PMLE Architecting low-code ML solutions — 20 Questions

Question 1

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?

Accepted Answer

Use BigQuery ML's matrix factorization model (CREATE MODEL with model_type='matrix_factorization') to train directly on historical interaction data.. Option D is correct because BigQuery ML's matrix factorization model (model_type='matrix_factorization') is purpose-built for recommendation systems using implicit or explicit feedback data. It trains directly on historical interaction data (e.g., user-item purchases) without requiring manual feature engineering, aligning with the goal of minimizing low-code ML effort. This approach leverages BigQuery's native SQL interface and scales automatically, making it ideal for the described e-commerce scenario.

Answer

Use a pre-built recommendation model from Vertex AI Model Garden and deploy it to an endpoint.

Answer

Write a custom TensorFlow model using the Vertex AI Training service and deploy it via Vertex AI Prediction.

Answer

Export the data to CSV and use AutoML Tables to train a recommendation model.

Question 2

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?

Accepted Answer

BigQuery ML with CREATE MODEL statement using SQL.. Option C is correct because BigQuery ML allows a data scientist to train a binary classification model directly in BigQuery using a `CREATE MODEL` SQL statement, without writing any code or moving data. This is the fastest low-code approach for users with limited ML experience, as it leverages familiar SQL syntax and runs entirely within BigQuery's serverless infrastructure.

Answer

Vertex AI Workbench with a built-in scikit-learn notebook.

Answer

Dataflow with a TensorFlow pipeline.

Answer

AutoML Tables with a direct BigQuery connection.

Question 3

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?

Accepted Answer

Add retry logic with exponential backoff to the deployment step in the pipeline.. Option D is correct because implementing retry logic with exponential backoff is a resilient pattern for transient quota errors. Option A is wrong because increasing quota requires a support ticket and may not be granted immediately. Option B is wrong because using a custom container does not address quota limits. Option C is wrong because sequential execution does not prevent quota errors.

Answer

Run the pipeline steps sequentially with longer wait times.

Answer

Switch to deploying models using a custom container on Compute Engine.

Answer

Request a permanent quota increase for Vertex AI Endpoints.

Question 4

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?

Accepted Answer

Let AutoML Tables handle the imbalance automatically; it has built-in techniques for class imbalance.. AutoML Tables has built-in techniques to handle class imbalance, such as automatically adjusting class weights and using stratified sampling during training. This allows the model to learn from the minority class without requiring manual data preprocessing, making it the most effective and simplest approach for users with limited ML expertise.

Answer

Downsample the majority class to balance the dataset.

Answer

Use a custom loss function in the training configuration.

Answer

Oversample the minority class using SQL before training.

Question 5

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?

Accepted Answer

Deploy the model to a Vertex AI Endpoint directly from Model Garden.. Option D is correct because deploying directly to a Vertex AI Endpoint from Model Garden eliminates all infrastructure management. Vertex AI handles model serving, scaling, and monitoring automatically, which is ideal for a team with no experience in model serving infrastructure. This is a fully managed, serverless deployment that requires no containerization or server configuration.

Answer

Deploy the model as a Cloud Function invoked by Cloud Storage events.

Answer

Deploy the model as a Cloud Run service using a custom Docker container.

Answer

Deploy the model on App Engine flexible environment.

Question 6

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?

Accepted Answer

Export the trained model as a SQL function using the EXPORT MODEL statement, then use it for predictions.. Option A is correct because exporting the trained model as a SQL function via `EXPORT MODEL` converts the linear regression coefficients into a persistent SQL UDF, eliminating the overhead of model loading and serialization during each `ML.PREDICT` call. This approach is low-code (no external pipeline) and cost-effective since predictions are executed as standard SQL without consuming BigQuery ML slot resources for model inference.

Answer

Create a Dataflow pipeline to precompute predictions and store them in a separate table.

Answer

Use a materialized view to precompute the prediction features.

Answer

Increase the BigQuery compute capacity by reserving more slots.

Question 7

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.)

