PMLE Architecting Low-Code ML Solutions — All Questions With Answers

Question 1mediummultiple choice

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A retail company wants to predict customer churn using historical purchase data stored in BigQuery. The data includes customer demographics, transaction history, and support interactions. The team is comfortable writing SQL and wants to avoid moving data to a separate environment. Which approach should they take?

Question 2easymultiple choice

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A data scientist needs to train a time-series forecasting model on historical sales data stored in BigQuery to predict future demand. The data has strong seasonal patterns. Which BigQuery ML model type should they use?

Question 3mediummultiple choice

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A healthcare provider needs to extract structured information from incoming PDF forms (e.g., patient intake forms). They want to automate data extraction without writing custom models. Which Google Cloud service should they use?

Question 4mediummultiple choice

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A company wants to build a product recommendation engine for their e-commerce website. They have historical purchase data and user interaction logs. They want a managed service that can quickly generate personalized recommendations without building custom models. Which service should they use?

Question 5hardmultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A media company wants to automatically moderate user-uploaded videos by detecting explicit content (e.g., violence, adult material). They need a solution that integrates with their video processing pipeline and scales to millions of videos. Which approach should they take?

Question 6easymultiple choice

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A company wants to transcribe customer service calls in real-time to detect sentiment and identify urgent issues. They need a solution with low latency. Which combination of pre-built APIs should they use?

Question 7mediummultiple choice

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A data analyst wants to train a binary classification model in BigQuery ML on a dataset of 10 million rows with 50 features. They need to evaluate the model's performance on a held-out test set. Which sequence of SQL statements should they run?

Question 8hardmultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A financial services company uses Document AI to process loan applications. They want to ensure that any documents the model cannot process with high confidence are reviewed by a human before finalizing the decision. Which Document AI feature should they enable?

Question 9mediummultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A company has a large dataset of labeled images (e.g., different species of plants). They want to train a custom image classification model with minimal effort and no prior ML experience. Which Google Cloud service should they use?

Question 10easymultiple choice

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A developer wants to add text translation to a mobile app. They need to translate user-generated content into multiple languages, and latency is critical. Which pre-built API should they use?

Question 11hardmultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A company wants to use BigQuery ML to train a DNN_CLASSIFIER model on a dataset with 100 million rows. They are concerned about training time and cost. Which approach can help optimize training performance while staying within BigQuery ML?

Question 12mediummultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A company needs to analyze customer feedback from app reviews to identify common themes and sentiment. They have millions of reviews in multiple languages. Which combination of pre-built APIs should they use?

Question 13mediummulti select

Read the full Architecting Low-Code ML Solutions explanation →

A retail company uses Recommendations AI to power personalized product recommendations on their website. They notice that the 'frequently-bought-together' model is not capturing complementary items that are often purchased in the same session but not necessarily in the same transaction. Which TWO actions should they take to improve the model?

Question 14hardmulti select

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A company is building a document processing pipeline using Document AI to extract data from invoices. They want to ensure high accuracy and handle edge cases where the model may be uncertain. Which THREE steps should they include in their pipeline?

Question 15easymulti select

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A data analyst wants to use BigQuery ML to train a linear regression model (LINEAR_REG) to predict house prices. They have a table with features like square footage, number of bedrooms, and location. Which TWO statements about the training process are correct?

Question 16easymultiple choice

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A data analyst wants to train a binary classification model on a BigQuery table without moving data out of BigQuery. They have limited ML expertise. Which approach should they take?

Question 17mediummultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A retail company wants to build a recommendation system for their e-commerce website. They have user purchase history and product metadata. Which Google Cloud service is most suitable for building a 'frequently bought together' recommendation model with minimal custom ML development?

Question 18hardmultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A financial institution needs to extract structured data from scanned PDFs of loan applications, including text fields and tables. They require a human review step for high-risk applications. Which Google Cloud service and configuration should they use?

