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Architecting Low-Code ML Solutions practice questions

Practise Google Professional Machine Learning Engineer Architecting Low-Code ML Solutions practice questions — original exam-style scenarios with answer choices, explanations, and analysis of common mistakes.

Courseiva uses original exam-style practice questions designed for learning and revision. The goal is to understand the concepts, recognise exam patterns, and improve through explanations — not memorise copied exam dumps.

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20 questionsDomain: Architecting Low-Code ML Solutions

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What the exam tests

What to know about Architecting Low-Code ML Solutions

Architecting Low-Code ML Solutions questions test whether you can apply the concept in context, not just recognise a definition.

How the topic appears in realistic exam-style scenarios.

Which detail in the question changes the correct answer.

How to eliminate plausible but wrong options.

How to connect the question back to the wider exam objective.

Watch out for

Common Architecting Low-Code ML Solutions exam traps

▸Answering from memory before reading the full scenario.
▸Missing a constraint such as cost, availability, security, scope or command context.
▸Choosing a broad answer when the question asks for the most specific fix.
▸Ignoring why the wrong options are tempting.

Practice set

Architecting Low-Code ML Solutions questions

20 questions · select your answer, then reveal the explanation

Question 1mediummulti select

Read the full Architecting Low-Code ML Solutions explanation →

A data scientist wants to use BigQuery ML for time-series forecasting. They need to evaluate model accuracy and compare different models. Which TWO BigQuery ML functions should they use?

Trap 1: ML.EXPLAIN_PREDICT

ML.EXPLAIN_PREDICT provides local explanations for predictions, not evaluation or comparison.

Trap 2: ML.FEATURE_IMPORTANCE

ML.FEATURE_IMPORTANCE is used for tree-based models to show feature importance, not for evaluation.

Trap 3: ML.TRAIN

ML.TRAIN is not a valid BigQuery ML function; model training is performed via CREATE MODEL statement.

Study all Architecting Low-Code ML Solutions common traps →

A
ML.EXPLAIN_PREDICT
Why wrong: ML.EXPLAIN_PREDICT provides local explanations for predictions, not evaluation or comparison.
B
ML.PREDICT
ML.PREDICT generates forecasts, which are necessary for comparing model outputs.
C
ML.FEATURE_IMPORTANCE
Why wrong: ML.FEATURE_IMPORTANCE is used for tree-based models to show feature importance, not for evaluation.
D
ML.EVALUATE
ML.EVALUATE directly computes evaluation metrics like MAE, MSE, etc., enabling model comparison.
E
ML.TRAIN
Why wrong: ML.TRAIN is not a valid BigQuery ML function; model training is performed via CREATE MODEL statement.

Full breakdown with real-world context →

Question 2mediummultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A retail company wants to predict customer churn using historical purchase data stored in BigQuery. The data includes customer demographics, transaction history, and support interactions. The team is comfortable writing SQL and wants to avoid moving data to a separate environment. Which approach should they take?

Trap 1: Use the Cloud Natural Language API to analyze customer support…

This does not build a predictive churn model; it only analyzes text.

Trap 2: Export the data to a CSV file and use Vertex AI AutoML Tables to…

This requires moving data out of BigQuery, which the scenario wants to avoid.

Trap 3: Create a Dataflow pipeline to stream data to Cloud SQL and use…

Cloud SQL does not have built-in ML functions like BigQuery ML.

Study all Architecting Low-Code ML Solutions common traps →

A
Use the Cloud Natural Language API to analyze customer support interactions and combine results with purchase data in BigQuery.
Why wrong: This does not build a predictive churn model; it only analyzes text.
B
Export the data to a CSV file and use Vertex AI AutoML Tables to train a classification model.
Why wrong: This requires moving data out of BigQuery, which the scenario wants to avoid.
C
Use BigQuery ML to create a logistic regression model (LOGISTIC_REG) on the data directly in BigQuery.
BigQuery ML supports logistic regression for binary classification and runs entirely in BigQuery using SQL.
D
Create a Dataflow pipeline to stream data to Cloud SQL and use Cloud SQL's built-in ML functions.
Why wrong: Cloud SQL does not have built-in ML functions like BigQuery ML.

