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Professional-Machine-Learning-Engineer Dumps With Exact Questions and Answers

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Professional-Machine-Learning-Engineer Questions and Answers

Question # 1

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.

Use the Cloud Natural Language API to extract custom entities for classification

D.

Build a custom model to identify the product keywords from the transcribed calls, and then run the keywords through a classification algorithm

Question # 2

As the lead ML Engineer for your company, you are responsible for building ML models to digitize scanned customer forms. You have developed a TensorFlow model that converts the scanned images into text and stores them in Cloud Storage. You need to use your ML model on the aggregated data collected at the end of each day with minimal manual intervention. What should you do?

A.

Use the batch prediction functionality of Al Platform

B.

Create a serving pipeline in Compute Engine for prediction

C.

Use Cloud Functions for prediction each time a new data point is ingested

D.

Deploy the model on Al Platform and create a version of it for online inference.

Question # 3

You work for an advertising company and want to understand the effectiveness of your company's latest advertising campaign. You have streamed 500 MB of campaign data into BigQuery. You want to query the table, and then manipulate the results of that query with a pandas dataframe in an Al Platform notebook. What should you do?

A.

Use Al Platform Notebooks' BigQuery cell magic to query the data, and ingest the results as a pandas dataframe

B.

Export your table as a CSV file from BigQuery to Google Drive, and use the Google Drive API to ingest the file into your notebook instance

C.

Download your table from BigQuery as a local CSV file, and upload it to your Al Platform notebook instance Use pandas. read_csv to ingest the file as a pandas dataframe

D.

From a bash cell in your Al Platform notebook, use the bq extract command to export the table as a CSV file to Cloud Storage, and then use gsutii cp to copy the data into the notebook Use pandas. read_csv to ingest the file as a pandas dataframe

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