Question 19easymultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A media company wants to transcribe audio files from customer support calls into text for analysis. The audio is in English with clear speech and no background noise. They want a quick solution with no ML model training. Which Google Cloud service should they use?

Question 20mediummultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A data scientist needs to forecast daily sales for the next 30 days using historical sales data stored in BigQuery. They want to use BigQuery ML. Which model type should they choose?

Question 21mediummultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A logistics company wants to classify shipping documents into categories (invoice, packing slip, bill of lading) using a custom model with minimal code. They have labeled training images. Which Google Cloud service is most appropriate?

Question 22hardmultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A company uses BigQuery ML to train a boosted tree classifier on a large dataset. After training, they want to understand which features most influence predictions. Which BigQuery ML function should they use?

Question 23mediummultiple choice

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A retailer wants to implement a recommendation engine that suggests products based on a user's current cart. They have limited ML expertise and want a quick deployment. Which Recommendations AI model type should they use?

Question 24easymultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A company needs to detect objects in real-time from a live video feed. They want to use a pre-trained model with minimal setup. Which Google Cloud service should they use?

Question 25mediummultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A data engineer wants to use BigQuery ML to train a model that predicts customer churn using a table with customer features and a label column. They want to use a deep neural network. Which model type should they specify?

Question 26hardmultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A company has a TensorFlow model trained outside of Google Cloud and wants to use it for online predictions on Vertex AI. They have saved the model in SavedModel format. What is the most efficient way to deploy this model?

Question 27mediummultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A company wants to analyze customer reviews for sentiment (positive, negative, neutral) using a pre-trained model with no training. They have text data stored in BigQuery. Which Google Cloud service should they use?

Question 28hardmulti select

Read the full Architecting Low-Code ML Solutions explanation →

A company is building a document processing pipeline for invoices. They need to extract key fields (invoice number, date, total amount) and allow human review for invoices over $10,000. Which TWO Google Cloud services/features should they combine?

Question 29mediummulti select

Read the full Architecting Low-Code ML Solutions explanation →

A data scientist wants to use BigQuery ML for time-series forecasting. They need to evaluate model accuracy and compare different models. Which THREE BigQuery ML functions should they use?

Question 30mediummulti select

Read the full Architecting Low-Code ML Solutions explanation →

A company needs to classify images of products into categories (e.g., electronics, clothing, food). They have labeled images and want to use a low-code solution on Google Cloud. Which TWO services are suitable for this task?

Question 31easymultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A data analyst wants to train a linear regression model to predict house prices using only SQL queries on BigQuery. Which BigQuery ML model type should they use?

Question 32easymultiple choice

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A retail company wants to build a recommendation system to show 'frequently bought together' items. Which Recommendations AI model type should they use?

Question 33easymultiple choice

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A company needs to extract text from scanned invoices and parse key fields like invoice number and total amount. Which Document AI processor should they use?

Question 34mediummultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A data scientist wants to use AutoML to classify images of retail products into categories. There are 50 categories and the dataset has 100,000 labelled images. Which Vertex AI AutoML service is most appropriate?

Question 35mediummultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

An engineer needs to perform sentiment analysis on customer reviews. They have a large volume of text and need a solution that requires minimal customisation. Which option is most efficient?

Question 36mediummultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A company wants to transcribe customer service calls in real-time. The audio is telephony quality (8 kHz). Which Speech-to-Text model should they specify?

Question 37mediummultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A data engineer wants to use BigQuery ML to train a model for predicting customer churn (binary classification) using a large dataset. They want the model to be automatically tuned. Which model type should they choose?

Question 38mediummultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

An organisation wants to use Document AI to process contracts but requires human review for high-risk clauses. Which feature should they enable?

Question 39mediummultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A company needs to forecast product demand for the next 12 months using historical sales data. They want to use BigQuery ML with minimal coding. Which model type is most suitable?

Question 40hardmultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A team trained a TensorFlow model locally and wants to deploy it to BigQuery ML for predictions without retraining. They have exported the SavedModel to Cloud Storage. Which statement is correct?

