20+ practice questions focused on Preparing and Using Data for Analysis — one of the most tested topics on the Google Professional Data Engineer exam. Each question includes a detailed explanation so you learn why the right answer is correct.
Start Preparing and Using Data for Analysis PracticeTo enable data lineage tracking in BigQuery, which feature should be activated?
Explanation: The correct feature for data lineage tracking in BigQuery is Dataplex Lineage. Dataplex provides end-to-end lineage tracking across data assets, including BigQuery tables and views. BigQuery Lineage API is not a distinct feature; lineage capabilities are integrated into Dataplex. BigQuery Audit Logs capture metadata changes but are not lineage-specific, and Data Catalog is for metadata management, not lineage.
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?
Explanation: Time-series data requires temporal splitting: use a date column to define split boundaries (E), ensure all data points for a given time period are in the same split (B), and use a sliding window validation approach for hyperparameter tuning (A). Random assignment (D) would leak future data, and Looker (C) is not a data splitting tool.
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?
Explanation: 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.
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?
Explanation: The correct approach is to use the class_weight parameter available in AutoML Tables. This parameter allows you to assign higher weight to the minority class during training, directly addressing the imbalance. AutoML Tables does not automatically apply class weighting without configuration; you need to set the class_weight parameter. Oversampling (SMOTE) or downsampling are not recommended as they can distort the data distribution and may not integrate well with AutoML's training pipeline. Option B is incorrect because while AutoML has built-in capabilities, explicitly setting class_weight yields better results.
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?
Explanation: Looker Studio's RUNNING_SUM function computes a running total within a group, exactly what is needed for a running total per store partitioned by date.
+15 more Preparing and Using Data for Analysis questions available
Practice all Preparing and Using Data for Analysis questions1. Baseline your knowledge
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2. Review every explanation
For each question — right or wrong — read the full explanation. Understanding why an answer is correct is more valuable than knowing the answer itself.
3. Focus on exam traps
Preparing and Using Data for Analysis questions on the PDE frequently use trap wording. Look for subtle differences in answers that test your precision, not just general knowledge.
4. Reach 80% consistently
Do repeated sessions until you score 80%+ three times in a row. Then move to mixed-mode practice to test cross-topic recall under realistic conditions.
The exact number varies per candidate. Preparing and Using Data for Analysis is tested as part of the Google Professional Data Engineer blueprint. Practicing with targeted Preparing and Using Data for Analysis questions ensures you can handle any format or difficulty that appears.
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