PDE Storing the Data Practice Test 2 — 15 Questions

Question 1

A team is designing a Spanner database for a global inventory system. They need to optimize query performance for frequently joined tables. Which THREE design decisions help achieve this? (Choose 3.)

Accepted Answer

Design primary keys to distribute write load evenly across splits.. Spanner uses distributed splits for scalability, and designing primary keys to avoid hotspots (e.g., using hash prefixes or monotonically increasing keys) ensures write load is evenly distributed across splits, preventing performance bottlenecks.

Answer

Use Cloud SQL instead if joins are needed.

Answer

Store all data in a single table with JSON columns to avoid joins.

Question 2

A company needs to store petabytes of time-series IoT sensor data and query it with single-digit millisecond latency at millions of reads per second. The data has a simple key-value structure with timestamps. Which Google Cloud database is MOST appropriate?

Accepted Answer

Cloud Bigtable. Cloud Bigtable is a fully managed, scalable NoSQL database designed for large analytical and operational workloads, handling petabytes of data with consistent sub-10ms latency at millions of reads per second. Its key-value model with timestamps directly matches the time-series IoT sensor data structure, and it supports high-throughput, low-latency access via the HBase API or Bigtable client libraries.

Answer

BigQuery

Answer

Cloud Spanner

Answer

Firestore

Question 3

A company stores highly sensitive financial data in BigQuery. They need to encrypt certain columns (e.g., credit card numbers) with customer-managed encryption keys (CMEK) at the column level. Which BigQuery feature should they use?

Accepted Answer

AEAD encryption functions with Cloud KMS. BigQuery's AEAD (Authenticated Encryption with Associated Data) encryption functions, when used with Cloud KMS, allow you to encrypt individual columns (e.g., credit card numbers) using customer-managed encryption keys (CMEK) at the column level. This provides granular, application-layer encryption where the key is managed by the customer, not Google, and decryption is performed within SQL queries using the AEAD.DECRYPT_STRING function. Dataset-level CMEK (Option A) encrypts all data at rest in the dataset but cannot target specific columns.

Answer

Customer-managed encryption keys (CMEK) on the dataset

Answer

VPC Service Controls

Answer

BigQuery Data Catalog with policy tags

Question 4

A mobile app needs a real-time NoSQL database that supports offline sync and automatic conflict resolution. Which Google Cloud database is best suited?

Accepted Answer

Firestore. Firestore is the correct choice because it is a NoSQL, real-time database that provides offline data persistence and automatic conflict resolution via multi-version concurrency control (MVCC) and last-write-wins (LWW) semantics. Its client SDKs synchronize data in the background when connectivity is restored, making it ideal for mobile apps requiring offline-first functionality.

Answer

Cloud SQL

Answer

Cloud Bigtable

Answer

Cloud Spanner

Question 5

A data engineer is designing a Bigtable row key for a time-series dataset where each row represents a sensor reading. The team expects high write throughput and wants to avoid hotspots. Which row key design is BEST?

Accepted Answer

Sensor ID followed by reversed timestamp. Prepending the sensor ID distributes writes across multiple tablet servers, while reversing the timestamp ensures that recent data (which is written most frequently) is spread across different rows rather than creating a hotspot at the end of the table. This design leverages Bigtable's lexicographic ordering to balance write load.

Answer

Only sensor ID

Answer

Timestamp followed by sensor ID

Answer

Random salt appended to timestamp

Question 6

A company has a Cloud SQL for PostgreSQL instance and wants to create a read replica to offload read traffic from the primary. They also need to ensure the replica is in a different region for disaster recovery. Which Cloud SQL feature should they use?

Accepted Answer

Cross-region read replica. Cross-region read replicas in Cloud SQL for PostgreSQL allow you to create a replica in a different region from the primary instance. This offloads read traffic from the primary while also providing disaster recovery capabilities by maintaining a standby copy in a geographically separate location. The replica uses asynchronous replication to stay up-to-date with the primary.

Answer

External replica

Answer

Automatic backups with PITR

Answer

High availability (HA) configuration

Question 7

A global e-commerce platform requires a relational database that can handle millions of transactions per second across regions with strong consistency and automatic failover. The database must also support SQL joins. Which database should they choose?

Accepted Answer

Cloud Spanner. Cloud Spanner is the correct choice because it is a globally distributed, horizontally scalable relational database that provides strong consistency and automatic failover across regions, while fully supporting SQL joins. It combines the benefits of a traditional relational database with the horizontal scalability of NoSQL systems, making it ideal for high-throughput, globally distributed applications requiring ACID transactions.