Accepted Answer

Vertex AI Pipeline steps can include BigQuery ML training via the BigQueryQueryJob operator.. Option D is correct because Vertex AI Pipelines can integrate with BigQuery ML by using the BigQueryQueryJob operator to execute SQL-based training queries, such as `CREATE MODEL`, as a pipeline step. This allows you to orchestrate BigQuery ML model training within a Vertex AI Pipeline, enabling a low-code ML workflow.

Answer

BigQuery ML models are automatically stored in Vertex AI Model Registry after training.

Answer

BigQuery ML supports hyperparameter tuning using the CREATE MODEL statement.

Answer

Vertex AI Pipelines supports automatic retry of failed steps due to transient errors.

Question 8

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.)

Accepted Answer

Remove highly correlated features using AutoML Tables' built-in feature importance analysis.. Option B is correct because AutoML Tables provides built-in feature importance analysis that can identify and remove highly correlated features, which reduces noise and multicollinearity, often improving model performance without manual intervention. This is a low-code technique that leverages the platform's automated capabilities to streamline feature selection.

Answer

Increase the training time budget to the maximum allowed.

Answer

Use a custom model architecture via AutoML Tables advanced options.

Question 9

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?

Accepted Answer

Use the ML.PREDICT function with a lower classification threshold (e.g., 0.3 instead of 0.5) to capture more positive cases.. Option A is correct because lowering the classification threshold in ML.PREDICT (e.g., from 0.5 to 0.3) causes the model to classify more transactions as fraud, directly increasing recall. This is a low-code adjustment that does not require retraining or complex feature engineering, and it allows the data scientist to trade off precision for recall as needed.

Answer

Apply feature selection to reduce the number of features and focus on the most predictive ones.

Answer

Increase the number of training iterations by setting the MAX_ITERATIONS option to a higher value.

Answer

Re-train the model using AutoML Tables with class weights to penalize false negatives more heavily.

Question 10

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?

Accepted Answer

The training_data input should be a Vertex AI Dataset resource, not a simple string.. Option C is correct because Vertex AI Pipeline steps that use a CustomJob to train a model require the training data input to be a Vertex AI Dataset resource (a Dataset object), not a plain string. When a string is passed instead of a Dataset resource, the pipeline attempts to create a custom job with an invalid resource name, as the backend expects a properly formatted Dataset resource name (e.g., projects/{project}/locations/{location}/datasets/{dataset_id}). This mismatch triggers the 'Invalid resource name' error.

Answer

The container image URI is incorrect; it should be from gcr.io/vertex-ai/training.

Answer

The output artifact schema is missing the 'type' property.

Answer

The machine type n1-standard-4 is not supported for Vertex AI training.

Question 11

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?

Accepted Answer

Modify the deployment step to check if an endpoint already exists and, if so, deploy a new model version to the existing endpoint instead of creating a new one.. Option D is correct because it directly addresses the root cause: the pipeline fails because it tries to create a new endpoint each time, exceeding the regional quota of one endpoint. By modifying the deployment step to check for an existing endpoint and deploying a new model version to it, the engineer avoids quota issues without altering the pipeline's core logic or requiring external approvals. This approach leverages Vertex AI's model versioning capability, which allows multiple model versions under a single endpoint, aligning with minimal code changes.

Answer

Submit a quota increase request to Google Cloud for Vertex AI Endpoints in the current region.

Answer

Change the region in the pipeline configuration to a region with available endpoint quota.

Answer

Remove the accuracy threshold and deploy every model automatically to a pre-created endpoint.

Question 12

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?

Accepted Answer

Use BigQuery ML to create and evaluate models directly in BigQuery.. BigQuery ML (BQML) allows data scientists to create, train, and evaluate machine learning models directly in BigQuery using SQL, without moving data to another environment. This approach supports rapid experimentation with various model types (e.g., logistic regression, boosted trees, deep neural networks) and is ideal for the stated requirement of quick iteration while keeping data in place.

