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Professional-Machine-Learning-Engineer Exam Dumps - Google Machine Learning Engineer Questions and Answers

Question # 84

You are an ML engineer at a global car manufacturer. You need to build an ML model to predict car sales in different cities around the world. Which features or feature crosses should you use to train city-specific relationships between car type and number of sales?

Options:

A.

Three individual features binned latitude, binned longitude, and one-hot encoded car type

B.

One feature obtained as an element-wise product between latitude, longitude, and car type

C.

One feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type

D.

Two feature crosses as a element-wise product the first between binned latitude and one-hot encoded car type, and the second between binned longitude and one-hot encoded car type

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Question # 85

You recently joined an enterprise-scale company that has thousands of datasets. You know that there are accurate descriptions for each table in BigQuery, and you are searching for the proper BigQuery table to use for a model you are building on AI Platform. How should you find the data that you need?

Options:

A.

Use Data Catalog to search the BigQuery datasets by using keywords in the table description.

B.

Tag each of your model and version resources on AI Platform with the name of the BigQuery table that was used for training.

C.

Maintain a lookup table in BigQuery that maps the table descriptions to the table ID. Query the lookup table to find the correct table ID for the data that you need.

D.

Execute a query in BigQuery to retrieve all the existing table names in your project using the

INFORMATION_SCHEMA metadata tables that are native to BigQuery. Use the result o find the table that you need.

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Question # 86

You work for a hospital that wants to optimize how it schedules operations. You need to create a model that uses the relationship between the number of surgeries scheduled and beds used You want to predict how many beds will be needed for patients each day in advance based on the scheduled surgeries You have one year of data for the hospital organized in 365 rows

The data includes the following variables for each day

• Number of scheduled surgeries

• Number of beds occupied

• Date

You want to maximize the speed of model development and testing What should you do?

Options:

A.

Create a BigQuery table Use BigQuery ML to build a regression model, with number of beds as the target variable and number of scheduled surgeries and date features (such as day of week) as the predictors

B.

Create a BigQuery table Use BigQuery ML to build an ARIMA model, with number of beds as the target variable and date as the time variable.

C.

Create a Vertex Al tabular dataset Tram an AutoML regression model, with number of beds as the target variable and number of scheduled minor surgeries and date features (such as day of the week) as the predictors

D.

Create a Vertex Al tabular dataset Train a Vertex Al AutoML Forecasting model with number of beds as the target variable, number of scheduled surgeries as a covariate, and date as the time variable.

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Question # 87

You need to develop a custom TensorRow model that will be used for online predictions. The training data is stored in BigQuery. You need to apply instance-level data transformations to the data for model training and serving. You want to use the same preprocessing routine during model training and serving. How should you configure the preprocessing routine?

Options:

A.

Create a BigQuery script to preprocess the data, and write the result to another BigQuery table.

B.

Create a pipeline in Vertex Al Pipelines to read the data from BigQuery and preprocess it using a custom preprocessing component.

C.

Create a preprocessing function that reads and transforms the data from BigQuery Create a Vertex Al custom prediction routine that calls the preprocessing function at serving time.

D.

Create an Apache Beam pipeline to read the data from BigQuery and preprocess it by using TensorFlow Transform and Dataflow.

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Question # 88

Your organization’s marketing team is building a customer recommendation chatbot that uses a generative AI large language model (LLM) to provide personalized product suggestions in real time. The chatbot needs to access data from millions of customers, including purchase history, browsing behavior, and preferences. The data is stored in a Cloud SQL for PostgreSQL database. You need the chatbot response time to be less than 100ms. How should you design the system?

Options:

A.

Use BigQuery ML to fine-tune the LLM with the data in the Cloud SQL for PostgreSQL database, and access the model from BigQuery.

B.

Replicate the Cloud SQL for PostgreSQL database to AlloyDB. Configure the chatbot server to query AlloyDB.

C.

Transform relevant customer data into vector embeddings and store them in Vertex AI Search for retrieval by the LLM.

D.

Create a caching layer between the chatbot and the Cloud SQL for PostgreSQL database to store frequently accessed customer data. Configure the chatbot server to query the cache.

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Exam Name: Google Professional Machine Learning Engineer
Last Update: Apr 1, 2025
Questions: 285
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