PDE Practice Test 12 — 15 Questions

Question 1

You are designing a Cloud Spanner schema for a global e-commerce application. The database will include a Customers table and an Orders table. To optimise performance for queries that join Customers with their Orders, which THREE design choices are recommended? (Choose 3.)

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

Accepted Answer

Use interleaved tables: make Orders an interleaved table under Customers.. Spanner interleaved tables store child rows physically close to parent rows, improving join performance. Including the parent's primary key as the first part of the child's primary key is required for interleaving. Secondary indexes on non-key columns are also needed for efficient lookups.

Answer

Use a single table for both Customers and Orders and filter by CustomerId.

Answer

Denormalise the schema by embedding order details into the Customers table as repeated fields.

Question 2

A company uses Pub/Sub with push subscriptions to deliver events to a Cloud Run service. Recently, the service has been returning HTTP 429 (Too Many Requests), causing messages to be retried and eventually sent to the dead letter topic. What is the MOST likely cause?

Accepted Answer

The Cloud Run service needs more instances to handle the incoming request rate. Push subscriptions can be rate limited by the receiving service. Increasing ackDeadline gives more time but doesn't reduce rate. Using pull subscriptions shifts the rate control to the subscriber. Adjusting max delivery attempts only affects how many retries before dead letter, not rate limiting.

Answer

The subscription ackDeadline is set too low, causing messages to be redelivered

Answer

The push endpoint is not acknowledging messages quickly enough, causing a backlog

Answer

The dead letter topic is misconfigured, causing messages to be sent to it prematurely

Question 3

A company needs to predict whether a product image contains a specific defect. They have 10,000 labeled images and want to build a model quickly without writing custom code or training from scratch. Which GCP service should they use?

Accepted Answer

AutoML Vision. AutoML Vision is designed for custom image classification tasks with minimal ML expertise. It uses transfer learning and supports up to millions of images. AutoML Tables handles tabular data, not images. Vertex AI custom training would require more effort. AutoML NLP is for text data.

Answer

AutoML Tables

Answer

Vertex AI custom training

Answer

AutoML Natural Language

Question 4

A company uses BigQuery ML to create a classification model. The model is used for batch prediction on a weekly basis. After six months, the data distribution shifts, and model accuracy drops. Which approach should the company take to maintain model performance?

Accepted Answer

Schedule automatic retraining of the model using the most recent three months of data.. The model's accuracy drop is due to data distribution shift (concept drift). Scheduling automatic retraining using the most recent three months of data ensures the model adapts to the new patterns without manual intervention. BigQuery ML supports scheduled queries and automatic model retraining via the `CREATE OR REPLACE MODEL` statement, making this approach both practical and aligned with MLOps best practices for batch prediction pipelines.

Answer

Use Cloud Dataflow to preprocess the data and then update the model with new features.

Answer

Perform hyperparameter tuning on the original training data.

Answer

Apply model quantization to reduce model size and improve inference speed.

Question 5

A data engineer needs to query data from BigQuery and another cloud provider's storage (AWS S3) using a single SQL query. The data must not be moved or copied to GCP. Which Google Cloud service should they use?

Accepted Answer

BigQuery Omni. BigQuery Omni allows querying data across multiple clouds (AWS S3, Azure Blob Storage) using BigQuery's interface without moving data. BigQuery Omni runs compute in the other cloud's region. BigQuery Transfer Service moves data into BigQuery. Dataplex is for data management, not cross-cloud queries. Cloud Storage Transfer Service is for moving data between clouds.

Answer

Cloud Storage Transfer Service

Answer

Dataplex

Answer

BigQuery Data Transfer Service

Question 6

You are building a machine learning pipeline for credit risk assessment. The dataset has a severe class imbalance (1% default rate). You want to use AutoML Tables on Vertex AI. Which strategy should you incorporate to handle imbalance?

Accepted Answer

Use the `class_weight` parameter in the AutoML Tables model. AutoML Tables automatically applies class imbalance handling (e.g., class weighting) by default. You can adjust the weight strategy. SMOTE is not directly supported in AutoML Tables; you would need custom training. Downsampling and upsampling are manual steps not needed.

Answer

Downsample the majority class to a 50-50 ratio

Answer

Apply SMOTE in a Dataflow pipeline before training

Answer

Upsample the minority class using BigQuery SQL

Question 7

A data team uses Looker Studio to create a report that combines data from two different BigQuery tables: one with sales transactions and another with customer demographics. They need to join these tables in the report without writing SQL. Which feature should they use?

Accepted Answer

Data blending. Looker Studio's data blending feature allows combining data from multiple sources (including BigQuery tables) using a common key, without writing SQL. It provides a graphical interface to define joins. Creating a custom query requires SQL. Looker Studio reports support multiple charts, but blending is the specific feature for joining data. Calculated fields transform data within a single source.

