Architecting low-code ML solutions

A retail company wants to build a product recommendation system using BigQuery ML for their e-commerce platform. The data includes customer purchase history, product metadata, and clickstream logs. The ML engineer needs to minimize manual feature engineering and leverage pre-built solutions. Which approach should the engineer take?

A data scientist wants to quickly train a binary classification model on a tabular dataset stored in BigQuery without writing any code. They have limited ML experience. Which Google Cloud service should they use?

A company uses Vertex AI Pipelines to orchestrate their ML training workflow. The pipeline includes a BigQuery ML training step, a model evaluation step, and a deployment step to Vertex AI Endpoints. The engineer notices that the pipeline fails intermittently due to a quota exceeded error on Vertex AI Endpoints during model deployment. What is the best long-term solution to prevent this failure?

A manufacturing company wants to predict equipment failure using sensor data stored in BigQuery. They have limited ML expertise and want to use AutoML Tables. The data includes timestamps, numerical sensor readings, and a boolean 'failure' column. The dataset is highly imbalanced with only 1% failure cases. Which of the following is the most effective approach to handle the imbalance in AutoML Tables?

A marketing team wants to use a pre-built natural language processing (NLP) model from Vertex AI Model Garden to analyze customer feedback. They need to extract sentiment from text data stored in Cloud Storage. The team has no experience with model serving infrastructure. Which deployment option minimizes operational overhead?

A financial institution uses BigQuery ML to train a linear regression model to predict loan default risk. The model is trained on a dataset with 100 million rows and 50 features. During inference, the engineer uses the ML.PREDICT function. However, the query takes several minutes to run and times out frequently. The data is static and updated monthly. What is the most cost-effective and low-code solution to improve prediction latency?

A company wants to build a low-code ML pipeline using Vertex AI Pipelines and BigQuery ML. They need to train, evaluate, and deploy a model. Which TWO statements are correct about the integration between Vertex AI Pipelines and BigQuery ML? (Choose TWO.)

A healthcare company uses AutoML Tables to predict patient readmission risk. The dataset contains 500,000 rows and 200 features, including patient demographics, lab results, and medical history. The model accuracy is lower than expected. The engineer wants to improve performance using low-code techniques. Which THREE actions are most effective? (Choose THREE.)

Refer to the exhibit. A data scientist trained a BigQuery ML classification model to detect fraudulent transactions. The dataset has 95% non-fraud (class 0) and 5% fraud (class 1). The evaluation metrics show high accuracy (0.91) but low recall (0.60) for fraud detection. Which low-code approach should the data scientist take to improve recall without significantly sacrificing precision?

Refer to the exhibit. A data engineer is defining a Vertex AI Pipeline step to train a model. The pipeline fails with an error: "Failed to create vertex ai custom job: Invalid resource name." What is the most likely cause of the error?

You are an ML engineer at a logistics company. The company uses a Vertex AI Pipeline with BigQuery ML to train a model that predicts delivery delays based on weather, traffic, and historical order data. The pipeline runs daily and includes steps: (1) data extraction from BigQuery, (2) feature engineering using Dataflow, (3) model training with BigQuery ML (logistic regression), (4) model evaluation, and (5) conditional deployment to a Vertex AI Endpoint if accuracy > 0.85. Recently, the pipeline has been failing at step 5 with the error: "Vertex AI Endpoint creation failed: Quota limit of 1 endpoint per region exceeded." The company has already created one endpoint in the same region for another model. The pipeline is configured to create a new endpoint each time a model is deployed. The engineer needs to fix this with minimal changes to the pipeline code. Which course of action should the engineer take?

A retail company wants to build a customer churn prediction model using BigQuery ML. The data is stored in BigQuery tables and includes customer demographics, purchase history, and support interactions. The data scientist wants to experiment with different model types quickly without moving data to another environment. Which approach should they use?

A company wants to implement a document processing solution that extracts key information from invoices and receipts. They have limited ML expertise and want to use a pre-trained solution as much as possible. Which Google Cloud service should they use?

Which TWO are best practices for implementing a low-code ML solution using Vertex AI AutoML? (Choose 2)

Refer to the exhibit. A data analyst creates a BigQuery ML logistic regression model for churn prediction. The model evaluation shows high precision but low recall. Which change to the model creation would most likely improve recall?

A financial services company uses Vertex AI AutoML Tables to build a credit risk model. The dataset contains 500,000 rows and 50 features, including loan amount, credit score, debt-to-income ratio, and employment length. The target variable is binary: 'default' (1) or 'no default' (0). The data is highly imbalanced, with only 2% defaults. The data scientist trains a model with AutoML Tables using default settings. The evaluation metrics show an AUC of 0.85, but the confusion matrix reveals that the model predicts 'no default' for almost all cases, missing most defaults. The data scientist needs to improve the model's ability to identify defaults without significantly increasing false positives. They have limited time and cannot write custom code. What should they do?

