PMLE Architecting Low-Code ML Solutions Practice Test 1 — 15 Questions

Question 1

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?

Accepted Answer

ML.PREDICT. For evaluating model accuracy and comparing different models in BigQuery ML, you use ML.EVALUATE to compute metrics and ML.PREDICT to generate forecasts. ML.TRAIN is not a valid function; training is done via CREATE MODEL. ML.FEATURE_IMPORTANCE and ML.EXPLAIN_PREDICT are for model interpretability, not for evaluation or comparison.

Answer

ML.EXPLAIN_PREDICT

Answer

ML.FEATURE_IMPORTANCE

Answer

ML.TRAIN

Question 2

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?

Accepted Answer

Use BigQuery ML to create a logistic regression model (LOGISTIC_REG) on the data directly in BigQuery.. BigQuery ML allows the team to build and train a logistic regression model directly on data stored in BigQuery using SQL syntax, without moving data to a separate environment. The LOGISTIC_REG model type is specifically designed for binary classification tasks like churn prediction, and it runs entirely within BigQuery's serverless infrastructure, satisfying the team's requirement to avoid data movement.

Answer

Use the Cloud Natural Language API to analyze customer support interactions and combine results with purchase data in BigQuery.

Answer

Export the data to a CSV file and use Vertex AI AutoML Tables to train a classification model.

Answer

Create a Dataflow pipeline to stream data to Cloud SQL and use Cloud SQL's built-in ML functions.

Question 3

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?

Accepted Answer

ARIMA_PLUS. ARIMA_PLUS is the correct choice because it is specifically designed for time-series forecasting in BigQuery ML, handling seasonal patterns, trend decomposition, and automatic hyperparameter tuning. It models autoregressive (AR) and moving average (MA) components with seasonal differencing, making it ideal for historical sales data with strong seasonal cycles.

Answer

MATRIX_FACTORIZATION

Answer

BOOSTED_TREE_REGRESSOR

Answer

K_MEANS

Question 4

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?

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

Answer

Vision API

Answer

AutoML Vision for object detection

Question 5

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 managed service specifically for retail recommendation use cases. It offers pre-built models like 'recommended-for-you' and 'frequently-bought-together'. BigQuery ML would require custom model building, and AutoML Tables is for general tabular data, not specifically for recommendations.

Answer

Dataflow with TensorFlow

Answer

BigQuery ML with MATRIX_FACTORIZATION

Answer

AutoML Tables

Question 6

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 as a pre-built feature. It can analyze video content and flag inappropriate material. AutoML Video would require custom training, which is unnecessary. Vision API is for images. Natural Language API is for text.

Answer

Use AutoML Video to train a custom explicit content detection model

Answer

Use Natural Language API on video transcripts

Answer

Use Vision API to analyze each video frame

Question 7

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. Speech-to-Text can transcribe audio in real-time, and Natural Language API can analyze sentiment from the transcribed text. Text-to-Speech is for generating speech. Video Intelligence is for video content.

Answer

Text-to-Speech and Natural Language API

Answer

Speech-to-Text and Translation API

Answer

Video Intelligence API

Question 8

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. First, create the model using CREATE MODEL. Then, evaluate it using ML.EVALUATE, which uses the test split defined in the model options. ML.PREDICT is for predictions, not evaluation. ML.TRAIN is not a valid function; model training is done via CREATE MODEL.

Answer

CREATE MODEL then ML.FEATURE_IMPORTANCE

Answer

ML.TRAIN then ML.EVALUATE

Answer

CREATE MODEL then ML.PREDICT

Question 9

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) allows documents with low confidence scores to be routed to human reviewers. This is a built-in feature of Document AI. AutoML Tables is not directly related. Cloud DLP is for data loss prevention.

Answer

AutoML Tables model retraining

Answer

Cloud DLP for data inspection

Answer

Increase the number of processors

Question 10

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

Answer

Vertex AI Workbench with a custom TensorFlow model

Answer

Vision API

Question 11

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

Answer

Text-to-Speech API

Answer

Natural Language API

Question 12

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?

Accepted Answer

BigQuery ML automatically optimizes training; no additional configuration needed. BigQuery ML automatically selects the optimal training configuration. However, using OPTIONS like 'MAX_ITERATIONS' with a smaller number can reduce training time, but setting it too low may harm accuracy. Using a filtered subset for training is not standard. Using Vertex AI AutoML Tables would require data export. Early stopping is not directly configurable in BigQuery ML DNN.

Answer

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

Answer

Use Vertex AI AutoML Tables instead of BigQuery ML

Answer

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

Question 13

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?

Accepted Answer

Translation API then Natural Language API. Natural Language API can perform sentiment analysis and entity extraction. Translation API can translate non-English reviews to English before analysis. Speech-to-Text is for audio. Vision API is for images.

Answer

Speech-to-Text then Natural Language API

Answer

Natural Language API only

Answer

Vision API then Natural Language API

Question 14

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?

Accepted Answer

Enable the 'others-you-may-like' recommendation type in addition to 'frequently-bought-together'. To capture cross-session patterns, the company should ensure that user events (including session-level co-occurrence) are properly tracked and ingested. Enabling session-level events and using the 'others-you-may-like' model (which uses co-viewed behavior) can help. Setting the recommendation type to 'frequently-bought-together' does not address session-level data. Reducing event retention would harm model quality.

Answer

Decrease the event retention period to focus on recent purchases

Answer

Use AutoML Tables to build a custom recommendation model

Answer

Set the recommendation type to only 'frequently-bought-together'

Question 15

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. To improve accuracy, use human review for low-confidence documents, create a custom processor (or use a pre-built one) tailored to their invoice format, and continuously improve the model with human-labeled data. Using the form parser is a good start but not enough; they need to enable HITL and potentially train a custom processor.

Answer

Use the pre-built invoice parser without any modifications

Answer

Use AutoML Vision to classify invoice types