PDE Preparing and Using Data for Analysis Practice Test 7 — 15 Questions

Question 1

You are migrating a large on-premises data warehouse to BigQuery. The data includes sensitive PII columns that must be masked for certain users. Which BigQuery feature can automatically redact PII in query results based on user roles?

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

Accepted Answer

Column-level security with data masking. BigQuery column-level security with data masking (also called dynamic data masking) lets you attach policy tags to specific columns and define masking rules that automatically redact values in query results based on the querying user's IAM roles. Users without the fine-grained reader role see masked values (e.g., hashed or null), while authorized users see the real data — all without changing the query. This is the native BigQuery feature designed for exactly this PII-redaction use case.

Answer

IAM conditions on tables

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Authorized views

Answer

Cloud DLP API

Question 2

A data scientist wants to use Vertex AI Workbench for exploratory data analysis. Which TWO statements are true about Vertex AI Workbench?

Accepted Answer

It supports custom container images for the notebook environment.. Option B is correct because Vertex AI Workbench instances let you specify a custom container image for the notebook environment, so you can bring your own dependencies and tooling rather than being limited to the default image. Option D is correct because Vertex AI Workbench provides a managed JupyterLab environment with pre-installed ML libraries (such as TensorFlow, PyTorch, and scikit-learn), which is exactly what a data scientist needs for exploratory data analysis. Option A is incorrect because Workbench instances are user-managed VMs that do not scale to zero; they keep running (and incurring cost) until stopped. Option C is incorrect because Workbench supports multiple frameworks and languages, not only TensorFlow. Option E is incorrect because a built-in SQL query editor for BigQuery is a feature of BigQuery and other tools, not a defining capability of Vertex AI Workbench.

Answer

It is a serverless service that scales to zero when not in use.

Answer

It can only be used with TensorFlow.

Answer

It includes a built-in SQL query editor for BigQuery.

Question 3

You are designing a data pipeline for ML training with Vertex AI. You need to split time-series data into train/validation/test sets without leaking future data. Which THREE practices should you follow?

Accepted Answer

Use a sliding window validation approach for hyperparameter tuning.. Option A is correct because a sliding-window (rolling-origin) validation scheme trains on past data and validates on the immediately following window, which respects temporal order and prevents future leakage during hyperparameter tuning. Option B is correct because keeping all data points from the same time period (e.g., same day or timestamp bucket) in a single split avoids boundary leakage, where records from one period appear in both train and test. Option E is correct because using an explicit date column to define split boundaries (for example, train on data before 2024-01-01, validate on Q1 2024, test on Q2 2024) enforces a strict chronological cutoff. Option C is not appropriate because Looker is a BI/analytics and dashboarding tool, not a mechanism for producing leakage-safe ML dataset splits. Option D is wrong because random row assignment mixes past and future observations across splits, which is exactly the temporal leakage the scenario must avoid.

Answer

Use Looker to generate the splits automatically.

Answer

Randomly assign rows to each split to ensure statistical distribution.

Question 4

You are building a forecasting model to predict daily sales for the next 90 days using historical sales data with clear seasonality and trend. You want to use BigQuery ML with minimal manual tuning. Which model type should you choose?

Accepted Answer

ARIMA_PLUS. ARIMA_PLUS is the correct choice because it is a Google-developed, automated time-series model in BigQuery ML that handles seasonality, trend, holidays, and outliers with minimal manual tuning. It automatically performs preprocessing such as missing value imputation, holiday effect detection, and seasonality decomposition, making it ideal for forecasting daily sales over a 90-day horizon. Unlike standard ARIMA, ARIMA_PLUS requires no manual specification of p, d, q parameters or seasonal orders, aligning with the 'minimal manual tuning' requirement.

Answer

ARIMA

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Boosted tree (XGBoost)

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Linear regression

Question 5

Your team uses Looker to develop a model on top of BigQuery. The data is partitioned by ingestion time, and analysts frequently query the last 7 days. However, Looker queries are scanning the entire table, causing high costs. Which change should you implement?

Accepted Answer

Apply a LookML access_filter to dynamically filter on the partition column.. The single best approach is to apply a partition filter requirement in LookML and enable partition pruning in BigQuery. Other options are not directly about Looker or are suboptimal.

Answer

Enable BI Engine on the BigQuery table to accelerate queries.

Answer

Create a materialized view that aggregates data daily and point Looker to that view.

Answer

Use clustering on the order_date column to improve query performance.

Question 6

You are building a multi-cloud analytics solution to join data from Google Cloud and AWS S3. You need to query the S3 data using BigQuery without moving it. Which Google Cloud service should you use?

Accepted Answer

BigQuery Omni. BigQuery Omni is the service that allows querying data in AWS S3 directly from BigQuery without moving it. It deploys BigQuery compute in AWS to process the data locally, returning results to Google Cloud.

Answer

Looker

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Dataproc

Answer

BigQuery Data Transfer Service

Question 7

You are using BigQuery ML to train a matrix factorization model for a recommendation system. The training data consists of user-item interactions. You notice that the model is overfitting. Which of the following hyperparameter changes would most likely reduce overfitting?

Accepted Answer

Increase w_reg (regularization weight) from 0.1 to 0.5. Increasing w_reg raises the L2 regularization penalty applied to the learned latent factor matrices, which shrinks factor magnitudes and constrains model complexity. In BigQuery ML's matrix factorization, w_reg directly controls how strongly the optimizer penalizes large weights, so moving from 0.1 to 0.5 tightens the fit and reduces overfitting on sparse user-item interaction data.