Question 41hardmultiple choice

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An engineer wants to use BigQuery ML to explain predictions from a trained boosted tree classifier for a specific set of input rows. Which function should they use?

Question 42hardmultiple choice

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A company uses BigQuery ML with a remote model calling Vertex AI's pre-trained image classification model. They need to classify images stored in Cloud Storage buckets. What is the correct approach?

Question 43mediummulti select

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A data scientist wants to use AutoML Tables to build a binary classification model for loan default prediction. The dataset has 200 features and 1 million rows, with highly imbalanced classes. Which TWO options should they consider? (Choose 2)

Question 44mediummulti select

Read the full Architecting Low-Code ML Solutions explanation →

A company needs to detect objects in live video streams from security cameras. They require low-latency predictions and want to minimise operational overhead. Which TWO services should they use? (Choose 2)

Question 45hardmulti select

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A company wants to use Document AI to process a large volume of invoices. They need to extract line items and also have a human review the extracted data for accuracy. Which THREE features should they use? (Choose 3)

Question 46easymultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A data analyst wants to build a binary classification model to predict customer churn using SQL queries in BigQuery. Which BigQuery ML model type should they use?

Question 47mediummultiple choice

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A retail company wants to generate product recommendations on their website using Google Cloud. They have historical transaction data and need a managed service that provides personalized recommendations like 'frequently bought together'. Which service should they use?

Question 48mediummultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A company needs to extract key fields from scanned invoices, such as invoice number and total amount, with high accuracy. They want a managed service and plan to use human review for low-confidence results. Which combination of services should they use?

Question 49hardmultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A healthcare organization wants to build a model to predict patient readmission risk using structured electronic health record (EHR) data. They need to train a model using SQL in BigQuery, but they also want to leverage AutoML's ability to automatically search for the best architecture. Which approach should they take?

Question 50easymultiple choice

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A company wants to classify customer support emails into categories like 'billing', 'technical', or 'account'. They have labeled email text data. Which AutoML solution should they use?

Question 51mediummultiple choice

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A developer needs to transcribe phone calls with high accuracy for a call center analytics application. The audio is in English and has background noise. Which Speech-to-Text model should they choose?

Question 52hardmultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A company has an existing TensorFlow model for fraud detection that they want to use for predictions in BigQuery. They want to call the model from SQL queries without moving data out of BigQuery. How should they deploy the model?

Question 53easymultiple choice

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A data scientist wants to evaluate the performance of a BigQuery ML classification model on a test dataset. Which function should they use?

Question 54mediummulti select

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A company wants to build a model to predict housing prices using BigQuery ML. They have a dataset with features like area, number of bedrooms, and location. Which TWO model types are appropriate for this regression task?

Question 55mediummulti select

Read the full Architecting Low-Code ML Solutions explanation →

A company wants to analyze videos to detect objects and track their movement over time. Which TWO Google Cloud services are suitable for this task?

Question 56hardmulti select

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A retail company wants to implement a recommendation system using Recommendations AI. They need to generate personalized recommendations for users based on their browsing history and purchase behavior. Which THREE recommendation types are available in Recommendations AI?

Question 57easymulti select

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A company wants to transcribe audio from customer service calls and then analyze the sentiment of the transcribed text. Which TWO Google Cloud services should they use?

Question 58hardmulti select

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A data engineer is using BigQuery ML with a BOOSTED_TREE_CLASSIFIER model. After training, they want to evaluate the model and understand which features contribute most to predictions. Which THREE BigQuery ML functions should they use?

Question 59mediummulti select

Read the full Architecting Low-Code ML Solutions explanation →

A company needs to build a custom model to classify images of products into categories. They have a large labeled dataset. They want to use AutoML but are unsure which options support image classification. Which TWO AutoML products support image classification?

