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NEW QUESTION 31
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 stream by 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 delivery 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 increasing 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?

A. Increase the number of shards for the data stream.B. Decrease the retention period for the data stream.C. Increase the number of S3 prefixes for the delivery stream to write to.D. Add more consumers using the Kinesis Client Library (KCL).

Answer: A

Explanation:
Explanation/Reference:

 

NEW QUESTION 32
You are an ML engineer on an agricultural research team working on a crop disease detection tool to detect leaf rust spots in images of crops to determine the presence of a disease. These spots, which can vary in shape and size, are correlated to the severity of the disease. You want to develop a solution that predicts the presence and severity of the disease with high accuracy. What should you do?

A. Develop a template matching algorithm using traditional computer vision libraries.B. Develop an image segmentation ML model to locate the boundaries of the rust spots.C. Develop an image classification ML model to predict the presence of the disease.D. Create an object detection model that can localize the rust spots.

Answer: B

 

NEW QUESTION 33
You trained a text classification model. You have the following SignatureDefs:

What is the correct way to write the predict request?

A. data = json.dumps({"signature_name": "serving_default'\ "instances": [fab', 'be1, 'cd']]})B. data = json dumps({"signature_name": "serving_default"! "instances": [['a', 'b', "c", 'd', 'e', 'f']]})C. data = json.dumps({"signature_name": "serving_default, "instances": [['a', 'b\ 'c'1, [d\ 'e\ T]]})D. data = json dumps({"signature_name": f,serving_default", "instances": [['a', 'b'], [c\ 'd'], ['e\ T]]})

Answer: D

Explanation:
https://stackoverflow.com/questions/37956197/what-is-the-negative-index-in-shape-arrays-used-for-tensorflow

 

NEW QUESTION 34
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. Use the Vertex AI Training to submit training jobs using any framework.B. Set up Slurm workload manager to receive jobs that can be scheduled to run on your cloud infrastructure.C. Configure Kubeflow to run on Google Kubernetes Engine and submit training jobs through TFJob.D. Create a library of VM images on Compute Engine, and publish these images on a centralized repository.

Answer: B

 

NEW QUESTION 35
A Data Scientist wants to gain real-time insights into a data stream of GZIP files.
Which solution would allow the use of SQL to query the stream with the LEAST latency?

A. AWS Glue with a custom ETL script to transform the data.B. Amazon Kinesis Data Analytics with an AWS Lambda function to transform the data.C. An Amazon Kinesis Client Library to transform the data and save it to an Amazon ES cluster.D. Amazon Kinesis Data Firehose to transform the data and put it into an Amazon S3 bucket.

Answer: B

Explanation:
Explanation/Reference: https://aws.amazon.com/big-data/real-time-analytics-featured-partners/

 

NEW QUESTION 36
......

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