Full breakdown with real-world context →

Question 3easymultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A data scientist needs to train a time-series forecasting model on historical sales data stored in BigQuery to predict future demand. The data has strong seasonal patterns. Which BigQuery ML model type should they use?

Trap 1: MATRIX_FACTORIZATION

Matrix factorization is for recommendation systems.

Trap 2: BOOSTED_TREE_REGRESSOR

Boosted tree regressor is for regression, not time-series forecasting.

Trap 3: K_MEANS

K_MEANS is for clustering, not forecasting.

Study all Architecting Low-Code ML Solutions common traps →

A
MATRIX_FACTORIZATION
Why wrong: Matrix factorization is for recommendation systems.
B
BOOSTED_TREE_REGRESSOR
Why wrong: Boosted tree regressor is for regression, not time-series forecasting.
C
ARIMA_PLUS
ARIMA_PLUS is the correct model for time-series forecasting in BigQuery ML.
D
K_MEANS
Why wrong: K_MEANS is for clustering, not forecasting.

Full breakdown with real-world context →

Question 4mediummultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

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?

Trap 1: Natural Language API for entity extraction

Natural Language API analyzes text, not PDF forms.

Trap 2: Vision API

Vision API is for image classification, OCR, and object detection, not structured form extraction.

Trap 3: AutoML Vision for object detection

AutoML Vision would require custom training and is not suited for form extraction.

Study all Architecting Low-Code ML Solutions common traps →

A
Document AI with a form parser processor
Document AI's form parser is designed to extract key-value pairs and tables from forms.
B
Natural Language API for entity extraction
Why wrong: Natural Language API analyzes text, not PDF forms.
C
Vision API
Why wrong: Vision API is for image classification, OCR, and object detection, not structured form extraction.
D
AutoML Vision for object detection
Why wrong: AutoML Vision would require custom training and is not suited for form extraction.

Full breakdown with real-world context →

Question 5mediummultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

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?

Trap 1: Dataflow with TensorFlow

This would require custom development and is not a managed recommendation service.

Trap 2: BigQuery ML with MATRIX_FACTORIZATION

Matrix factorization can build recommendations, but it requires more custom work compared to Recommendations AI.

Trap 3: AutoML Tables

AutoML Tables is for classification/regression on tabular data, not specifically for recommendations.

Study all Architecting Low-Code ML Solutions common traps →

A
Dataflow with TensorFlow
Why wrong: This would require custom development and is not a managed recommendation service.
B
BigQuery ML with MATRIX_FACTORIZATION
Why wrong: Matrix factorization can build recommendations, but it requires more custom work compared to Recommendations AI.
C
AutoML Tables
Why wrong: AutoML Tables is for classification/regression on tabular data, not specifically for recommendations.
D
Recommendations AI
Recommendations AI provides pre-built models for personalized recommendations, ideal for e-commerce.

Full breakdown with real-world context →

Question 6hardmultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

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?

Trap 1: Use AutoML Video to train a custom explicit content detection model

AutoML Video is incorrect because it necessitates training a custom model with a vast, labelled dataset of explicit content, which the scenario does not imply is available or desired for this generic task. The company requires an immediate, pre-trained solution for common explicit content detection. This option is tempting as AutoML simplifies custom model creation, making it suitable when detecting highly niche or proprietary content for which no off-the-shelf solution exists and a specific training dataset is readily available.

Trap 2: Use Natural Language API on video transcripts

Natural Language API only analyzes text, not visual content.

Trap 3: Use Vision API to analyze each video frame

Vision API is for images and does not scale well to video; it lacks temporal analysis.

Study all Architecting Low-Code ML Solutions common traps →

A
Use Video Intelligence API with explicit content detection
Video Intelligence API has built-in explicit content detection, suitable for this use case.
B
Use AutoML Video to train a custom explicit content detection model
Why wrong: AutoML Video is incorrect because it necessitates training a custom model with a vast, labelled dataset of explicit content, which the scenario does not imply is available or desired for this generic task. The company requires an immediate, pre-trained solution for common explicit content detection. This option is tempting as AutoML simplifies custom model creation, making it suitable when detecting highly niche or proprietary content for which no off-the-shelf solution exists and a specific training dataset is readily available.
C
Use Natural Language API on video transcripts
Why wrong: Natural Language API only analyzes text, not visual content.
D
Use Vision API to analyze each video frame
Why wrong: Vision API is for images and does not scale well to video; it lacks temporal analysis.