Question 1mediummultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A retail company wants to predict customer churn using historical purchase data stored in BigQuery. The data includes customer demographics, transaction history, and support interactions. The team is comfortable writing SQL and wants to avoid moving data to a separate environment. Which approach should they take?

Question 2easymultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A data scientist needs to train a time-series forecasting model on historical sales data stored in BigQuery to predict future demand. The data has strong seasonal patterns. Which BigQuery ML model type should they use?

Question 3mediummultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A healthcare provider needs to extract structured information from incoming PDF forms (e.g., patient intake forms). They want to automate data extraction without writing custom models. Which Google Cloud service should they use?

Question 4mediummultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A company wants to build a product recommendation engine for their e-commerce website. They have historical purchase data and user interaction logs. They want a managed service that can quickly generate personalized recommendations without building custom models. Which service should they use?

Question 5hardmultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A media company wants to automatically moderate user-uploaded videos by detecting explicit content (e.g., violence, adult material). They need a solution that integrates with their video processing pipeline and scales to millions of videos. Which approach should they take?

Question 6easymultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A company wants to transcribe customer service calls in real-time to detect sentiment and identify urgent issues. They need a solution with low latency. Which combination of pre-built APIs should they use?

Question 7mediummultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A data analyst wants to train a binary classification model in BigQuery ML on a dataset of 10 million rows with 50 features. They need to evaluate the model's performance on a held-out test set. Which sequence of SQL statements should they run?

Question 8hardmultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A financial services company uses Document AI to process loan applications. They want to ensure that any documents the model cannot process with high confidence are reviewed by a human before finalizing the decision. Which Document AI feature should they enable?

Question 9mediummultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A company has a large dataset of labeled images (e.g., different species of plants). They want to train a custom image classification model with minimal effort and no prior ML experience. Which Google Cloud service should they use?

Question 10easymultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A developer wants to add text translation to a mobile app. They need to translate user-generated content into multiple languages, and latency is critical. Which pre-built API should they use?

Question 11hardmultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A company wants to use BigQuery ML to train a DNN_CLASSIFIER model on a dataset with 100 million rows. They are concerned about training time and cost. Which approach can help optimize training performance while staying within BigQuery ML?

Question 12mediummultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A company needs to analyze customer feedback from app reviews to identify common themes and sentiment. They have millions of reviews in multiple languages. Which combination of pre-built APIs should they use?

Question 13mediummulti select

Read the full Architecting Low-Code ML Solutions explanation →

A retail company uses Recommendations AI to power personalized product recommendations on their website. They notice that the 'frequently-bought-together' model is not capturing complementary items that are often purchased in the same session but not necessarily in the same transaction. Which TWO actions should they take to improve the model?

Question 14hardmulti select

Read the full Architecting Low-Code ML Solutions explanation →

A company is building a document processing pipeline using Document AI to extract data from invoices. They want to ensure high accuracy and handle edge cases where the model may be uncertain. Which THREE steps should they include in their pipeline?

Question 15easymulti select

Read the full Architecting Low-Code ML Solutions explanation →

A data analyst wants to use BigQuery ML to train a linear regression model (LINEAR_REG) to predict house prices. They have a table with features like square footage, number of bedrooms, and location. Which TWO statements about the training process are correct?

Question 16easymultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A data analyst wants to train a binary classification model on a BigQuery table without moving data out of BigQuery. They have limited ML expertise. Which approach should they take?

Question 17mediummultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A retail company wants to build a recommendation system for their e-commerce website. They have user purchase history and product metadata. Which Google Cloud service is most suitable for building a 'frequently bought together' recommendation model with minimal custom ML development?

Question 18hardmultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A financial institution needs to extract structured data from scanned PDFs of loan applications, including text fields and tables. They require a human review step for high-risk applications. Which Google Cloud service and configuration should they use?

Question 19easymultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A media company wants to transcribe audio files from customer support calls into text for analysis. The audio is in English with clear speech and no background noise. They want a quick solution with no ML model training. Which Google Cloud service should they use?

