PMLE Architecting Low-Code ML Solutions Practice Test 6 — 15 Questions

Question 1

A retail company wants to forecast daily sales for each of its 500 stores for the next 90 days. They have three years of historical daily sales data stored in BigQuery, including promotions, holidays, and store attributes. The data science team has minimal ML expertise and wants to use SQL to build and deploy the model with minimal coding. Which approach should they use?

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

Accepted Answer

Create an ARIMA_PLUS model in BigQuery ML using the sales time series and specify the store ID as the time series identifier.. BigQuery ML's ARIMA_PLUS is purpose-built for time series forecasting and can handle multiple related time series by specifying an identifier column. It automatically models seasonality, holidays, and trends, and supports additional regressors. This requires only SQL, making it ideal for teams with limited ML expertise who need per-store forecasts.

Answer

Build a custom LSTM model in Vertex AI using TensorFlow, training on the historical sales data.

Answer

Use AutoML Forecasting in Vertex AI, exporting the data from BigQuery to Cloud Storage.

Answer

Train a linear regression model in BigQuery ML with store ID as a feature and date as a numeric feature.

Question 2

A marketing team wants to build a model that predicts customer lifetime value (CLV) using historical transaction data. They are comfortable with spreadsheets but have no coding experience. They need a low-code solution that automatically handles feature engineering and model selection. Which Google Cloud service should they use?

Accepted Answer

Vertex AI AutoML. Vertex AI AutoML is designed for users with limited ML expertise to build models without writing code. It automates feature engineering, model selection, and hyperparameter tuning. For predicting CLV from historical data, the marketing team can simply upload a dataset and let AutoML train a regression model. Other options require SQL or Python coding, which the team lacks.

Answer

AI Platform Training

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BigQuery ML

Answer

Vertex AI Workbench

Question 3

A retail company wants to build a demand forecasting model for thousands of product SKUs. They have historical sales data in BigQuery and limited ML expertise. They want to minimize coding and automatically handle seasonality and promotions. Which approach should they use?

Accepted Answer

Use BigQuery ML with the ARIMA_PLUS model type and provide the time series data.. BigQuery ML's ARIMA_PLUS is purpose-built for time series forecasting and automatically addresses seasonality, holidays, and outliers without manual feature engineering. It operates entirely within BigQuery using SQL, aligning with the team's limited ML expertise and desire to minimize coding. It scales to many time series, making it suitable for thousands of SKUs.

Answer

Use Dataflow to preprocess the data and then train a custom TensorFlow model on Vertex AI.

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Use BigQuery ML with the LINEAR_REG model type and include date features.

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Use Vertex AI AutoML Forecasting with the sales data exported to Cloud Storage.

Question 4

A hospital wants to build a system that automatically transcribes doctors' dictated notes into text and then identifies key medical terms such as diagnoses and medications. They have no ML expertise and want to use Google Cloud's pre-trained APIs. Which combination of services should they use?

Accepted Answer

Speech-to-Text API for transcription and Healthcare Natural Language API for medical term extraction.. The hospital needs two capabilities: converting audio to text and extracting medical terms. Speech-to-Text API handles audio transcription with high accuracy, and Healthcare Natural Language API is purpose-built for medical text analysis, identifying entities like medications and diagnoses. Both are pre-trained, requiring no ML expertise, and integrate easily. Other options use incorrect services for transcription or require custom model training, which is unnecessary given the availability of specialized pre-trained APIs.

Answer

Speech-to-Text API for transcription and AutoML Natural Language for custom entity extraction.

Answer

Text-to-Speech API for transcription and Natural Language API for entity extraction.

Answer

Dialogflow for transcription and Healthcare Natural Language API for medical term extraction.

Question 5

A marketing team wants to build a model to predict which customers are likely to churn. They have a BigQuery table with customer demographics, usage metrics, and a binary churn label. They want to use BigQuery ML and need to evaluate the model's performance. Which two statements are true regarding model evaluation in BigQuery ML? (Choose two.)

Accepted Answer

ML.CONFUSION_MATRIX can be used to visualize the confusion matrix of a classification model.. ML.EVALUATE computes standard classification metrics, and ML.CONFUSION_MATRIX provides a detailed breakdown of prediction outcomes. Both are essential for assessing a churn model. The other functions serve different purposes: ML.PREDICT for scoring, ML.TRAINING_INFO for training details, and ML.FEATURE_INFO for feature statistics.

