PMLE Architecting Low-Code ML Solutions Practice Test 4 — 15 Questions

Question 1

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?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

ML.FEATURE_IMPORTANCE. ML.FEATURE_IMPORTANCE is the BigQuery ML function specifically designed to return the relative importance of each input feature for a trained model, including boosted tree classifiers (Boosted Tree, XGBoost, Random Forest). It computes importance scores using the model's internal split-gain or weight-based metrics, giving a ranked list of features that most influence predictions. This is the direct, purpose-built answer for global feature attribution on a trained model.

Answer

ML.EXPLAIN_PREDICT

Answer

ML.EVALUATE

Answer

ML.PREDICT

Question 2

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?

Accepted Answer

DNN_CLASSIFIER. The DNN_CLASSIFIER model type in BigQuery ML is specifically designed for classification tasks using a deep neural network architecture. Since the problem is predicting customer churn (a binary classification problem) and the data engineer explicitly wants to use a deep neural network, DNN_CLASSIFIER is the appropriate choice.

Answer

BOOSTED_TREE_CLASSIFIER

Answer

LOGISTIC_REG

Answer

DNN_REGRESSOR

Question 3

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?

Accepted Answer

Upload the saved model to Vertex AI and create an endpoint for online predictions. The most efficient way to deploy a TensorFlow SavedModel for online predictions on Vertex AI is to upload the SavedModel to Vertex AI and create an endpoint. Vertex AI supports importing custom models in SavedModel format and deploying them to endpoints for low-latency online predictions. This leverages Vertex AI's managed infrastructure for scaling and serving.

Answer

Import the model into BigQuery ML using CREATE MODEL with model_type='TENSORFLOW'

Answer

Use Vertex AI AutoML Tables to retrain the model

Answer

Use Cloud Functions to run the model for each prediction request

Question 4

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?

Accepted Answer

Natural Language API. The Natural Language API is a pre-trained service that provides sentiment analysis out of the box, requiring no training. It can directly process text data from BigQuery (via integration or export) to classify sentiment as positive, negative, or neutral. This matches the requirement of using a pre-trained model with no training.

Answer

Translation API

Answer

Speech-to-Text API

Answer

AutoML NLP

Question 5

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?

Accepted Answer

Human-in-the-Loop (HITL) on Document AI. Option D is correct because Document AI's specialized Invoice parser processor is purpose-built to extract structured fields such as invoice number, invoice date, and total amount from invoice documents, which is exactly the extraction requirement in this pipeline. Option B is correct because Document AI's Human-in-the-Loop (HITL) feature lets you define confidence thresholds and route documents for manual review, which directly satisfies the requirement to have humans review invoices over $10,000 before downstream processing. Together, the Invoice parser handles automated field extraction while HITL provides the human verification step for high-value invoices. Option A is not the best fit because Cloud Vision API provides generic OCR without invoice-specific field extraction or built-in human review workflows. Option C is incorrect because AutoML Tables is a tabular ML service for structured data prediction, not for predicting missing fields in parsed documents. Option E is incorrect because Cloud Translation API only translates text and does not extract invoice fields or support human review.

Answer

Cloud Vision API for OCR

Answer

AutoML Tables to predict missing fields

Answer

Cloud Translation API to translate invoices

Question 6

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?

Accepted Answer

LINEAR_REG. The question specifies a linear regression model for predicting house prices, which is a regression task with a continuous target variable. BigQuery ML's LINEAR_REG model type is explicitly designed for linear regression, making it the correct choice for this use case.

Answer

BOOSTED_TREE_REGRESSOR

Answer

LOGISTIC_REG

Answer

DNN_REGRESSOR

Question 7

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

Accepted Answer

frequently-bought-together. The 'frequently-bought-together' model type in Google Cloud Recommendations AI is specifically designed to identify items that are commonly purchased in the same transaction, using co-occurrence analysis of historical purchase data. This directly matches the requirement to show items that are frequently bought together, leveraging association rule mining (e.g., Apriori algorithm) to generate recommendations.