Answer

Use Cloud Composer to orchestrate a custom training pipeline on Vertex AI.

Answer

Use AI Platform Notebooks with pandas and scikit-learn.

Answer

Export the data to Cloud Storage and use Vertex AI AutoML Tables.

Question 13

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?

Accepted Answer

Document AI with a pre-trained invoice processor.. Document AI with a pre-trained invoice processor is the correct choice because it provides a fully managed, pre-trained solution specifically designed for extracting structured data (e.g., vendor name, invoice number, line items) from invoices and receipts. This aligns with the company's limited ML expertise and desire to use a pre-trained solution, requiring no custom model training or complex coding.

Answer

AutoML Natural Language with custom entity extraction.

Answer

Vertex AI Workbench with custom Python scripts.

Answer

Cloud Vision API with OCR.

Question 14

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

Accepted Answer

Use the AutoML recommended data split (train/validation/test) to avoid overfitting.. Option A is correct because AutoML's recommended data split (train/validation/test) is designed to prevent overfitting by ensuring the model is evaluated on unseen data. AutoML automatically handles the split ratio (e.g., 80/10/10) and stratification, which is a best practice for low-code ML solutions where manual split logic is error-prone.

Answer

Impute missing values manually before uploading the dataset.

Answer

Normalize numerical features to zero mean and unit variance.

Answer

Export the data and train a custom model with a different architecture.

Question 15

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?

Accepted Answer

Use ML.ADJUST_THRESHOLD to lower the classification threshold.. Option C is correct because lowering the classification threshold (e.g., from 0.5 to 0.3) will classify more customers as positive (churn), increasing recall (true positives / (true positives + false negatives)). In BigQuery ML, ML.ADJUST_THRESHOLD directly modifies the decision boundary, trading off precision for recall. This is the most direct way to address low recall without altering the model architecture or training data.

Answer

Drop more columns to reduce overfitting.

Answer

Increase the training data by including customers without churn dates.

Answer

Change model_type to 'BOOSTED_TREE_CLASSIFIER'.

Question 16

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?

Accepted Answer

Enable 'Enable weighted evaluation' and set the optimization objective to 'Maximize recall at a specific recall@P%' with a target precision of 0.5.. Option C is correct because AutoML Tables allows you to set a custom optimization objective to handle class imbalance without custom code. By enabling weighted evaluation and setting the objective to 'Maximize recall at a specific recall@P%' with a target precision of 0.5, the model will be tuned to prioritize identifying defaults (recall) while maintaining a specified precision level, directly addressing the need to catch more defaults without a massive increase in false positives.

Answer

Manually split the data into a stratified train/test set to ensure the same proportion of defaults in each.

Answer

Train multiple models with different algorithms (e.g., XGBoost, Random Forest) and blend them using a custom script.

Answer

Under-sample the majority class to create a balanced dataset and retrain.

Question 17

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?

Accepted Answer

The churned class is underrepresented, causing the model to favor the majority class.. Option C is correct because in imbalanced datasets, AutoML Tables optimizes for overall accuracy, which can be high if the majority class dominates. Low recall for the churned class indicates the model predicts most instances as non-churned, a classic symptom of class imbalance. AutoML Tables provides class weighting and sampling options to mitigate this, but without them, the model favors the majority class.

Answer

The dataset is too small for AutoML to train effectively.

Answer

The features are not normalized, leading to biased predictions.

Answer

The dataset includes a unique customer ID feature, causing overfitting.

Question 18

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

Question 19

Match each model evaluation metric to its use case.

Question 20

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

Accepted Answer

Cloud Natural Language API. The Cloud Natural Language API (option D) is the correct choice because it provides pre-trained models for sentiment analysis, entity recognition, and syntax analysis via a simple REST API, requiring no code beyond sending HTTP requests. This aligns perfectly with the requirement to analyze customer reviews for sentiment without writing code, as the API abstracts all ML complexity.

Answer

Cloud Dataflow

Answer

Vertex AI Workbench

Answer

BigQuery ML