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NEW QUESTION 44
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. * 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.
• 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. * 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
• 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: B

Explanation:
https://cloud.google.com/architecture/data-preprocessing-for-ml-with-tf-transform-pt1#where_to_do_preprocessing

NEW QUESTION 45
You are responsible for building a unified analytics environment across a variety of on-premises data marts. Your company is experiencing data quality and security challenges when integrating data across the servers, caused by the use of a wide range of disconnected tools and temporary solutions. You need a fully managed, cloud-native data integration service that will lower the total cost of work and reduce repetitive work. Some members on your team prefer a codeless interface for building Extract, Transform, Load (ETL) process. Which service should you use?

• A. Apache Flink
• B. Dataflow
• C. Cloud Data Fusion
• D. Dataprep

Answer: C

Explanation:
https://cloud.google.com/data-fusion/docs/concepts/overview#using_the_code-free_web_ui

NEW QUESTION 46
You have recently created a proof-of-concept (POC) deep learning model. You are satisfied with the overall architecture, but you need to determine the value for a couple of hyperparameters. You want to perform hyperparameter tuning on Vertex AI to determine both the appropriate embedding dimension for a categorical feature used by your model and the optimal learning rate. You configure the following settings:
For the embedding dimension, you set the type to INTEGER with a minValue of 16 and maxValue of 64.
For the learning rate, you set the type to DOUBLE with a minValue of 10e-05 and maxValue of 10e-02.
You are using the default Bayesian optimization tuning algorithm, and you want to maximize model accuracy. Training time is not a concern. How should you set the hyperparameter scaling for each hyperparameter and the maxParallelTrials?

• A. Use UNIT_LINEAR_SCALE for the embedding dimension, UNIT_LOG_SCALE for the learning rate, and a small number of parallel trials.
• B. Use UNIT_LOG_SCALE for the embedding dimension, UNIT_LINEAR_SCALE for the learning rate, and a small number of parallel trials.
• C. Use UNIT_LINEAR_SCALE for the embedding dimension, UNIT_LOG_SCALE for the learning rate, and a large number of parallel trials.
• D. Use UNIT_LOG_SCALE for the embedding dimension, UNIT_LINEAR_SCALE for the learning rate, and a large number of parallel trials.

Answer: A

NEW QUESTION 47
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