Full breakdown with real-world context →

Question 7easymultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

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?

Trap 1: Text-to-Speech and Natural Language API

Text-to-Speech synthesizes speech, it does not transcribe.

Trap 2: Speech-to-Text and Translation API

Translation API is for translating text, not sentiment analysis.

Trap 3: Video Intelligence API

Video Intelligence API does not provide real-time transcription for audio only.

Study all Architecting Low-Code ML Solutions common traps →

A
Text-to-Speech and Natural Language API
Why wrong: Text-to-Speech synthesizes speech, it does not transcribe.
B
Speech-to-Text and Translation API
Why wrong: Translation API is for translating text, not sentiment analysis.
C
Video Intelligence API
Why wrong: Video Intelligence API does not provide real-time transcription for audio only.
D
Speech-to-Text and Natural Language API
Speech-to-Text transcribes audio, then Natural Language API performs sentiment analysis on the text.

Full breakdown with real-world context →

Question 8mediummultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

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?

Trap 1: CREATE MODEL then ML.FEATURE_IMPORTANCE

ML.FEATURE_IMPORTANCE shows feature importance, not evaluation metrics.

Trap 2: ML.TRAIN then ML.EVALUATE

ML.TRAIN is not a valid function; training is done via CREATE MODEL.

Trap 3: CREATE MODEL then ML.PREDICT

ML.PREDICT generates predictions, not evaluation metrics.

Study all Architecting Low-Code ML Solutions common traps →

A
CREATE MODEL then ML.FEATURE_IMPORTANCE
Why wrong: ML.FEATURE_IMPORTANCE shows feature importance, not evaluation metrics.
B
ML.TRAIN then ML.EVALUATE
Why wrong: ML.TRAIN is not a valid function; training is done via CREATE MODEL.
C
CREATE MODEL then ML.PREDICT
Why wrong: ML.PREDICT generates predictions, not evaluation metrics.
D
CREATE MODEL then ML.EVALUATE
CREATE MODEL trains the model, and ML.EVALUATE returns evaluation metrics on the test set.

Full breakdown with real-world context →

Question 9hardmultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

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?

Trap 1: AutoML Tables model retraining

AutoML Tables is a separate service; Document AI has its own HITL feature.

Trap 2: Cloud DLP for data inspection

Cloud DLP is for sensitive data detection, not human review of documents.

Trap 3: Increase the number of processors

Adding processors does not enable human review.

Study all Architecting Low-Code ML Solutions common traps →

A
AutoML Tables model retraining
Why wrong: AutoML Tables is a separate service; Document AI has its own HITL feature.
B
Cloud DLP for data inspection
Why wrong: Cloud DLP is for sensitive data detection, not human review of documents.
C
Increase the number of processors
Why wrong: Adding processors does not enable human review.
D
Human-in-the-Loop (HITL)
HITL enables human review for low-confidence documents, ensuring accuracy.

Full breakdown with real-world context →

Question 10mediummultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

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?

Trap 1: Cloud TPU

Cloud TPU is a hardware accelerator, not a managed ML service.

Trap 2: Vertex AI Workbench with a custom TensorFlow model

This requires coding and ML expertise, which the team lacks.

Trap 3: Vision API

Vision API provides pre-built models, not custom training.

Study all Architecting Low-Code ML Solutions common traps →

A
Cloud TPU
Why wrong: Cloud TPU is a hardware accelerator, not a managed ML service.
B
AutoML Vision
AutoML Vision allows training custom image classification models with a simple UI, no coding required.
C
Vertex AI Workbench with a custom TensorFlow model
Why wrong: This requires coding and ML expertise, which the team lacks.
D
Vision API
Why wrong: Vision API provides pre-built models, not custom training.

Full breakdown with real-world context →

Question 11easymultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

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?

Trap 1: Vision API

Vision API processes images, not text translation.

Trap 2: Text-to-Speech API

Text-to-Speech converts text to speech, not translation.

