PMLE Architecting Low-Code ML Solutions Practice Test 3 — 15 Questions

Question 1

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 2

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 3

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 specialised for parsing invoice documents. OCR Processor extracts text only, Form Parser extracts form fields, and Contract Parser is for legal contracts.

Answer

OCR Processor

Answer

Contract Parser

Answer

Form Parser

Question 4

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 designed for image classification, object detection, and segmentation. AutoML Tables is for tabular data, AutoML NLP for text, and AutoML Video for video.

Answer

AutoML Tables

Answer

AutoML Video

Answer

AutoML NLP

Question 5

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 6

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?

Accepted Answer

telephony. The 'telephony' model is optimised for audio from phone calls (8 kHz). 'latest_long' is for long-form non-telephony audio, 'latest_short' for short utterances, and there is no 'default' model.

Answer

latest_short

Answer

latest_long

Answer

default

Question 7

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 8

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 9

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 designed for time-series forecasting and automatically handles seasonality, trends, and holidays. K_MEANS is for clustering, MATRIX_FACTORIZATION for recommendations, and LINEAR_REG for simple regression.

Answer

K_MEANS

Answer

MATRIX_FACTORIZATION

Answer

LINEAR_REG

Question 10

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 SavedModels with model_type='tensorflow'. ML.TRAIN is for training, not importing. ML.PREDICT requires a model in BigQuery, not directly on Cloud Storage. No conversion is needed.

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 11

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 provides feature attributions for each prediction. ML.EVALUATE returns aggregate metrics, ML.FEATURE_IMPORTANCE gives global importance, and ML.PREDICT only returns predictions.

Answer

ML.EVALUATE

Answer

ML.FEATURE_IMPORTANCE

Answer

ML.PREDICT

Question 12

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?

Accepted Answer

Create a remote model with model_type='VERTEX_AI' and use ML.PREDICT with image URIs.. BigQuery ML remote models allow you to invoke Vertex AI pre-trained models via the `model_type='VERTEX_AI'` setting. You can then use `ML.PREDICT` directly on Cloud Storage image URIs without needing to export or transform the image data, as BigQuery ML handles the URI resolution and passes the image to Vertex AI for classification.

Answer

Train a custom model in BigQuery ML with image data.

Answer

Export images to BigQuery as base64 and then use ML.PREDICT.

Answer

Use ML.PREDICT with IMAGE data type directly.

Question 13

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)

Accepted Answer

Set the target column to 'default'. For binary classification, the target column must be set. For imbalanced classes, 'maximize AUC' is recommended. AutoML Tables automatically handles feature engineering and hyperparameter tuning. Manual split is optional.

Answer

Enable automatic feature engineering

Answer

Disable hyperparameter tuning to save cost

Answer

Use Manual data split

Question 14

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)

Accepted Answer

Cloud Run. Video Intelligence API can analyse live streams with object detection. Cloud Run provides serverless compute to host the streaming pipeline. AutoML Video requires custom training, Vertex AI Predictions is for deployed models, Live Stream API is for ingestion.

Answer

Live Stream API

Answer

Vertex AI Predictions

Answer

AutoML Video

Question 15

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)

Accepted Answer

Invoice Parser Processor. The Invoice Parser Processor (C) is a specialized Document AI processor designed to extract structured data from invoices, including line items, which directly meets the requirement for extracting line items from a large volume of invoices. It is pre-trained on invoice layouts and can handle complex table structures, making it the correct choice for this task.

Answer

Online Processing

Answer

Custom Extractor