Answer

Decrease w_reg (regularization weight) from 0.1 to 0.01

Answer

Increase num_factors from 10 to 20

Answer

Increase num_training_iterations from 10 to 20

Question 8

You are building a binary classification model using AutoML Tables on Vertex AI. The dataset has a severe class imbalance (1% positive class). Which strategy should you use to handle the imbalance?

Accepted Answer

Use a weight column to assign higher weights to the minority class.. The correct option is D: use a weight column in AutoML Tables to assign higher weights to the minority class. In Vertex AI AutoML Tables, you can add a weight column to the training data and specify it as the weight column, so minority-class rows contribute more to the loss. This directly counteracts the 1% positive-class imbalance without changing the data distribution. Options A and C are not supported preprocessing steps in AutoML Tables, since it manages data splitting and training internally and does not expose SMOTE or manual downsampling. Option B is incorrect because AutoML Tables does not automatically correct severe class imbalance; you must supply weights or adjust the optimization objective. Option D is therefore the appropriate, supported mechanism for this scenario.

Answer

Oversample the minority class using SMOTE before training.

Answer

Do nothing; AutoML Tables automatically handles class imbalance.

Answer

Downsample the majority class to match the minority class size.

Question 9

You have a BigQuery table 'events' with a TIMESTAMP column 'event_time'. You need to compute, for each event, the difference in seconds from the previous event of the same user. Which window function should you use?

Accepted Answer

LAG(event_time) OVER (PARTITION BY user_id ORDER BY event_time). LAG(event_time) OVER (PARTITION BY user_id ORDER BY event_time) returns the event_time value from the previous row within the same user_id partition, ordered chronologically. Subtracting that returned timestamp from the current row's event_time (e.g., TIMESTAMP_DIFF(event_time, LAG(...), SECOND)) yields the seconds elapsed since the user's prior event. This is the canonical pattern for gap/delta calculations in BigQuery analytic functions.

Answer

FIRST_VALUE(event_time) OVER (PARTITION BY user_id ORDER BY event_time)

Answer

LEAD(event_time) OVER (PARTITION BY user_id ORDER BY event_time)

Answer

ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY event_time)

Question 10

You are using Looker to model data from BigQuery. You have a dimension that should be filtered by a user attribute (e.g., user's region). Which LookML concept allows you to apply dynamic row-level security based on user attributes?

Accepted Answer

Access filter. Access filters in LookML allow you to apply row-level security by referencing user attributes, which are values passed from the Looker user's account or via SSO. By using an access filter on a dimension, you can dynamically restrict the data a user sees based on their attribute (e.g., region), ensuring they only view rows matching their assigned region. This is the standard LookML mechanism for dynamic row-level security.

Answer

Custom field

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Derived table

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Required access grant

Question 11

A company uses BigQuery and wants to reduce query costs by using BI Engine for Looker Studio dashboards. The data is stored in a BigQuery dataset with 5 TB of frequently accessed tables. The dashboards run dozens of concurrent queries. What is the recommended approach to enable BI Engine acceleration?

Accepted Answer

Create a reservation in the Administration panel and assign it to the project.. BI Engine is a reserved capacity service that caches data in memory. You must reserve capacity (amount of memory) for a specific BigQuery region, and then BI Engine automatically accelerates queries from Looker Studio and other BI tools. It does not require you to grant specific IAM roles to users (they just need BigQuery permissions) or to create materialized views. You do not need to enable it per dataset; it works at the project/region level.

Answer

Enable BI Engine by setting the dataset option 'enable_bi_engine=TRUE' in the dataset metadata.

Answer

Grant all Looker Studio users the 'biengine.user' IAM role on the project.

Answer

Create materialized views of the tables and connect Looker Studio to the views.

Question 12

Which BigQuery SQL function can be used to get an approximate count of distinct values in a large column faster than COUNT(DISTINCT) with lower accuracy?

Accepted Answer

APPROX_COUNT_DISTINCT. APPROX_COUNT_DISTINCT is a BigQuery function that returns an approximate count of distinct values using a HyperLogLog++ algorithm. It is significantly faster and uses far fewer resources than COUNT(DISTINCT) on large datasets, at the cost of a small statistical error (typically under 1%). This matches the requirement for a faster, lower-accuracy distinct count.

Answer

APPROX_QUANTILES

Answer

COUNT(DISTINCT)

Answer

DISTINCT_COUNT

Question 13

An organization wants to integrate BigQuery Omni to query data stored in AWS S3. They have set up the necessary connections. What is the primary benefit of using BigQuery Omni over simply copying the data to BigQuery?

Accepted Answer

Ability to use BigQuery ML models on data in S3 without moving data.. BigQuery Omni allows you to query data across clouds without moving it, providing a unified analytics experience. It reduces data egress costs and avoids duplication.

Answer

Automatic encryption of data at rest in S3.

Answer

Lower latency queries due to in-memory caching.

Answer

Support for real-time streaming inserts into S3.

Question 14

A company uses Dataplex to manage data lakes on Google Cloud. They want to enforce data quality rules on a BigQuery table, such as ensuring that a 'email' column is not null and matches a regex pattern. Which Dataplex feature should they use?

Accepted Answer

Dataplex Data Quality. Dataplex Data Quality is the feature specifically designed to define and enforce data quality rules on BigQuery tables, including checks for null values and regex pattern matching. It allows you to create data quality rules that can be scheduled or run on-demand, and it provides results that can be used for monitoring and alerting. The other options are Dataplex components for metadata management, lake organization, and lineage tracking, not for enforcing data quality rules.

Answer

Dataplex Universal Catalog

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Dataplex Lake

Answer

Dataplex Data Lineage

Question 15

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