Trap 3: Natural Language API

Natural Language API performs analysis like sentiment, not translation.

Study all Architecting Low-Code ML Solutions common traps →

A
Translation API
Translation API is designed for text translation across languages.
B
Vision API
Why wrong: Vision API processes images, not text translation.
C
Text-to-Speech API
Why wrong: Text-to-Speech converts text to speech, not translation.
D
Natural Language API
Why wrong: Natural Language API performs analysis like sentiment, not translation.

Full breakdown with real-world context →

Question 12hardmultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A company wants to use BigQuery ML to train a DNN_CLASSIFIER model on a dataset with 100 million rows. They are concerned about training time and cost. Which approach can help optimize training performance while staying within BigQuery ML?

Trap 1: Use OPTIONS('MAX_ITERATIONS' = 10) to limit training iterations

While this can reduce time, it may result in underfitting; not an optimization recommendation.

Trap 2: Use Vertex AI AutoML Tables instead of BigQuery ML

This introduces additional complexity and data export; the question asks to stay within BigQuery ML.

Trap 3: Train on a 10% random sample of the data to reduce cost

Sampling reduces representativeness and may not be acceptable for production models.

Study all Architecting Low-Code ML Solutions common traps →

A
Use OPTIONS('MAX_ITERATIONS' = 10) to limit training iterations
Why wrong: While this can reduce time, it may result in underfitting; not an optimization recommendation.
B
Use Vertex AI AutoML Tables instead of BigQuery ML
Why wrong: This introduces additional complexity and data export; the question asks to stay within BigQuery ML.
C
Train on a 10% random sample of the data to reduce cost
Why wrong: Sampling reduces representativeness and may not be acceptable for production models.
D
BigQuery ML automatically optimizes training; no additional configuration needed
BigQuery ML handles optimization internally, adjusting training parameters for efficiency.

Full breakdown with real-world context →

Question 13mediummultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

A company needs to analyze customer feedback from app reviews to identify common themes and sentiment. They have millions of reviews in multiple languages. Which combination of pre-built APIs should they use?

Trap 1: Speech-to-Text then Natural Language API

App reviews are text, not audio.

Trap 2: Natural Language API only

Natural Language API supports multiple languages for sentiment analysis, but for best accuracy, translation may be needed if reviews are in many languages.

Trap 3: Vision API then Natural Language API

Vision API is for images; app reviews are text.

Study all Architecting Low-Code ML Solutions common traps →

A
Speech-to-Text then Natural Language API
Why wrong: App reviews are text, not audio.
B
Natural Language API only
Why wrong: Natural Language API supports multiple languages for sentiment analysis, but for best accuracy, translation may be needed if reviews are in many languages.
C
Translation API then Natural Language API
Translate reviews to a common language, then analyze sentiment and entities.
D
Vision API then Natural Language API
Why wrong: Vision API is for images; app reviews are text.

Full breakdown with real-world context →

Question 14mediummulti select

Read the full Architecting Low-Code ML Solutions explanation →

A retail company uses Recommendations AI to power personalized product recommendations on their website. They notice that the 'frequently-bought-together' model is not capturing complementary items that are often purchased in the same session but not necessarily in the same transaction. Which TWO actions should they take to improve the model?

Trap 1: Decrease the event retention period to focus on recent purchases

This would reduce data and likely harm model quality.

Trap 2: Use AutoML Tables to build a custom recommendation model

AutoML Tables is not designed for recommendation systems; it would require significant custom effort.

Trap 3: Set the recommendation type to only 'frequently-bought-together'

This limits to transaction-level co-purchase, not session-level.

Study all Architecting Low-Code ML Solutions common traps →

A
Decrease the event retention period to focus on recent purchases
Why wrong: This would reduce data and likely harm model quality.
B
Enable the 'others-you-may-like' recommendation type in addition to 'frequently-bought-together'
'Others-you-may-like' uses co-viewed and co-purchased signals, capturing session-level patterns.
C
Use AutoML Tables to build a custom recommendation model
Why wrong: AutoML Tables is not designed for recommendation systems; it would require significant custom effort.
D
Set the recommendation type to only 'frequently-bought-together'
Why wrong: This limits to transaction-level co-purchase, not session-level.
E
Ingest session-level event data (e.g., product views in the same session) into Recommendations AI
Session-level events capture co-viewed items across sessions, improving recommendations.