Answer

Creating a report with multiple charts

Answer

Custom query in BigQuery connector

Answer

Calculated fields

Question 8

A data scientist is using AutoML Tables to build a classification model for predicting customer churn. The dataset is highly imbalanced (only 1% churn). Which strategy should they use to handle the class imbalance within AutoML Tables?

Accepted Answer

No action needed; AutoML Tables automatically handles class imbalance by adjusting class weights.. AutoML Tables automatically computes class weights and applies them during training to handle imbalanced data. You do not need to manually apply SMOTE or change the training budget; AutoML Tables handles it out of the box.

Answer

Manually apply SMOTE to the training data before uploading to AutoML Tables.

Answer

Enable the 'enable_class_imbalance_handling' flag during training.

Answer

Set the 'class_weight' parameter in the AutoML Tables training configuration to 'balanced'.

Question 9

A data engineer needs to train a linear regression model in BigQuery ML using a table with 10 million rows. The model will predict sales based on features like advertising spend, seasonality, and store location. Which SQL statement should they use to create and train the model?

Accepted Answer

CREATE MODEL mymodel OPTIONS(model_type='LINEAR_REG') AS SELECT * FROM sales_data. In BigQuery ML, the CREATE MODEL statement with option MODEL_TYPE='LINEAR_REG' creates a linear regression model. The training data is specified in the AS SELECT clause.

Answer

CREATE MODEL mymodel AS SELECT * FROM sales_data WITH LINEAR REGRESSION

Answer

CREATE OR REPLACE MODEL mymodel OPTIONS(model_type='LINEAR_REGRESSION') AS SELECT * FROM sales_data

Answer

CREATE MODEL mymodel OPTIONS(model_type='linear_reg') AS SELECT * FROM sales_data

Question 10

A data analyst wants to rank products by sales within each category. They need to assign a unique rank to each product, with no gaps in the ranking numbers (i.e., ties should have different ranks). Which window function should they use?

Accepted Answer

ROW_NUMBER(). ROW_NUMBER() assigns a unique sequential integer to each row within a partition, starting at 1, regardless of ties. RANK() would give the same rank to ties and skip numbers, so it would introduce gaps.

Answer

NTILE()

Answer

RANK()

Answer

DENSE_RANK()

Question 11

A machine learning engineer needs to deploy a custom TensorFlow model for online predictions with low latency. The model is already trained and saved in SavedModel format. Which Vertex AI service should they use?

Accepted Answer

Vertex AI Prediction. Vertex AI Prediction allows you to deploy custom models (including TensorFlow SavedModel) to an endpoint for online predictions. It supports autoscaling and low-latency serving.

Answer

Vertex AI Workbench

Answer

Vertex AI Feature Store

Answer

Vertex AI AutoML

Question 12

You need to create a time-series forecast for inventory demand using BigQuery ML. The data includes daily sales for 5 years. Which model type should you use?

Accepted Answer

ARIMA_PLUS. BigQuery ML supports ARIMA_PLUS for time-series forecasting. Linear regression, k-means, and matrix factorization are not appropriate for time-series forecasting.

Answer

K-means

Answer

Linear regression

Answer

Matrix factorization

Question 13

A data engineer needs to build a LookML model in Looker to define business logic and relationships for a new dataset. They want to create an 'explore' that joins an 'orders' view with a 'customers' view. Where should they define this join?

Accepted Answer

In the model file, within the 'explore' definition, add a 'join' parameter.. In LookML, the explore file (or the explore definition within a model file) is where you define which views to include and how they join together. The view files (*.view.lkml) define the dimension and measure logic for a single table or derived table. The model file (*.model.lkml) ties everything together and defines explores.

Answer

In the Looker admin panel, under 'Joins', create a new join.

Answer

In the 'orders.view.lkml' file, add a 'join' parameter.

Answer

In the 'customers.view.lkml' file, add a 'join' parameter.

Question 14

You need to implement data quality rules on a Dataplex lake to ensure that critical columns are not null and meet certain constraints. Which two Dataplex features can you use? (Choose TWO)

Accepted Answer

Auto Data Quality. Dataplex Data Quality Tasks allow you to define and run data quality checks. Dataplex Auto Data Quality automates profiling and monitoring. The other options are not specific to quality: Catalog is for discovery, Scans for security, and Tag Templates for metadata.

Answer

Tag Templates

Answer

Data Catalog

Answer

Data Scan

Question 15

You need to preprocess tabular data for training a classification model using Vertex AI. The dataset has missing values in numerical columns and categorical columns with high cardinality. Which Vertex AI service provides automated feature engineering and preprocessing as part of the pipeline?

Accepted Answer

Vertex AI Pipelines. Vertex AI Pipelines allows you to build ML pipelines with components for feature engineering, including handling missing values and encoding. Vertex AI Feature Store is for serving features, not preprocessing.

Answer

AutoML Tables

Answer

Vertex AI Feature Store

Answer

Vertex AI Workbench