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

Question 1

A healthcare provider needs to extract structured information from incoming PDF forms (e.g., patient intake forms). They want to automate data extraction without writing custom models. Which Google Cloud service should they use?

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

Accepted Answer

Document AI with a form parser processor. Document AI with a form parser processor is the correct choice because it is purpose-built for extracting structured data from PDF forms, including key-value pairs and tables, without requiring custom model development. It uses pre-trained models specifically for form understanding, making it ideal for automating intake form processing.

Answer

Natural Language API for entity extraction

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Vision API

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AutoML Vision for object detection

Question 2

A company wants to build a product recommendation engine for their e-commerce website. They have historical purchase data and user interaction logs. They want a managed service that can quickly generate personalized recommendations without building custom models. Which service should they use?

Accepted Answer

Recommendations AI. Recommendations AI is a fully managed Google Cloud service purpose-built for personalized product recommendations, ingesting purchase and interaction data and serving recommendations via API without custom model development. It handles training, tuning, and serving, which matches the requirement for a managed service that quickly generates personalized recommendations.

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Dataflow with TensorFlow

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BigQuery ML with MATRIX_FACTORIZATION

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AutoML Tables

Question 3

A media company wants to automatically moderate user-uploaded videos by detecting explicit content (e.g., violence, adult material). They need a solution that integrates with their video processing pipeline and scales to millions of videos. Which approach should they take?

Accepted Answer

Use Video Intelligence API with explicit content detection. Video Intelligence API provides explicit content detection specifically designed to identify violence, adult material, and other explicit content in videos. It is a managed service that scales automatically and can be integrated into video processing pipelines via its API. This is the most direct and scalable solution for moderating user-uploaded videos.

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Use AutoML Video to train a custom explicit content detection model

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Use Natural Language API on video transcripts

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Use Vision API to analyze each video frame

Question 4

A company wants to transcribe customer service calls in real-time to detect sentiment and identify urgent issues. They need a solution with low latency. Which combination of pre-built APIs should they use?

Accepted Answer

Speech-to-Text and Natural Language API. Real-time call transcription requires Speech-to-Text to convert audio to text with low latency, and Natural Language API to analyze that text for sentiment and entity/urgency detection. Together they form the standard streaming audio-analysis pipeline on Google Cloud. Speech-to-Text supports streaming recognition, and Natural Language provides sentiment and entity analysis on the resulting transcript.

Answer

Text-to-Speech and Natural Language API

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Speech-to-Text and Translation API

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Video Intelligence API

Question 5

A data analyst wants to train a binary classification model in BigQuery ML on a dataset of 10 million rows with 50 features. They need to evaluate the model's performance on a held-out test set. Which sequence of SQL statements should they run?

Accepted Answer

CREATE MODEL then ML.EVALUATE. To train and evaluate a model in BigQuery ML, you use CREATE MODEL to train the model, and then ML.EVALUATE to assess its performance on a held-out dataset. CREATE MODEL automatically splits the data into training and evaluation sets if specified, or you can use a separate table for evaluation. ML.EVALUATE returns metrics like accuracy, precision, recall, etc. This sequence is standard for model development in BigQuery ML.

Answer

CREATE MODEL then ML.FEATURE_IMPORTANCE

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ML.TRAIN then ML.EVALUATE

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CREATE MODEL then ML.PREDICT

Question 6

A financial services company uses Document AI to process loan applications. They want to ensure that any documents the model cannot process with high confidence are reviewed by a human before finalizing the decision. Which Document AI feature should they enable?

Accepted Answer

Human-in-the-Loop (HITL). Human-in-the-Loop (HITL) in Document AI allows you to route documents that the model processes with low confidence to human reviewers for validation or correction before finalizing. This directly matches the requirement to have a human review any documents the model cannot process with high confidence.

Answer

AutoML Tables model retraining

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Cloud DLP for data inspection

Answer

Increase the number of processors

Question 7

A company has a large dataset of labeled images (e.g., different species of plants). They want to train a custom image classification model with minimal effort and no prior ML experience. Which Google Cloud service should they use?

Accepted Answer

AutoML Vision. AutoML Vision is the correct choice because it allows users with no prior ML experience to train a custom image classification model using a simple graphical interface, requiring only labeled images as input. It automates model architecture search, hyperparameter tuning, and deployment, minimizing manual effort while delivering a production-ready model.

Answer

Cloud TPU

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Vertex AI Workbench with a custom TensorFlow model

Answer

Vision API

Question 8

A developer wants to add text translation to a mobile app. They need to translate user-generated content into multiple languages, and latency is critical. Which pre-built API should they use?

Accepted Answer

Translation API. Translation API provides fast, real-time translation for text. Natural Language API is for analysis. Text-to-Speech is for audio. Vision API is for images.

Answer

Vision API

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Text-to-Speech API

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Natural Language API

Question 9

A company is building a document processing pipeline using Document AI to extract data from invoices. They want to ensure high accuracy and handle edge cases where the model may be uncertain. Which THREE steps should they include in their pipeline?

Accepted Answer

Regularly retrain the processor using human-verified data. Option A is correct because regularly retraining the processor with human-verified data continuously improves extraction accuracy and adapts the model to new invoice variations and edge cases over time. Option D is correct because enabling Human-in-the-Loop (HITL) routes documents with low confidence scores to human reviewers, ensuring uncertain or edge-case extractions are validated and corrected before entering downstream systems. Option E is correct because a custom processor trained on the company's specific invoice format captures their unique layouts, fields, and terminology, yielding higher accuracy than a generic model. Option B is not appropriate because using the pre-built invoice parser without modification offers no tuning for the company's specific formats and provides no mechanism for handling uncertainty. Option C is not appropriate because AutoML Vision is an image classification service, not a document entity-extraction tool, and classifying invoice types does not extract the required field data or address low-confidence edge cases.

Answer

Use the pre-built invoice parser without any modifications

Answer

Use AutoML Vision to classify invoice types

Question 10

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?

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.

Answer

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 11

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.

Answer

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

Answer

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 12

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's Form Parser processor is purpose-built to extract structured key-value pairs and tables from forms like loan applications, and it natively integrates with Human-in-the-Loop (HITL) to route low-confidence or high-risk documents to human reviewers. This combination directly satisfies both the extraction and human review requirements without custom code.

Answer

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

Answer

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

Question 13

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. The Speech-to-Text API is Google Cloud's fully managed, pre-trained automatic speech recognition (ASR) service that requires no model training. The latest_long model is specifically optimized for transcribing long-form audio content such as customer support calls, providing high accuracy for clear English speech. Since the audio is clear with no background noise and the company wants a quick, training-free solution, Speech-to-Text API with latest_long is the ideal fit.

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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 14

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.

Answer

LINEAR_REG

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BOOSTED_TREE_REGRESSOR

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K_MEANS

Question 15

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. AutoML Vision for image classification is designed to train custom image classification models with minimal code using labeled images. It automatically handles data preprocessing, model selection, and hyperparameter tuning, making it ideal for classifying shipping documents from images. The other services are either for tabular data, document extraction, or pre-trained label detection without custom training.

Answer

Vertex AI AutoML Tables

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Document AI custom extractor

Answer

Cloud Vision API with label detection