirements?
- A. Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Batch job, and an external Apache Hive metastore to search and discover metadata.
- B. Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Glue ETL job, and an AWS Glue Data catalog to search and discover metadata.
- C. Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Glue ETL job, and an external Apache Hive metastore to search and discover metadata.
- D. Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Batch job, and an AWS Glue Data Catalog to search and discover metadata.
Answer: B
NEW QUESTION 37
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