Storing Data

Selecting and configuring appropriate storage systems, data warehouses, data lakes, and data platforms on Google Cloud for diverse workloads.

5 minutes 5 Questions

Storing Data in Google Cloud involves selecting the right storage solution based on data type, access patterns, cost, and performance requirements. Google Cloud offers several storage services tailored for different use cases. **Cloud Storage** is an object storage service ideal for unstructured d…

Concepts covered
Cloud SQL and AlloyDB for Managed DatabasesData Access Pattern AnalysisBigtable for NoSQL WorkloadsCloud Storage for Object and Unstructured DataStorage Cost and Performance PlanningData Warehouse Modeling and NormalizationData Lake Processing and MonitoringFederated Governance for Distributed Data SystemsBigQuery for Analytics and Data WarehousingCloud Spanner for Global Relational DataFirestore and Memorystore for Specialized StorageData Lifecycle ManagementData Lake Management and Cost ControlsDataplex and BigLake for Data Platforms
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GCP Data Engineer - Storing Data Example Questions

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Question 1

A team wants to load data from a CSV file stored in Cloud Storage into a new BigQuery table. They want the simplest approach using the BigQuery console. What should they do?

Correct Answer: Use the BigQuery console to create a table and specify the Cloud Storage CSV file as the source

The simplest approach to load CSV data from Cloud Storage into BigQuery is to use the BigQuery console's built-in "Create Table" feature, where you specify the source as a Cloud Storage URI, select CSV format, and BigQuery handles the ingestion into a native managed table.

Creating an external (federated) table does not actually load data into BigQuery; it only creates a reference to the external file, so the data remains in Cloud Storage. Setting up a scheduled transfer job is unnecessarily complex for a one-time load operation. Running a LOAD DATA SQL statement requires additional manual steps beyond what the console's UI provides, making it less simple.

Key concept tested: BigQuery native data loading from Cloud Storage using the console UI.

Question 2

A team wants to find out how much storage their BigQuery tables are using. Where should they look to find this information?

Correct Answer: Query the INFORMATION_SCHEMA.TABLE_STORAGE view to retrieve storage usage details for each table

The INFORMATION_SCHEMA.TABLE_STORAGE view is specifically designed to provide detailed storage usage information for BigQuery tables, including active bytes, long-term bytes, and total logical and physical bytes per table.

The JOBS view contains metadata about query and load jobs, not storage metrics. The DESCRIBE statement provides schema information such as column names and data types, not storage usage details. Cloud Monitoring with slot utilization filtering tracks compute resource consumption, not per-table storage allocation.

The key concept tested is understanding BigQuery's INFORMATION_SCHEMA views and knowing which specific view provides table-level storage consumption details.

Question 3

What does the Cloud Storage metric `storage.googleapis.com/storage/total_bytes` represent?

Correct Answer: The total size of all objects stored in a bucket

The metric storage.googleapis.com/storage/total_bytes represents the total size of all objects stored in a bucket, measured as a gauge metric that reflects the current storage usage at any point in time.

Cumulative bytes transferred out would be tracked by network egress metrics, not storage total bytes. Total API requests are counted by separate request count metrics such as api/request_count. Aggregate bytes uploaded during a billing cycle describes an upload volume metric, not a storage size metric.

The key concept tested is understanding Cloud Storage monitoring metrics in Cloud Monitoring, specifically distinguishing between storage size gauge metrics and transfer or request metrics.

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