Question 20mediummultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A data scientist needs to forecast daily sales for the next 30 days using historical sales data stored in BigQuery. They want to use BigQuery ML. Which model type should they choose?

Question 21mediummultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A logistics company wants to classify shipping documents into categories (invoice, packing slip, bill of lading) using a custom model with minimal code. They have labeled training images. Which Google Cloud service is most appropriate?

Question 22hardmultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A company uses BigQuery ML to train a boosted tree classifier on a large dataset. After training, they want to understand which features most influence predictions. Which BigQuery ML function should they use?

Question 23mediummultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A retailer wants to implement a recommendation engine that suggests products based on a user's current cart. They have limited ML expertise and want a quick deployment. Which Recommendations AI model type should they use?

Question 24easymultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A company needs to detect objects in real-time from a live video feed. They want to use a pre-trained model with minimal setup. Which Google Cloud service should they use?

Question 25mediummultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A data engineer wants to use BigQuery ML to train a model that predicts customer churn using a table with customer features and a label column. They want to use a deep neural network. Which model type should they specify?

Question 26hardmultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A company has a TensorFlow model trained outside of Google Cloud and wants to use it for online predictions on Vertex AI. They have saved the model in SavedModel format. What is the most efficient way to deploy this model?

Question 27mediummultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A company wants to analyze customer reviews for sentiment (positive, negative, neutral) using a pre-trained model with no training. They have text data stored in BigQuery. Which Google Cloud service should they use?

Question 28hardmulti select

Read the full Architecting Low-Code ML Solutions explanation →

A company is building a document processing pipeline for invoices. They need to extract key fields (invoice number, date, total amount) and allow human review for invoices over $10,000. Which TWO Google Cloud services/features should they combine?

Question 29mediummulti select

Read the full Architecting Low-Code ML Solutions explanation →

A data scientist wants to use BigQuery ML for time-series forecasting. They need to evaluate model accuracy and compare different models. Which THREE BigQuery ML functions should they use?

Question 30mediummulti select

Read the full Architecting Low-Code ML Solutions explanation →

A company needs to classify images of products into categories (e.g., electronics, clothing, food). They have labeled images and want to use a low-code solution on Google Cloud. Which TWO services are suitable for this task?

Question 31easymultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A data analyst wants to train a linear regression model to predict house prices using only SQL queries on BigQuery. Which BigQuery ML model type should they use?

Question 32easymultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A retail company wants to build a recommendation system to show 'frequently bought together' items. Which Recommendations AI model type should they use?

Question 33easymultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A company needs to extract text from scanned invoices and parse key fields like invoice number and total amount. Which Document AI processor should they use?

Question 34mediummultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A data scientist wants to use AutoML to classify images of retail products into categories. There are 50 categories and the dataset has 100,000 labelled images. Which Vertex AI AutoML service is most appropriate?

Question 35mediummultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

An engineer needs to perform sentiment analysis on customer reviews. They have a large volume of text and need a solution that requires minimal customisation. Which option is most efficient?

Question 36mediummultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A company wants to transcribe customer service calls in real-time. The audio is telephony quality (8 kHz). Which Speech-to-Text model should they specify?

Question 37mediummultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A data engineer wants to use BigQuery ML to train a model for predicting customer churn (binary classification) using a large dataset. They want the model to be automatically tuned. Which model type should they choose?

Question 38mediummultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

An organisation wants to use Document AI to process contracts but requires human review for high-risk clauses. Which feature should they enable?

Question 39mediummultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A company needs to forecast product demand for the next 12 months using historical sales data. They want to use BigQuery ML with minimal coding. Which model type is most suitable?

Question 40hardmultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A team trained a TensorFlow model locally and wants to deploy it to BigQuery ML for predictions without retraining. They have exported the SavedModel to Cloud Storage. Which statement is correct?

Question 41hardmultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

An engineer wants to use BigQuery ML to explain predictions from a trained boosted tree classifier for a specific set of input rows. Which function should they use?

