Free Data Practitioner Practice Test

A retail company collects customer purchase data that includes transaction amounts, product categories, customer IDs, and timestamps. The data team needs to identify which type of data the 'product category' field represents. What type of data is this?

Your organization stores both structured sales data in BigQuery and unstructured customer feedback images in Cloud Storage. A business analyst asks what distinguishes these two data types. What is the key difference between structured and unstructured data?

A data team is designing a schema for an e-commerce database. They need to store customer orders where each order can contain multiple products. What type of relationship exists between orders and products in this scenario?

Your company receives IoT sensor data every second from thousands of devices, generating high-velocity data streams. The data includes temperature readings, device IDs, and timestamps. What characteristic of Big Data does this scenario primarily represent?

A healthcare provider needs to ensure that patient medical records maintain their accuracy and consistency throughout their lifecycle, from creation to archival. Data should not be corrupted or altered inappropriately. Which data quality dimension is the primary concern?

A startup needs to analyze 500 GB of customer transaction data stored in CSV files. They want to run SQL queries without managing infrastructure. Which Google Cloud service should they use?

Your team needs to store application logs, backup files, and infrequently accessed historical data totaling 10 TB. Cost optimization is a priority, and the data doesn't require frequent access. Which Google Cloud storage option provides the most cost-effective solution?

A financial services company runs a PostgreSQL database on-premises and wants to migrate to Google Cloud with minimal changes to their application code. They need automated backups, high availability, and managed maintenance. What should they choose?

Your company ingests streaming clickstream data from a website and needs to process it in real-time before storing it in BigQuery for analysis. The solution must handle variable data rates and process events with low latency. Which Google Cloud service is best suited for this stream processing?

A data engineer needs to schedule and orchestrate a complex data pipeline that includes multiple steps: extracting data from Cloud Storage, transforming it with Dataflow, loading it into BigQuery, and triggering downstream processes. The workflow runs daily and has dependencies between tasks. What tool should they use?

Your organization stores 50 TB of historical transaction data in BigQuery. Analysts frequently query recent data (last 6 months) but rarely access older data. You want to optimize costs while maintaining query performance for recent data. What BigQuery feature should you implement?

A data analyst needs to quickly explore and visualize sales data stored in BigQuery to create interactive dashboards for executives. They need a tool that integrates directly with BigQuery without data movement and requires minimal technical setup. What should they use?

A data team has messy CSV files with inconsistent date formats, missing values, and duplicate records. They need to clean and standardize this data before loading it into BigQuery. Which Google Cloud service provides a visual interface for data preparation without writing code?

Your marketing team wants to understand which advertising channels drive the most conversions. You have data showing ad spend by channel, website visits, and conversion events. What type of analysis should you perform to answer this question?

A retail company wants to forecast next quarter's sales based on historical sales data, seasonal trends, and economic indicators. What type of analytics approach is this?

A data analyst creates a visualization showing monthly revenue trends over three years for a business presentation. The chart clearly shows a 15% decline in Q2 2024. What type of insight does this represent?

Your team built a BigQuery ML model to predict customer churn. After deployment, you need to monitor the model's accuracy over time to detect if it degrades. When actual outcomes are compared to predictions, accuracy has dropped from 85% to 72%. What problem is the model experiencing?

A data analyst runs a query in BigQuery and gets results showing the average order value for each product category. To make the findings actionable for the sales team, what should the analyst include in their report beyond just the raw numbers?

Your company handles customer health records and must ensure that only authorized healthcare providers can access patient data. Which Google Cloud feature should you implement to control who can access specific BigQuery datasets?

A financial institution stores sensitive customer data in BigQuery and must comply with regulations requiring encryption of data at rest. They want to maintain control over encryption keys. What should they implement?