PMLE Practice Test 18 — 15 Questions

Question 1

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?

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

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 2

A company needs to forecast product demand for the next 12 months using historical sales data. They want to use BigQuery ML with minimal coding. Which model type is most suitable?

Accepted Answer

ARIMA_PLUS. ARIMA_PLUS is designed for time-series forecasting and automatically handles seasonality, trends, and holidays. K_MEANS is for clustering, MATRIX_FACTORIZATION for recommendations, and LINEAR_REG for simple regression.

Answer

K_MEANS

Answer

MATRIX_FACTORIZATION

Answer

LINEAR_REG

Question 3

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 4

A company uses BigQuery ML to train a boosted tree classifier on a large dataset. After training, they want to understand which features most influence predictions. Which BigQuery ML function should they use?

Accepted Answer

ML.FEATURE_IMPORTANCE. ML.FEATURE_IMPORTANCE returns feature importance for tree-based models in BigQuery ML. ML.EVALUATE gives evaluation metrics, ML.PREDICT gives predictions, ML.EXPLAIN_PREDICT gives local explanations with SHAP values.

Answer

ML.EXPLAIN_PREDICT

Answer

ML.EVALUATE

Answer

ML.PREDICT

Question 5

A company is building a document processing pipeline for invoices. They need to extract key fields (invoice number, date, total amount) and allow human review for invoices over $10,000. Which TWO Google Cloud services/features should they combine?

Accepted Answer

Human-in-the-Loop (HITL) on Document AI. Document AI with a specialized invoice parser can extract fields. Human-in-the-Loop (HITL) is a feature of Document AI that enables human review. Cloud Vision API is not specialized for invoices. AutoML Tables is for tabular models. Cloud Translation is not needed.

Answer

Cloud Vision API for OCR

Answer

AutoML Tables to predict missing fields

Answer

Cloud Translation API to translate invoices

Question 6

A data scientist wants to use AutoML to classify images of retail products into categories. There are 50 categories and the dataset has 100,000 labelled images. Which Vertex AI AutoML service is most appropriate?

Accepted Answer

AutoML Vision. AutoML Vision is designed for image classification, object detection, and segmentation. AutoML Tables is for tabular data, AutoML NLP for text, and AutoML Video for video.

Answer

AutoML Tables

Answer

AutoML Video

Answer

AutoML NLP

Question 7

A team is building ML pipelines with Vertex AI. They want to reuse standard pipeline components across teams and enforce governance. What approach should they take?

Accepted Answer

Use Vertex AI Pipelines with pre-built and custom components organized in a component registry.. Standardized pipeline templates (components) in Vertex AI Pipelines allow reuse and governance across teams.

Answer

Store pipeline definitions in a shared Cloud Storage bucket and copy them manually.

Answer

Use Cloud Composer to orchestrate ad-hoc scripts.

Answer

Have each team build their own pipelines independently.

Question 8

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 offers pre-built retail models including frequently-bought-together. AutoML Tables would require custom training, BigQuery ML is for SQL-based models, and Vertex AI Prediction is for deploying custom models.

Answer

BigQuery ML

Answer

Vertex AI Prediction

Answer

AutoML Tables

Question 9

A team wants to track the lineage of ML pipeline runs, including which datasets, parameters, and models were used in each execution. Which Vertex AI service should they use?

Accepted Answer

Vertex AI Metadata. Vertex AI Metadata (part of Vertex ML Metadata) tracks artifacts, executions, and lineage.

Answer

Vertex AI Feature Store

Answer

Vertex AI Model Registry

Answer

Vertex AI Experiments

Question 10

A company uses Vertex AI Model Registry to manage multiple model versions. They want to designate a model version as 'champion' for production deployment and another as 'challenger' for A/B testing. Which feature of the registry should they use?

Accepted Answer

Model aliases. Model Registry aliases allow tagging model versions with labels like 'champion' or 'challenger', enabling easy routing and comparison.

Answer

Model version labels

Answer

Model lineage

Answer

Model evaluation metrics

Question 11

A data scientist wants to use AutoML Tables to build a binary classification model for loan default prediction. The dataset has 200 features and 1 million rows, with highly imbalanced classes. Which TWO options should they consider? (Choose 2)

Accepted Answer

Set the target column to 'default'. For binary classification, the target column must be set. For imbalanced classes, 'maximize AUC' is recommended. AutoML Tables automatically handles feature engineering and hyperparameter tuning. Manual split is optional.

Answer

Enable automatic feature engineering

Answer

Disable hyperparameter tuning to save cost

Answer

Use Manual data split

Question 12

An ML team wants to automatically track training runs, including hyperparameters and metrics, with minimal code changes. Which Vertex AI service should they use?

Accepted Answer

Vertex AI Experiments with autologging. Vertex AI Experiments with autologging captures parameters and metrics automatically when using the Vertex AI SDK or MLflow.

Answer

Vertex AI Prediction

Answer

Vertex AI Workbench

Answer

Vertex AI Metadata

Question 13

A company wants to implement a centralized model registry for governance. Which two features should they use? (Choose two.)

Accepted Answer

Vertex AI Model Registry

Answer

Vertex AI Feature Store

Answer

Vertex AI Experiments

Answer

Vertex AI Metadata

Question 14

A team wants to use Vertex AI Workbench for collaborative notebook development. They need a persistent environment that can be stopped and restarted without losing installed packages and data. Which instance type should they choose?

Accepted Answer

User-managed notebooks. User-managed notebooks are persistent and retain customizations even when stopped, while managed notebooks are not persistent and lose customizations after stop.

Answer

Managed notebooks

Answer

Colab Enterprise notebooks

Answer

Vertex AI Pipelines

Question 15

A data scientist is using Vertex AI Workbench notebooks and wants to collaborate with team members in real-time on the same notebook. Which notebook type supports real-time collaboration?

Accepted Answer

Managed notebooks. Managed notebooks in Vertex AI Workbench support real-time collaboration similar to Google Docs, while user-managed instances do not.

Answer

JupyterLab on Compute Engine

Answer

Deep Learning VMs

Answer

User-managed notebooks