PMLE Architecting Low-Code ML Solutions Practice Test 2 — 15 Questions

Question 1

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?

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

Accepted Answer

The model is automatically evaluated on a held-out test set if data splitting is enabled. When data splitting is enabled in BigQuery ML, the `CREATE MODEL` statement automatically reserves a portion of the input data as a held-out test set. After training completes, BigQuery ML evaluates the model on this test set and reports metrics like mean absolute error and R², without requiring any manual split or separate evaluation step.

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The analyst must call ML.TRAIN after CREATE MODEL to start training

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The trained model is stored in Cloud Storage

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The model must be exported to Vertex AI for prediction

Question 2

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?

Accepted Answer

Use BigQuery ML with CREATE MODEL and LOGISTIC_REG model type.. BigQuery ML allows users to create and train binary classification models directly on data in BigQuery using SQL, with no need to move data or have deep ML expertise. The LOGISTIC_REG model type implements logistic regression, a standard algorithm for binary classification, and the CREATE MODEL statement handles all the underlying training infrastructure, making it ideal for a data analyst with limited ML skills.

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Use Cloud Datalab to train an XGBoost model on BigQuery data.

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Train a model using Vertex AI Workbench with a custom container.

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

Question 3

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?

Accepted Answer

Recommendations AI with the 'frequently bought together' model type. Recommendations AI provides pre-built models for retail recommendations, including 'frequently bought together'. AutoML Tables could be used but requires more customisation. Vertex AI Prediction and BigQuery ML are not purpose-built for this use case and would require more development.

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Vertex AI Prediction with a custom TensorFlow model

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BigQuery ML with MATRIX_FACTORIZATION model type

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Vertex AI AutoML Tables

Question 4

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?

Accepted Answer

Document AI with a form parser processor and enable Human-in-the-Loop for high-risk applications. Document AI provides specialised processors for document parsing (including form parser). Human-in-the-Loop (HITL) is a feature of Document AI that allows human review for high-risk documents. Cloud Vision API is for image analysis, not document parsing. Vertex AI AutoML Vision is for image classification/object detection.

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Document AI with a custom extractor processor and use Cloud Functions for human review

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Cloud Vision API to detect text and tables, then send to Cloud Dataflow for processing

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Vertex AI AutoML Vision to train a custom model for document parsing

Question 5

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?

Accepted Answer

Speech-to-Text API with the latest_long model. Speech-to-Text is a pre-built API for transcribing audio to text. It is ready to use without training. AutoML NLP is for text classification, not transcription. Vertex AI Workbench and Translation API are not relevant.

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Translation API to translate the audio

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AutoML NLP to train a transcription model

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Vertex AI Workbench to train a custom speech recognition model

Question 6

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?

Accepted Answer

ARIMA_PLUS. ARIMA_PLUS is the correct choice because it is specifically designed for time-series forecasting, such as predicting daily sales over a future horizon. BigQuery ML's ARIMA_PLUS model automatically handles seasonality, trend, and holiday effects, making it ideal for 30-day sales forecasts from historical data.

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LINEAR_REG

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BOOSTED_TREE_REGRESSOR

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K_MEANS

Question 7

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?

Accepted Answer

AutoML Vision for image classification. Document AI provides custom extractors but not classification. AutoML Vision can train a custom image classification model. Vertex AI AutoML Tables is for tabular data, not images. Cloud Vision API is for pre-built image analysis, not custom classification.

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Vertex AI AutoML Tables

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Document AI custom extractor

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Cloud Vision API with label detection

Question 8

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?

Accepted Answer

ML.FEATURE_IMPORTANCE. ML.FEATURE_IMPORTANCE returns feature importance for tree-based models in BigQuery ML. ML.EVALUATE gives evaluation metrics, ML.PREDICT gives predictions, ML.EXPLAIN_PREDICT gives local explanations with SHAP values.

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ML.EXPLAIN_PREDICT

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ML.EVALUATE

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ML.PREDICT

Question 9

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?

Accepted Answer

Frequently bought together. The 'Frequently bought together' model type is the correct choice because it directly leverages collaborative filtering based on co-purchase patterns in historical transaction data, enabling the retailer to recommend items commonly purchased alongside the current cart contents. This model requires minimal ML expertise and can be quickly deployed using pre-built Recommendations AI templates, as it does not require user-level personalization or real-time session data.

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Question 10

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?

Accepted Answer

Video Intelligence API. The Video Intelligence API is the correct choice because it provides pre-trained models for real-time video analysis, including object detection, with minimal setup. It is designed to extract metadata from video streams without requiring custom training, making it ideal for real-time live video feeds. Vertex AI AutoML Video, in contrast, requires custom training and is not pre-trained for immediate use.

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Vertex AI AutoML Video

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Document AI

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Cloud Vision API

Question 11

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.

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BOOSTED_TREE_CLASSIFIER

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LOGISTIC_REG

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DNN_REGRESSOR

Question 12

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. Vertex AI supports importing SavedModel directly without retraining. BigQuery ML can import TensorFlow models but is for batch predictions. Vertex AI Prediction is the standard for online predictions. AutoML Tables is for training new models.

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

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Use Vertex AI AutoML Tables to retrain the model

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Use Cloud Functions to run the model for each prediction request

Question 13

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. Natural Language API provides pre-trained sentiment analysis. AutoML NLP is for custom models. Speech-to-Text is for audio transcription. Translation API is for language translation.

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Translation API

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Speech-to-Text API

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AutoML NLP

Question 14

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. Document AI with a specialized invoice parser can extract fields. Human-in-the-Loop (HITL) is a feature of Document AI that enables human review. Cloud Vision API is not specialized for invoices. AutoML Tables is for tabular models. Cloud Translation is not needed.

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Cloud Vision API for OCR

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AutoML Tables to predict missing fields

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Cloud Translation API to translate invoices

Question 15

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?

Accepted Answer

AutoML Vision. AutoML Vision is the only low-code service among the options that allows you to train custom image classification models using labeled images without writing code. It directly matches the requirement to classify product images into categories. The other options are unsuitable: AutoML Tables is for tabular data, Cloud Vision API using product search is for finding similar products rather than custom classification, Vertex AI Workbench with a custom TensorFlow model requires custom coding, and Document AI is designed for document processing.

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AutoML Tables

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Cloud Vision API using product search

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Vertex AI Workbench with a custom TensorFlow model

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Document AI custom extractor