Answer

ML.FEATURE_INFO returns the importance of each feature in the model.

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ML.PREDICT automatically calculates the model's accuracy on the input data.

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ML.TRAINING_INFO provides the evaluation metrics for the trained model.

Question 6

A financial services company wants to extract text and structured data from scanned loan application forms. They need a fully managed, low-code solution that can handle various form layouts and requires minimal machine learning expertise. Which Google Cloud service should they use?

Accepted Answer

Document AI. Document AI provides specialized document parsing capabilities, including form understanding and entity extraction, with pre-built models and the ability to train custom extractors with minimal effort. It is fully managed and designed for low-code document processing, making it the best fit for loan application forms.

Answer

Vision API

Answer

BigQuery ML

Answer

AutoML Natural Language

Question 7

A logistics company wants to classify shipping documents into categories such as invoice, packing slip, and bill of lading. They have a small set of labeled documents (about 50 per category) and want to use a low-code approach. They need a model that can be trained quickly and deployed for online predictions. Which Google Cloud service should they use?

Accepted Answer

AutoML Natural Language. AutoML Natural Language is designed for custom text classification with small labeled datasets. It provides a low-code environment, automatically handles preprocessing, and allows easy deployment for online predictions, making it ideal for classifying shipping documents.

Answer

Document AI

Answer

Natural Language API

Answer

BigQuery ML

Question 8

A small marketing team has a CSV file of 2,000 labeled customer support tickets (each with a category such as 'billing' or 'technical'). They have no ML engineers and want a fully managed, low-code way to train a text classification model that they can later call from their internal web app. Which Google Cloud service should they use?

Accepted Answer

Vertex AI AutoML text classification. AutoML text classification is purpose-built for teams with labeled text and no ML expertise: it ingests a CSV, trains, tunes, and serves a model behind a managed endpoint. The other services either require custom code, expect structured numeric features, or rely on a fixed taxonomy that cannot represent the team's custom ticket categories.

Answer

BigQuery ML with a CREATE MODEL statement using the LOGISTIC_REG model type

Answer

Vertex AI Pipelines with a custom Kubeflow component for text preprocessing

Answer

Cloud Natural Language API classifyText method

Question 9

A retail company wants to build a low-code ML solution to predict customer lifetime value (CLV) using historical transaction data stored in BigQuery. They have limited ML expertise and want to use BigQuery ML. Which two steps are necessary to train and evaluate a model using BigQuery ML? (Choose two.)

Accepted Answer

Use ML.EVALUATE to assess the model's performance on a test dataset.. Training a model in BigQuery ML requires the CREATE MODEL statement, which defines and trains the model using SQL. Evaluation is performed with ML.EVALUATE to compute metrics like RMSE or accuracy. These two steps are essential for building and assessing a model. Other steps like exporting or predicting are optional or occur after evaluation, and manual data splitting is unnecessary due to automatic splitting.

Answer

Use ML.PREDICT to generate predictions on new data.

Answer

Manually split the data into training and test sets using a CREATE TABLE statement.

Answer

Export the model to a TensorFlow SavedModel for deployment.

Question 10

A hospital's radiology department wants to build a model that flags possible pneumonia on chest X-rays. They have 8,000 labeled DICOM studies in a Cloud Storage bucket and no in-house data science staff. They need a managed service that can ingest the images, train a classifier, and provide an endpoint for their viewing software. What should they do?

Accepted Answer

Create a Vertex AI dataset from the images and train an AutoML image classification model. AutoML image classification is the managed, low-code route for a labeled image set: it reads images from Cloud Storage, trains and tunes a classifier, and serves predictions through an endpoint the viewing software can call. The alternatives either use fixed-label APIs, skip the essential fine-tuning, or apply a streaming analytics product instead of a diagnostic classifier.

Answer

Use Vertex AI Vision to build a pipeline that counts objects in the X-ray streams

Answer

Use the Cloud Vision API label detection feature on each X-ray

Answer

Deploy a pre-trained TensorFlow Hub model to Vertex AI Endpoints without retraining

Question 11

A media company wants to automatically transcribe and analyze customer support calls to identify common issues. They need a low-code solution that provides both transcription and sentiment analysis. Which Google Cloud service should they use?