A retail company wants to build a customer churn prediction model using AutoML Tables. They have a dataset with 5000 rows and 50 features, including customer ID, transaction history, and support tickets. The target is a binary column 'churned'. After training, the model shows high accuracy but low recall for the churned class. What is the most likely cause?

Drag and drop the steps to set up data lineage tracking for ML pipelines using Vertex AI Experiments in the correct order.

Match each model evaluation metric to its use case.

A marketing team wants to analyze customer reviews for sentiment without writing code. Which Google Cloud service should they use?

A company uses AutoML Tables to predict customer churn. The model's AUC is low. Which action is most likely to improve performance?

A data engineering team wants to orchestrate an ML pipeline that includes data preprocessing in Dataflow, AutoML training, and model deployment. They want to minimize operational overhead. Which approach is best?

A company wants to classify support ticket text into categories. They have labeled historical tickets. Which Google Cloud service allows them to train a custom classification model with no code?

A retailer uses BigQuery ML to build a linear regression model for sales forecasting. The model's evaluation shows high RMSE. Which step should they take first?

A team is using Vertex AI AutoML to train a forecasting model. They need to retrain the model weekly and only if the new week's data significantly changes the data distribution. What is the most efficient way to achieve this?

A non-technical user wants to build a binary classification model using Vertex AI. Which UI should they use?

A company deploys an AutoML Vision model for real-time defect detection. They notice high inference latency during peak hours. Which configuration change can help?

An organization uses Vertex AI Pipelines to automate a model training workflow. They want to reuse previously trained models if the data hasn't changed. Which pipeline component best achieves this?

A data analyst wants to use low-code ML to analyze text data. Which TWO Google Cloud services are appropriate?

A company uses Vertex AI for AutoML training. Which THREE are best practices for managing model versions?

A team is architecting a low-code ML system for real-time predictions with AutoML. Which THREE considerations are critical for production?

The pipeline fails during the evaluate component with error "Model not found". What is the most likely cause?

A user receives this error when deploying an AutoML model. What should they do?

A developer sees this error when calling the endpoint. What is the most likely cause?

A company wants to predict customer churn using a dataset with 10,000 rows and 20 features. They have no ML expertise. Which low-code solution should they use?

A data analyst wants to create a classification model directly in BigQuery using SQL. Which feature should they use?

A company needs to extract entities (e.g., names, dates) from customer emails using a pre-trained model. Which service should they use?

A company uses Vertex AI AutoML to train a vision model, but the model has low accuracy. What should they do first?

A team wants to deploy a BigQuery ML model for online prediction. Which approach should they take?

A company needs to perform sentiment analysis on streaming social media data. Which architecture should they use?

A company wants to use low-code ML for time series forecasting with 5 years of hourly data. They need to incorporate holiday effects. Which solution best meets these requirements?

A data scientist uses Vertex AI Pipelines to orchestrate an ML workflow. They want to reuse a component from Google's curated repository. What is the recommended way to incorporate it?

A company is using AutoML Vision for object detection and observes high latency for online predictions. What can they do to reduce latency?

Which TWO of the following are low-code machine learning solutions on Google Cloud?

Which TWO are best practices when deploying AutoML models to production?

Which THREE actions can help improve the performance of a BigQuery ML model?

Refer to the exhibit. What does this command do?

Refer to the exhibit. What is this Cloud Build step doing?

Refer to the exhibit. What is being configured?

A small business wants to build a sentiment analysis model for customer reviews without writing any code. They have a small labeled dataset with 500 positive and 500 negative reviews. Which Google Cloud service should they use?

A company uses AutoML Tables (Vertex AI AutoML for tabular data) to predict customer churn. Their dataset has 10,000 rows and 50 features. During training, they notice the model's performance is poor. Which approach is most likely to improve the model?

A financial institution wants to use Natural Language API for sentiment analysis on customer feedback, but the domain-specific language (e.g., 'bullish', 'bearish') is not correctly classified. They have 200 labeled examples. Which approach minimizes coding effort while improving accuracy?

A team needs to quickly create a visual interface for data exploration and model building without writing code. They want to run AutoML jobs and visualize results. Which Google Cloud tool should they use?

A company uses Vertex AI Pipelines to orchestrate an AutoML tabular training step followed by a BigQuery ML evaluation step. The pipeline fails because the output of the AutoML step (a model resource name) is not being passed to the BigQuery step. What is the most likely cause?

A global e-commerce company uses BigQuery ML to forecast daily sales for 10,000 products. They use a time-series model with a horizon of 7 days. Recently, forecasts for a specific product category have been consistently too high. They suspect the model is not capturing a new seasonal pattern. Which action should they take first to diagnose the issue?

