PMLE Architecting Low-Code ML Solutions Practice Test 5 — 15 Questions

Question 1

A data analyst wants to build a binary classification model to predict customer churn using SQL queries in BigQuery. Which BigQuery ML model type should they use?

Accepted Answer

LOGISTIC_REG. LOGISTIC_REG is the BigQuery ML model type for binary classification, predicting a binary outcome such as churn (yes/no). It uses logistic regression to estimate the probability of the binary outcome. This is the correct choice for predicting customer churn.

Answer

MATRIX_FACTORIZATION

Answer

LINEAR_REG

Answer

K_MEANS

Question 2

A retail company wants to generate product recommendations on their website using Google Cloud. They have historical transaction data and need a managed service that provides personalized recommendations like 'frequently bought together'. Which service should they use?

Accepted Answer

Recommendations AI. Recommendations AI is a purpose-built managed service on Google Cloud designed for retail recommendation use cases, including 'frequently bought together', 'recommended for you', and 'others you may like'. It ingests historical transaction and catalog data, trains retail-specific models automatically, and serves personalized recommendations via API without requiring ML expertise. This directly matches the requirement for a managed service providing personalized product recommendations.

Answer

BigQuery ML

Answer

Vertex AI Prediction

Answer

AutoML Tables

Question 3

A company needs to extract key fields from scanned invoices, such as invoice number and total amount, with high accuracy. They want a managed service and plan to use human review for low-confidence results. Which combination of services should they use?

Accepted Answer

Document AI and Human-in-the-Loop. Document AI is Google Cloud's managed document understanding service with specialized parsers (Invoice, Expense, Form) that extract structured fields like invoice number and total amount with high accuracy. Human-in-the-Loop (HITL) integrates directly with Document AI to route low-confidence predictions to human reviewers, whose corrections feed back to improve the processor. Together they satisfy the managed-service and human-review requirements in one pipeline.

Answer

Vision API and Natural Language API

Answer

Translation API and AutoML Vision

Answer

BigQuery ML and Vertex AI Prediction

Question 4

A healthcare organization wants to build a model to predict patient readmission risk using structured electronic health record (EHR) data. They need to train a model using SQL in BigQuery, but they also want to leverage AutoML's ability to automatically search for the best architecture. Which approach should they take?

Accepted Answer

Use BigQuery ML with the AUTOML_CLASSIFIER model type. BigQuery ML's AUTOML_CLASSIFIER model type automatically performs architecture search and hyperparameter tuning, making it ideal for users who want to leverage AutoML capabilities directly within SQL on structured EHR data. This approach avoids manual model selection while staying entirely within BigQuery's SQL interface, which is the stated requirement.

Answer

Use a pre-built Vision API model via BigQuery ML remote model

Answer

Use AutoML Tables with Vertex AI and export predictions

Answer

Use BigQuery ML with a DNN_CLASSIFIER and manual hyperparameter tuning

Question 5

A company wants to classify customer support emails into categories like 'billing', 'technical', or 'account'. They have labeled email text data. Which AutoML solution should they use?

Accepted Answer

AutoML Natural Language. AutoML Natural Language is designed for text classification tasks, including sentiment analysis, entity extraction, and content categorization. Since the company has labeled email text data and wants to classify emails into categories like 'billing', 'technical', or 'account', AutoML Natural Language is the correct choice. It handles text data natively and provides a simple interface to train custom models without requiring deep ML expertise.

Answer

AutoML Tables

Answer

AutoML Video

Answer

AutoML Vision

Question 6

A developer needs to transcribe phone calls with high accuracy for a call center analytics application. The audio is in English and has background noise. Which Speech-to-Text model should they choose?

Accepted Answer

telephony. The 'telephony' model is specifically optimized for transcribing audio from phone calls, which often includes background noise, low fidelity, and narrowband audio. It is designed to handle the unique characteristics of telephony audio, such as 8kHz sampling rate and compression artifacts, providing higher accuracy for call center analytics. Other models like 'latest_short' and 'latest_long' are general-purpose and may not perform as well on noisy phone calls.

Answer

latest_short

Answer

latest_long

Answer

Any model; they are all equivalent

Question 7

A company has an existing TensorFlow model for fraud detection that they want to use for predictions in BigQuery. They want to call the model from SQL queries without moving data out of BigQuery. How should they deploy the model?

Accepted Answer

Import the TensorFlow model directly into BigQuery ML. BigQuery ML (BQML) natively supports importing TensorFlow models directly, allowing you to use them for predictions via SQL without moving data out of BigQuery. This is the simplest and most efficient approach because it eliminates the need for external services or data export, leveraging BQML's built-in `CREATE MODEL` statement with the `OPTIONS(model_type = 'TENSORFLOW')` clause.

Answer

Deploy the model to Vertex AI Prediction and use a remote model in BigQuery ML

Answer

Export BigQuery data to Cloud Storage and use AI Platform Prediction

Answer

Use AutoML Tables to retrain the model in BigQuery ML

Question 8

A data scientist wants to evaluate the performance of a BigQuery ML classification model on a test dataset. Which function should they use?

Accepted Answer

ML.EVALUATE. ML.EVALUATE is the correct function because it computes classification metrics (e.g., precision, recall, accuracy, F1 score, ROC AUC) directly on a trained BigQuery ML model using a provided test dataset or evaluation input. This is the dedicated function for assessing model performance after training, aligning with the task of evaluating a classification model on held-out test data.

Answer

ML.PREDICT

Answer

ML.FEATURE_IMPORTANCE

Answer

ML.TRAIN

Question 9

A company wants to build a model to predict housing prices using BigQuery ML. They have a dataset with features like area, number of bedrooms, and location. Which TWO model types are appropriate for this regression task?