Answer

recently-viewed

Answer

recommended-for-you

Answer

others-you-may-like

Question 8

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?

Accepted Answer

Invoice Parser. The Invoice Parser is a specialized Document AI processor pre-trained to extract structured data from invoices, including fields like invoice number, total amount, due date, and line items. It goes beyond generic OCR by understanding the invoice layout and returning key-value pairs. OCR Processor only extracts raw text without parsing fields, while Form Parser and Contract Parser are for general forms and contracts, respectively.

Answer

OCR Processor

Answer

Contract Parser

Answer

Form Parser

Question 9

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?

Accepted Answer

AutoML Vision. AutoML Vision is Vertex AI's service for training custom image classification and object detection models. With 100,000 labeled images across 50 categories, AutoML Vision can train a multi-class image classifier that learns visual features for each product category. It is the appropriate choice when the input data is images and the task is classification.

Answer

AutoML Tables

Answer

AutoML Video

Answer

AutoML NLP

Question 10

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?

Accepted Answer

Use the Natural Language API. The Natural Language API provides pre-built sentiment analysis with minimal setup. AutoML NLP would require custom training, BigQuery ML is for tabular data, and Vertex AI Prediction needs a deployed model.

Answer

Use Vertex AI Prediction with a pre-trained model

Answer

Use BigQuery ML with LOGISTIC_REG

Answer

Train a custom model using AutoML NLP

Question 11

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?

Accepted Answer

AUTOML_CLASSIFIER. (AUTOML_CLASSIFIER) is correct because it automatically performs architecture search and hyperparameter tuning to find the best model for binary classification tasks, such as customer churn prediction. This is ideal when the data engineer wants the model to be automatically tuned without manual intervention, as AutoML handles feature engineering, model selection, and tuning under the hood.

Answer

LOGISTIC_REG

Answer

BOOSTED_TREE_CLASSIFIER

Answer

DNN_CLASSIFIER

Question 12

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

Accepted Answer

Human-in-the-Loop (HITL). Human-in-the-Loop (HITL) is the correct feature because it allows Document AI to automatically process contracts while routing high-risk clauses to human reviewers for validation. This balances automation efficiency with the need for expert oversight on sensitive content, which is a core requirement for compliance-driven document processing.

Answer

Batch Processing

Answer

Online Prediction

Answer

AutoML Training

Question 13

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?

Accepted Answer

ARIMA_PLUS. ARIMA_PLUS is BigQuery ML's purpose-built time-series forecasting model, designed for exactly this scenario: forecasting future values from historical time-ordered data with minimal SQL coding. It automatically handles seasonality, holidays, and trend decomposition, and supports features like forecasting multiple time series at once. LINEAR_REG could technically model time as a feature, but it cannot capture seasonality or autocorrelation, making it unsuitable for demand forecasting.

Answer

K_MEANS

Answer

MATRIX_FACTORIZATION

Answer

LINEAR_REG

Question 14

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?

Accepted Answer

They can create a model using CREATE MODEL with model_type='tensorflow' and the path to the SavedModel.. BigQuery ML supports importing TensorFlow models in SavedModel format directly using CREATE MODEL with model_type='tensorflow' and the path to the SavedModel in Cloud Storage. This allows you to use the model for prediction in BigQuery without retraining or converting to a native format. The model is imported as a BigQuery ML model and can be used with ML.PREDICT.

Answer

They need to convert the model to a BigQuery ML native format first.

Answer

They must first retrain the model using ML.TRAIN on BigQuery.

Answer

They can use ML.PREDICT directly on the SavedModel in Cloud Storage.

Question 15

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?

Accepted Answer

ML.EXPLAIN_PREDICT. ML.EXPLAIN_PREDICT is the BigQuery ML function specifically designed to explain predictions from a trained model for a given set of input rows. It returns the prediction along with feature attributions (e.g., Shapley values) that show how each feature contributed to the prediction. This is the correct function for interpreting individual predictions from a boosted tree classifier.

Answer

ML.EVALUATE

Answer

ML.FEATURE_IMPORTANCE

Answer

ML.PREDICT