Accepted Answer

Contact Center AI Insights. Contact Center AI Insights is a purpose-built, low-code solution for analyzing customer calls. It automatically transcribes audio, performs sentiment analysis, and extracts topics, providing actionable insights without custom development. Other options either lack sentiment analysis or require integration with multiple services, increasing complexity and coding effort. Thus, Contact Center AI Insights is the correct choice.

Answer

Speech-to-Text API

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Dialogflow CX

Answer

Natural Language API

Question 12

A financial institution wants to detect fraudulent transactions in real-time. They have a labeled dataset of historical transactions and want to build a custom model with minimal coding. They also need to integrate the model into an existing application that expects a REST API. Which Google Cloud service should they use to train and deploy the model with the least effort?

Accepted Answer

Vertex AI AutoML Tabular to train a model, then deploy it to a Vertex AI endpoint.. Vertex AI AutoML Tabular provides a low-code solution for training custom models on tabular data. It automatically handles feature engineering and model selection, and the trained model can be deployed to a Vertex AI endpoint, which offers a REST API for real-time predictions. This integration minimizes effort and meets the need for a custom model with REST API access. Other options either require more coding, add unnecessary complexity, or do not support real-time serving natively.

Answer

AI Platform Training with a custom TensorFlow model, then deploy to AI Platform Prediction.

Answer

Cloud Functions to call the BigQuery ML model directly via a REST API.

Answer

BigQuery ML to train a logistic regression model, then export the model to Vertex AI for deployment.

Question 13

A media company wants a low-code pipeline that ingests uploaded video files, detects scenes and on-screen text, and stores structured metadata for search. They prefer managed services and minimal custom code. Which TWO Google Cloud capabilities should they combine? (Choose two.)

Accepted Answer

Cloud Storage and Pub/Sub to trigger processing when new videos arrive. The Video Intelligence API supplies managed shot change and text detection on video, producing the scene and on-screen text metadata needed for search. Pairing it with Cloud Storage object notifications to Pub/Sub creates an event-driven trigger so each upload is analyzed automatically. Together they deliver a managed, low-code pipeline without custom frame extraction or model training.

Answer

Cloud Vision API for detecting labels and text in sampled video frames

Answer

Vertex AI Vision for building a custom object-tracking pipeline with trained detectors

Answer

Cloud Data Fusion for building a visual ETL pipeline for video ingestion

Question 14

A retail company wants to forecast daily sales for each of its 500 stores for the next 90 days. They have three years of historical daily sales data stored in BigQuery, including promotions, holidays, and store attributes. The data science team has minimal ML expertise and wants to use SQL to build and deploy the model with minimal coding. Which approach should they use?

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

Accepted Answer

BigQuery ML with the ARIMA_PLUS model. BigQuery ML's ARIMA_PLUS is the correct choice because it is a low-code, SQL-based time series forecasting model that automatically handles seasonality, trends, and holidays. It is designed for exactly this type of scenario: forecasting future values from historical time series data. Other options either require manual feature engineering, are not forecasting-specific, or involve significant coding, making ARIMA_PLUS the most efficient and appropriate solution.

Answer

Cloud Dataflow with a sliding window aggregation

Answer

Vertex AI Forecasting with a custom training container

Answer

AutoML Tables with a regression target

Question 15

A financial services firm wants relationship managers to query a natural-language interface such as 'show me customers likely to churn next quarter' and receive results from their BigQuery data warehouse. They want to minimize the code they maintain and keep data in BigQuery. Which approach best fits?

Accepted Answer

Enable Gemini in BigQuery to generate SQL from natural-language questions over the warehouse. Gemini in BigQuery converts natural-language prompts into SQL executed against the firm's existing tables, so relationship managers get ad hoc answers without maintaining query code and data never leaves BigQuery. The alternatives either require hand-written SQL per intent, apply an unsuitable forecasting model, or move data out of the warehouse into a batch training pipeline.

Answer

Export the warehouse to Cloud Storage and train a Vertex AI AutoML Tables model nightly

Answer

Build a custom chatbot with Dialogflow CX and hand-written SQL for each intent

Answer

Use BigQuery ML to train an ARIMA_PLUS model on customer activity to predict churn