Accepted Answer

BOOSTED_TREE_REGRESSOR. BOOSTED_TREE_REGRESSOR (D) is appropriate because it is a tree-based ensemble method specifically designed for regression tasks, and BigQuery ML supports it via the `CREATE MODEL` statement with `model_type='BOOSTED_TREE_REGRESSOR'`. It handles non-linear relationships and interactions between features like area, bedrooms, and location, making it suitable for predicting continuous housing prices.

Answer

LOGISTIC_REG

Answer

K_MEANS

Answer

MATRIX_FACTORIZATION

Question 10

A company wants to analyze videos to detect objects and track their movement over time. Which TWO Google Cloud services are suitable for this task?

Accepted Answer

AutoML Video. AutoML Video supports object tracking, and Video Intelligence API provides both object detection and tracking. AutoML Vision is for images only, Natural Language for text, and Speech-to-Text for audio.

Answer

AutoML Vision

Answer

Speech-to-Text

Answer

Natural Language API

Question 11

A retail company wants to implement a recommendation system using Recommendations AI. They need to generate personalized recommendations for users based on their browsing history and purchase behavior. Which THREE recommendation types are available in Recommendations AI?

Accepted Answer

recommended-for-you. Recommendations AI offers several built-in recommendation types, and recommended-for-you (B) is correct because it produces personalized product suggestions for a user based on that user's own browsing history, purchase behavior, and other interaction events. others-you-may-like (C) is also correct because it generates personalized recommendations related to a specific item the user is currently viewing, which fits the retail scenario of tailoring suggestions from user behavior. frequently-bought-together (D) is correct because it recommends complementary products commonly purchased alongside a given item, a standard Recommendations AI type for cross-sell use cases. The unmarked options do not belong: trending-now (A) and most-popular (E) are not valid Recommendations AI recommendation type identifiers, as the platform's types are named like recommended-for-you, others-you-may-like, frequently-bought-together, and similar-item, rather than those generic popularity labels.

Answer

trending-now

Answer

most-popular

Question 12

A company wants to transcribe audio from customer service calls and then analyze the sentiment of the transcribed text. Which TWO Google Cloud services should they use?

Accepted Answer

Natural Language API. Speech-to-Text (option C) is correct because it converts audio from the customer service calls into written text, which is the required transcription step. Natural Language API (option A) is correct because it performs sentiment analysis on text, allowing the company to analyze the sentiment of the transcribed call content. Document AI (option B) is not appropriate here because it processes documents and forms rather than audio or general sentiment analysis. Translation API (option D) only translates text between languages and does not transcribe audio or analyze sentiment. Vision API (option E) analyzes images, so it cannot handle audio transcription or text sentiment analysis.

Answer

Document AI

Answer

Translation API

Answer

Vision API

Question 13

A financial services firm wants to predict loan default risk using a dataset with 30,000 labeled examples and 25 numeric and categorical features. Their team includes SQL analysts but no Python developers, and they want to minimize operational overhead. They decide to use BigQuery ML. Which model type should they use to achieve the best predictive performance while keeping the solution low-code?

Accepted Answer

BOOSTED_TREE_CLASSIFIER. For a binary classification task with a moderate-sized tabular dataset and a low-code requirement, BigQuery ML's BOOSTED_TREE_CLASSIFIER offers an excellent balance of accuracy and ease of use. It automatically handles categorical features and non-linear relationships, often outperforming linear models. It requires only SQL to train and deploy, aligning with the team's skills and minimizing operational overhead.

Answer

DNN_CLASSIFIER

Answer

KMEANS

Answer

LOGISTIC_REG

Question 14

A marketing team needs to build a model that predicts whether a customer will respond to a promotional email. They have a BigQuery table with 2 million rows and 30 features, and they want to avoid writing any Python code. They require an explainable model and the ability to generate predictions directly in SQL. Which approach should they use?

Accepted Answer

Create a BigQuery ML logistic regression model using CREATE MODEL with model_type='LOGISTIC_REG', then use ML.PREDICT for scoring.. BigQuery ML enables training and prediction using SQL, which perfectly matches the team's need to avoid Python. Logistic regression is a standard binary classification model that provides interpretable coefficients, satisfying explainability. Creating the model with CREATE MODEL and scoring with ML.PREDICT keeps the entire workflow within BigQuery, leveraging its scalability for 2 million rows. Other options either require coding, use inappropriate algorithms, or involve external services that complicate the low-code requirement.

Answer

Use BigQuery ML to create a k-means clustering model to segment customers, then manually assign response probabilities based on cluster averages.

Answer

Export the data to Cloud Storage and train a custom TensorFlow model using Vertex AI Training with a pre-built container.

Answer

Use AutoML Tables (now Vertex AI Tabular) to train a model, then deploy it to an endpoint and call it from BigQuery using a remote function.

Question 15

A marketing team wants to automatically categorize customer feedback emails into topics such as 'billing', 'technical support', or 'general inquiry'. They have a dataset of 5,000 labeled emails and want to build a custom model with minimal coding effort. Which Google Cloud service should they use?

Accepted Answer

AutoML Natural Language. AutoML Natural Language is purpose-built for custom text classification with minimal coding. It allows training on labeled data to recognize specific categories like billing or technical support. The other services either provide only pre-trained general models or are designed for different modalities, making AutoML Natural Language the correct choice for this low-code custom classification task.

Answer

Dialogflow CX

Answer

Vision API

Answer

Natural Language API