Question 42hardmultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A company uses BigQuery ML with a remote model calling Vertex AI's pre-trained image classification model. They need to classify images stored in Cloud Storage buckets. What is the correct approach?

Question 43mediummulti select

Read the full Architecting Low-Code ML Solutions explanation →

A data scientist wants to use AutoML Tables to build a binary classification model for loan default prediction. The dataset has 200 features and 1 million rows, with highly imbalanced classes. Which TWO options should they consider? (Choose 2)

Question 44mediummulti select

Read the full Architecting Low-Code ML Solutions explanation →

A company needs to detect objects in live video streams from security cameras. They require low-latency predictions and want to minimise operational overhead. Which TWO services should they use? (Choose 2)

Question 45hardmulti select

Read the full Architecting Low-Code ML Solutions explanation →

A company wants to use Document AI to process a large volume of invoices. They need to extract line items and also have a human review the extracted data for accuracy. Which THREE features should they use? (Choose 3)

Question 46easymultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A data analyst wants to build a binary classification model to predict customer churn using SQL queries in BigQuery. Which BigQuery ML model type should they use?

Question 47mediummultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A retail company wants to generate product recommendations on their website using Google Cloud. They have historical transaction data and need a managed service that provides personalized recommendations like 'frequently bought together'. Which service should they use?

Question 48mediummultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A company needs to extract key fields from scanned invoices, such as invoice number and total amount, with high accuracy. They want a managed service and plan to use human review for low-confidence results. Which combination of services should they use?

Question 49hardmultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A healthcare organization wants to build a model to predict patient readmission risk using structured electronic health record (EHR) data. They need to train a model using SQL in BigQuery, but they also want to leverage AutoML's ability to automatically search for the best architecture. Which approach should they take?

Question 50easymultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A company wants to classify customer support emails into categories like 'billing', 'technical', or 'account'. They have labeled email text data. Which AutoML solution should they use?

Question 51mediummultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A developer needs to transcribe phone calls with high accuracy for a call center analytics application. The audio is in English and has background noise. Which Speech-to-Text model should they choose?

Question 52hardmultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A company has an existing TensorFlow model for fraud detection that they want to use for predictions in BigQuery. They want to call the model from SQL queries without moving data out of BigQuery. How should they deploy the model?

Question 53easymultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A data scientist wants to evaluate the performance of a BigQuery ML classification model on a test dataset. Which function should they use?

Question 54mediummulti select

Read the full Architecting Low-Code ML Solutions explanation →

A company wants to build a model to predict housing prices using BigQuery ML. They have a dataset with features like area, number of bedrooms, and location. Which TWO model types are appropriate for this regression task?

Question 55mediummulti select

Read the full Architecting Low-Code ML Solutions explanation →

A company wants to analyze videos to detect objects and track their movement over time. Which TWO Google Cloud services are suitable for this task?

Question 56hardmulti select

Read the full Architecting Low-Code ML Solutions explanation →

A retail company wants to implement a recommendation system using Recommendations AI. They need to generate personalized recommendations for users based on their browsing history and purchase behavior. Which THREE recommendation types are available in Recommendations AI?

Question 57easymulti select

Read the full Architecting Low-Code ML Solutions explanation →

A company wants to transcribe audio from customer service calls and then analyze the sentiment of the transcribed text. Which TWO Google Cloud services should they use?

Question 58hardmulti select

Read the full Architecting Low-Code ML Solutions explanation →

A data engineer is using BigQuery ML with a BOOSTED_TREE_CLASSIFIER model. After training, they want to evaluate the model and understand which features contribute most to predictions. Which THREE BigQuery ML functions should they use?

Question 59mediummulti select

Read the full Architecting Low-Code ML Solutions explanation →

A company needs to build a custom model to classify images of products into categories. They have a large labeled dataset. They want to use AutoML but are unsure which options support image classification. Which TWO AutoML products support image classification?