Architecting Low-Code ML Solutions

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 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 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?

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 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 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 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?

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 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)

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 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 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)

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?

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 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 service is suitable for this task?

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?

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?

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 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 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 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 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 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 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 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?

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

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?

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?

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 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 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?

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 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?

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 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 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 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 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)

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

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 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 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 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?

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 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 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 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 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?

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 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 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?

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?