Full breakdown with real-world context →

Question 15hardmulti select

Read the full Architecting Low-Code ML Solutions explanation →

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?

Trap 1: Use the pre-built invoice parser without any modifications

While a good start, it may not handle edge cases; additional steps are needed.

Trap 2: Use AutoML Vision to classify invoice types

AutoML Vision is for image classification, not document extraction; Document AI is the correct service.

Study all Architecting Low-Code ML Solutions common traps →

A
Regularly retrain the processor using human-verified data
Continuous improvement with human-labeled data helps the model learn from edge cases.
B
Use the pre-built invoice parser without any modifications
Why wrong: While a good start, it may not handle edge cases; additional steps are needed.
C
Use AutoML Vision to classify invoice types
Why wrong: AutoML Vision is for image classification, not document extraction; Document AI is the correct service.
D
Enable Human-in-the-Loop (HITL) to review documents with low confidence scores
HITL routes low-confidence documents for human review, improving accuracy.
E
Use a custom processor trained on their specific invoice format
A custom processor can be trained on their invoices to improve extraction accuracy.

Full breakdown with real-world context →

Question 16easymulti select

Read the full Architecting Low-Code ML Solutions explanation →

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?

Trap 1: The analyst must call ML.TRAIN after CREATE MODEL to start training

ML.TRAIN is not a valid function; training happens in CREATE MODEL.

Trap 2: The trained model is stored in Cloud Storage

The model is stored in BigQuery, not Cloud Storage.

Trap 3: The model must be exported to Vertex AI for prediction

Predictions can be done within BigQuery using ML.PREDICT.

Study all Architecting Low-Code ML Solutions common traps →

A
The analyst must call ML.TRAIN after CREATE MODEL to start training
Why wrong: ML.TRAIN is not a valid function; training happens in CREATE MODEL.
B
The trained model is stored in Cloud Storage
Why wrong: The model is stored in BigQuery, not Cloud Storage.
C
The model must be exported to Vertex AI for prediction
Why wrong: Predictions can be done within BigQuery using ML.PREDICT.
D
The model is automatically evaluated on a held-out test set if data splitting is enabled
By default, BigQuery ML splits data into training and evaluation sets.
E
Training is performed using the CREATE MODEL statement
BigQuery ML uses CREATE MODEL to train models.

Full breakdown with real-world context →

Question 17easymultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

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?

Question 18mediummultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

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?

Question 19hardmultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

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?

Question 20easymultiple choice

Read the full Architecting Low-Code ML Solutions explanation →

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?

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What does the PMLE exam test about Architecting Low-Code ML Solutions?: Architecting Low-Code ML Solutions questions test whether you can apply the concept in context, not just recognise a definition.
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All PMLE questions Architecting Low-Code ML Solutions domain overview PMLE exam guide

Exam traps to avoid

▸Answering from memory before reading the full scenario.
▸Missing a constraint such as cost, availability, security, scope or command context.
▸Choosing a broad answer when the question asks for the most specific fix.
▸Ignoring why the wrong options are tempting.

Architecting Low-Code ML Solutions practice questions

What to know about Architecting Low-Code ML Solutions

Common Architecting Low-Code ML Solutions exam traps

Architecting Low-Code ML Solutions questions

A data scientist wants to use BigQuery ML for time-series forecasting. They need to evaluate model accuracy and compare different models. Which TWO BigQuery ML functions should they use?

A data scientist needs to train a time-series forecasting model on historical sales data stored in BigQuery to predict future demand. The data has strong seasonal patterns. Which BigQuery ML model type should they use?

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?

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?

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?

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?

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?

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?

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?

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?

A company wants to use BigQuery ML to train a DNN_CLASSIFIER model on a dataset with 100 million rows. They are concerned about training time and cost. Which approach can help optimize training performance while staying within BigQuery ML?

A company needs to analyze customer feedback from app reviews to identify common themes and sentiment. They have millions of reviews in multiple languages. Which combination of pre-built APIs should they use?

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?

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?

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?

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?

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?

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?

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