A startup wants to build a product recommendation engine without writing custom training code. They have user-item interaction data stored in BigQuery. Which Google Cloud service should they use?

A data analyst wants to use Vision API to detect custom objects in manufacturing images, but the pre-trained API does not recognize their specific components. They have 1000 labeled images. Which path offers the fastest time-to-value with minimal coding?

A company has a pipeline that uses Vertex AI Pipelines to fetch data from BigQuery, preprocess with Dataflow (without code?), then train an AutoML model, and deploy. However, they want to reduce cloud costs. The pipeline runs hourly. Which change will most reduce compute costs while maintaining throughput?

Which TWO of the following are benefits of using BigQuery ML for low-code model development?

Which THREE of the following are valid best practices when using Vertex AI AutoML for tabular data?

Which THREE of the following are supported output types for BigQuery ML?

What is the most likely cause of the error?

What is the root cause of the failure?

A retail company has been using Vertex AI AutoML to predict store-level demand for each product. They have a pipeline that runs nightly: data is extracted from BigQuery, preprocessed via Dataflow, and then used to train a new AutoML model each night. The model is deployed to a Vertex AI Endpoint for real-time inference. After two months, they notice that predictions for a new product category (recently launched) are consistently inaccurate, with predicted sales far exceeding actuals. They suspect data drift due to the new category. The data scientist has limited coding skills and wants a low-code solution. Which course of action should they take to improve predictions for the new category?

A retail company wants to build a product recommendation system using customer purchase history and product attributes. They have limited ML expertise and want to minimize custom code. Which approach should they choose?

A healthcare startup deployed a Vertex AI AutoML Vision model to detect anomalies in medical images. The model performs well on the test set but has high latency in production, exceeding the 2-second SLA. The images are stored in Cloud Storage and are processed via a Cloud Function triggered by new uploads. What is the most likely cause?

A data scientist wants to use Vertex AI Pipelines to automate a low-code ML workflow. Which two statements are correct regarding best practices? (Choose TWO.)

A manufacturing company uses AutoML Tables to predict equipment failure. They want to improve model performance without increasing manual effort. Which three actions should they take? (Choose THREE.)

A financial services firm uses Vertex AI AutoML Natural Language to classify customer feedback into categories (positive, neutral, negative). They notice that the model performs poorly on neutral and negative classes, with high false negatives for negative. The dataset has 10,000 samples: 8,000 positive, 1,000 neutral, 1,000 negative. They have trained the model with automatic data split and default hyperparameters. Which course of action should they take to improve classification of minority classes?

An e-commerce company deployed a Vertex AI AutoML Tables model to predict customer churn. The model is served via a private endpoint with a dedicated machine type n1-standard-4. After a week, they observe that 5% of predictions fail with 'Request timed out' error. The average prediction time is 1.2 seconds but spikes to 4 seconds during peak hours. The input data is 50 features. They have enabled autoscaling with a min node count of 1 and max of 5. Which action is most likely to resolve the timeout issue without increasing complexity?

A marketing agency uses Vertex AI AutoML Vision to classify social media images into brand logos and generic content. They have 5,000 images per class. The model achieves 95% accuracy on validation set, but in production it misclassifies many images that contain logos in unusual angles or lighting. They have limited ML expertise and want to improve robustness. Which action should they take?

A logistics company uses Vertex AI AutoML Tables to predict delivery delays based on order attributes, weather data, and traffic data. The model is retrained weekly using a Vertex AI Pipeline that runs a BigQuery query to get training data, then triggers AutoML training. Recently, the pipeline fails with the error 'Dataset not found' when the AutoML training step starts. The BigQuery query runs successfully and outputs a table. Which is the most likely cause?

A data analyst wants to build a binary classification model using a low-code ML solution on Google Cloud. The dataset is stored in BigQuery and contains 500,000 rows with 20 features, including categorical and numerical columns. The analyst has minimal coding experience and needs to deploy the model as an API endpoint for real-time predictions. Which two Google Cloud services should the analyst use to accomplish this task with minimal code? Choose two options.

Refer to the exhibit. A data scientist runs the above BigQuery ML query to create a logistic regression model. After training, the model is evaluated using ML.EVALUATE. The evaluation shows poor performance with high bias. Which action would most likely improve the model's performance?

A company is using AutoML Tables to build a fraud detection model. The dataset has 10 million rows with 100 features, heavily imbalanced (fraud cases 0.1%). They used AutoML Tables with default settings and achieved high precision but very low recall. They need to deploy the model for real-time scoring on a Vertex AI Endpoint. The model will be used by a transaction processing system that requires low latency (<100 ms per prediction) and high throughput. The team is concerned about cost as the endpoint will receive up to 5,000 predictions per second. After deploying the model, they notice that the endpoint's latency occasionally spikes to over 1 second during peak hours. The team wants to optimize both model performance (recall) and serving performance. Which course of action should they take?