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NEW QUESTION: 1
What would you want to assess your patient for if they have a myocardial infarction that is developing into
cardiogenic shock?
A. Rising diastolic blood pressure
B. Falling central venous pressure
C. Bradycardia
D. Ventricular dysrhythmia
Answer: D
Explanation:
The nurse would want to assess the patient for ventricular dysrhythmia. The left ventricle
may have been damaged by the myocardial infarction, which could lead to left ventricular failure. The
patient would have tachycardia, low blood pressure and rising central venous pressure.

NEW QUESTION: 2
A data engineer in a manufacturing company is designing a data processing platform that receives a large volume of unstructured data. The data engineer must populate a well-structured star schema in Amazon Redshift.
What is the most efficient architecture strategy for this purpose?
A. When the data is saved to Amazon S3, use S3 Event Notifications and AWS Lambda to transform the file contents. Insert the data into the analysis schema on Redshift.
B. Transform the unstructured data using Amazon EMR and generate CSV data. COPY the CSV data into the analysis schema within Redshift.
C. Load the unstructured data into Redshift, and use string parsing functions to extract structured data for inserting into the analysis schema.
D. Normalize the data using an AWS Marketplace ETL tool, persist the results to Amazon S3, and use AWS Lambda to INSERT the data into Redshift.
Answer: B

NEW QUESTION: 3
You have trained a model on a dataset that required computationally expensive preprocessing operations. You need to execute the same preprocessing at prediction time. You deployed the model on Al Platform for high-throughput online prediction. Which architecture should you use?
A. Send incoming prediction requests to a Pub/Sub topic
* Set up a Cloud Function that is triggered when messages are published to the Pub/Sub topic.
* Implement your preprocessing logic in the Cloud Function
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue
B. Send incoming prediction requests to a Pub/Sub topic
* Transform the incoming data using a Dataflow job
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue
C. Stream incoming prediction request data into Cloud Spanner
* Create a view to abstract your preprocessing logic.
* Query the view every second for new records
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue.
D. Validate the accuracy of the model that you trained on preprocessed data
* Create a new model that uses the raw data and is available in real time
* Deploy the new model onto Al Platform for online prediction
Answer: A