Architecting Low-Code ML Solutions

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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

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?

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?

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?

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?

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

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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

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?

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?

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?

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?

A financial services firm wants to predict loan default risk using a dataset with 30,000 labeled examples and 25 numeric and categorical features. Their team includes SQL analysts but no Python developers, and they want to minimize operational overhead. They decide to use BigQuery ML. Which model type should they use to achieve the best predictive performance while keeping the solution low-code?

A marketing team needs to build a model that predicts whether a customer will respond to a promotional email. They have a BigQuery table with 2 million rows and 30 features, and they want to avoid writing any Python code. They require an explainable model and the ability to generate predictions directly in SQL. Which approach should they use?

A marketing team wants to automatically categorize customer feedback emails into topics such as 'billing', 'technical support', or 'general inquiry'. They have a dataset of 5,000 labeled emails and want to build a custom model with minimal coding effort. Which Google Cloud service should they use?

A retail company wants to forecast daily sales for each of its 500 stores for the next 90 days. They have three years of historical daily sales data stored in BigQuery, including promotions, holidays, and store attributes. The data science team has minimal ML expertise and wants to use SQL to build and deploy the model with minimal coding. Which approach should they use?

A marketing team wants to build a model that predicts customer lifetime value (CLV) using historical transaction data. They are comfortable with spreadsheets but have no coding experience. They need a low-code solution that automatically handles feature engineering and model selection. Which Google Cloud service should they use?

A retail company wants to build a demand forecasting model for thousands of product SKUs. They have historical sales data in BigQuery and limited ML expertise. They want to minimize coding and automatically handle seasonality and promotions. Which approach should they use?

A hospital wants to build a system that automatically transcribes doctors' dictated notes into text and then identifies key medical terms such as diagnoses and medications. They have no ML expertise and want to use Google Cloud's pre-trained APIs. Which combination of services should they use?

A marketing team wants to build a model to predict which customers are likely to churn. They have a BigQuery table with customer demographics, usage metrics, and a binary churn label. They want to use BigQuery ML and need to evaluate the model's performance. Which two statements are true regarding model evaluation in BigQuery ML? (Choose two.)

A financial services company wants to extract text and structured data from scanned loan application forms. They need a fully managed, low-code solution that can handle various form layouts and requires minimal machine learning expertise. Which Google Cloud service should they use?

A logistics company wants to classify shipping documents into categories such as invoice, packing slip, and bill of lading. They have a small set of labeled documents (about 50 per category) and want to use a low-code approach. They need a model that can be trained quickly and deployed for online predictions. Which Google Cloud service should they use?

A small marketing team has a CSV file of 2,000 labeled customer support tickets (each with a category such as 'billing' or 'technical'). They have no ML engineers and want a fully managed, low-code way to train a text classification model that they can later call from their internal web app. Which Google Cloud service should they use?

A retail company wants to build a low-code ML solution to predict customer lifetime value (CLV) using historical transaction data stored in BigQuery. They have limited ML expertise and want to use BigQuery ML. Which two steps are necessary to train and evaluate a model using BigQuery ML? (Choose two.)

A hospital's radiology department wants to build a model that flags possible pneumonia on chest X-rays. They have 8,000 labeled DICOM studies in a Cloud Storage bucket and no in-house data science staff. They need a managed service that can ingest the images, train a classifier, and provide an endpoint for their viewing software. What should they do?

A media company wants to automatically transcribe and analyze customer support calls to identify common issues. They need a low-code solution that provides both transcription and sentiment analysis. Which Google Cloud service should they use?

A financial institution wants to detect fraudulent transactions in real-time. They have a labeled dataset of historical transactions and want to build a custom model with minimal coding. They also need to integrate the model into an existing application that expects a REST API. Which Google Cloud service should they use to train and deploy the model with the least effort?

A media company wants a low-code pipeline that ingests uploaded video files, detects scenes and on-screen text, and stores structured metadata for search. They prefer managed services and minimal custom code. Which TWO Google Cloud capabilities should they combine? (Choose two.)

A retail company wants to forecast daily sales for the next 30 days based on historical sales data. They have two years of daily sales records with no missing values. They want to use a low-code solution on Google Cloud that automatically handles seasonality and trends. Which service should they use?

A financial services firm wants relationship managers to query a natural-language interface such as 'show me customers likely to churn next quarter' and receive results from their BigQuery data warehouse. They want to minimize the code they maintain and keep data in BigQuery. Which approach best fits?

A company wants to build a recommendation system that suggests products to users based on their past interactions. They have user-item interaction data in BigQuery and want a low-code solution that can generate recommendations for all users. Which approach should they use?

A financial institution wants to detect fraudulent transactions in real-time. They have a labeled dataset of historical transactions and want to use a low-code solution that can automatically handle feature engineering and model selection. They also need to deploy the model for online predictions with low latency. Which Google Cloud service should they use?

A marketing team wants to build a model that predicts whether a customer will click on an ad, using a dataset in BigQuery. They have limited ML expertise and want to avoid writing complex code. They decide to use BigQuery ML with a logistic regression model. Which SQL statement should they use to create the model?

A healthcare company wants to build a model to predict patient readmission risk using structured data in BigQuery. They have a dataset with 100,000 rows and 30 features, including numerical and categorical variables. They require a model that provides explainable predictions and can be trained quickly. They decide to use BigQuery ML. Which model type should they choose?

A retail company wants to forecast monthly sales for each of its 500 stores using historical sales data. They have two years of daily sales data per store and want to use BigQuery ML to build a forecasting model. They need to account for seasonality and trends. Which BigQuery ML model type should they use?

A company wants to build a recommendation system that suggests products to users based on their past purchase history. They have a large dataset of user-item interactions in BigQuery and want to use a low-code approach. They decide to use BigQuery ML's matrix factorization model. Which SQL statement correctly creates such a model?