How should I use these Preparing and Using Data for Analysis practice questions?

Read each scenario carefully and choose your answer before revealing the explanation. Then check why your choice was right or wrong. Repeat until the reasoning feels automatic.

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Preparing and Using Data for Analysis practice questions

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20 questionsDomain: Preparing and Using Data for Analysis

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What the exam tests

What to know about Preparing and Using Data for Analysis

Preparing and Using Data for Analysis 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 Preparing and Using Data for Analysis 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

Preparing and Using Data for Analysis questions

20 questions · select your answer, then reveal the explanation

Question 1easymultiple choice

Read the full Preparing and Using Data for Analysis explanation →

To enable data lineage tracking in BigQuery, which feature should be activated?

Trap 1: BigQuery Audit Logs

Incorrect. BigQuery Audit Logs capture metadata changes but are not designed for lineage tracking.

Trap 2: Data Catalog

Incorrect. Data Catalog is for metadata management and discovery, not for lineage tracking.

Trap 3: BigQuery Lineage API

Incorrect. There is no standalone BigQuery Lineage API; lineage is provided through Dataplex.

Study all Preparing and Using Data for Analysis common traps →

A
BigQuery Audit Logs
Why wrong: Incorrect. BigQuery Audit Logs capture metadata changes but are not designed for lineage tracking.
B
Data Catalog
Why wrong: Incorrect. Data Catalog is for metadata management and discovery, not for lineage tracking.
C
Dataplex Lineage
Correct. Dataplex Lineage provides comprehensive data lineage for BigQuery and other Google Cloud data services.
D
BigQuery Lineage API
Why wrong: Incorrect. There is no standalone BigQuery Lineage API; lineage is provided through Dataplex.

Preparing and Using Data for Analysis practice questions

What to know about Preparing and Using Data for Analysis

Common Preparing and Using Data for Analysis exam traps

Preparing and Using Data for Analysis questions

To enable data lineage tracking in BigQuery, which feature should be activated?

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?

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?

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?

A data engineer is building a Looker Studio dashboard that requires a calculated field to compute the running total of sales per day per store. Which Looker Studio function should they use?

A data engineer wants to train a linear regression model in BigQuery ML to predict sales. The training data includes a categorical feature with 1000+ unique values. Which method is most appropriate to handle this feature in the CREATE MODEL statement?

You need to create a Looker model that defines a 'sales' view based on a BigQuery table, with a measure for total revenue. Which LookML object defines the table and dimensions?

A company uses Looker Studio to build dashboards from BigQuery data. They notice that queries take several seconds to return. They want to improve performance without changing the schema or adding materialized views. Which option should they use?

A data scientist is training a binary classification model on an imbalanced dataset (95% negative, 5% positive) using AutoML Tables. Which strategy should they use to handle the class imbalance?

You need to split a time-series dataset into training and evaluation sets for a forecasting model. The data is ordered by timestamp. Which splitting technique should you use?

Which BigQuery SQL function returns the rank of a row within a window, with gaps in the ranking for ties?

A company uses Dataplex to manage data quality across multiple BigQuery datasets. They want to define a data quality rule that checks if a column 'email' contains a valid email format. Which Dataplex feature should they use?

A data engineer needs to query data across BigQuery (in Google Cloud) and Snowflake (in AWS) without moving the data. Which service should they use?

You want to train a custom TensorFlow model on Vertex AI using a managed Jupyter notebook environment. Which service should you use?

A company uses Looker to define business logic in LookML. They need to create a new measure that calculates the average order value, defined as total revenue divided by number of orders. Which LookML syntax should they use?

A data scientist wants to import a pre-trained TensorFlow model into BigQuery ML for batch predictions. The model is stored in a Cloud Storage bucket. Which statement is correct?

You need to track the lineage of data in BigQuery, showing how tables are derived from other tables via queries. Which service provides this capability?

A data engineer needs to build a feature engineering pipeline using Vertex AI Pipelines. The pipeline should preprocess data, train a model, and deploy it. Which two components are required to define the pipeline? (Choose 2)

A company uses AutoML Tables to train a classification model. They want to improve model performance by engineering new features from existing timestamp columns. Which three techniques can they apply within AutoML Tables? (Choose 3)

A data team wants to use Approximate Aggregation Functions in BigQuery to get faster query results. Which two functions can they use? (Choose 2)

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