Answer

Cloud SQL

Answer

Cloud Firestore

Answer

Cloud Bigtable

Question 8

A company has a Cloud SQL for PostgreSQL instance that must be highly available across zones with automatic failover. They also need a read replica for reporting workloads. Which configuration should they use?

Accepted Answer

Enable high availability (regional) and create a read replica. Cloud SQL for PostgreSQL supports regional high availability (HA) with synchronous replication across two zones, ensuring automatic failover without data loss. Additionally, you can create a read replica in a different zone or region to offload reporting workloads without impacting the primary instance's performance. This combination meets both the high availability and read replica requirements.

Answer

Deploy Cloud SQL in multi-region mode

Answer

Enable automatic backups and point-in-time recovery

Answer

Create a cross-region replica and use it for failover

Question 9

A data engineer wants to set up automatic deletion of objects from a Cloud Storage bucket after 30 days, and transition objects older than 7 days to Nearline storage. Which THREE steps should they take? (Select three.)

Accepted Answer

Create a lifecycle rule with action Delete and condition age 30 days. Lifecycle rules in Google Cloud Storage allow you to specify an action (Delete) and a condition (age 30 days) to automatically delete objects after 30 days. This meets the requirement for automatic deletion without manual intervention.

Answer

Set the bucket's default storage class to Nearline

Answer

Enable Object Versioning on the bucket

Question 10

You are designing a Cloud Storage bucket to hold sensitive financial documents that must not be deleted or overwritten for 7 years. After the retention period, the documents can be deleted automatically. Which configuration should you use?

Accepted Answer

Use Bucket Lock with a retention policy of 7 years and configure a lifecycle rule to delete objects after 7 years.. Bucket Lock (also known as Object Lock) provides a WORM (Write Once, Read Many) retention policy that prevents objects from being deleted or overwritten for a specified period. By setting a retention policy of 7 years, you enforce the required compliance hold. Then, a lifecycle rule configured to delete objects after 7 years ensures automatic removal once the retention period expires. This combination meets both the retention and automatic deletion requirements.

Answer

Set a retention policy on the bucket for 7 years and enable Object Versioning.

Answer

Use Object Lock with WORM mode and set a retention period of 7 years. After the period, objects are automatically deleted.

Answer

Set a lifecycle rule to delete objects after 7 years and enable Bucket Lock.

Question 11

You need to store and query a large dataset of customer profiles. The data is semi-structured and frequently updated. The application requires offline support for mobile users. Which database is MOST appropriate?

Accepted Answer

Firestore. Firestore is the most appropriate choice because it is a NoSQL document database designed for semi-structured data, real-time synchronization, and offline support. It provides built-in offline persistence for mobile clients, allowing users to read and write data even without network connectivity, and automatically syncs changes when the connection is restored. This directly meets the requirements of semi-structured data, frequent updates, and offline mobile support.

Answer

BigQuery

Answer

Cloud Bigtable

Answer

Cloud SQL

Question 12

Which Google Cloud database offers global distribution, strong consistency, and a 99.999% SLA?

Accepted Answer

Cloud Spanner. Cloud Spanner is the only Google Cloud database that provides global distribution (horizontally scaling across regions), strong consistency (external consistency with TrueTime), and a 99.999% SLA. It combines the benefits of relational database structure with non-relational horizontal scale, making it ideal for globally distributed, strongly consistent workloads.

Answer

Cloud Bigtable

Answer

Firestore

Answer

Cloud SQL

Question 13

A team needs to run analytics on data stored in Cloud Storage (Parquet format) without moving it into BigQuery storage. They want to use SQL queries and BigQuery features like caching and partitioning. Which approach should they use?

Accepted Answer

Create a BigQuery external table pointing to the Cloud Storage location.. An external table in BigQuery allows you to query data stored in Cloud Storage (Parquet format) using standard SQL without moving the data into BigQuery's managed storage. This approach supports BigQuery features like caching (results caching) and partitioning (by defining a Hive-partitioned external table), meeting all requirements.

Answer

Use Cloud Dataproc to run Spark SQL on the data.

Answer

Load the data into BigQuery tables using a batch load job.

Answer

Use BigLake tables with Cloud Storage.

Question 14

A company needs to store petabytes of time-series IoT sensor data and query it with single-digit millisecond latency at millions of reads per second. The data has a simple key-value structure with timestamps. Which Google Cloud database is MOST appropriate?

Accepted Answer

Cloud Bigtable. Cloud Bigtable is the correct choice because it is a fully managed, scalable NoSQL database designed for large analytical and operational workloads, offering single-digit millisecond latency for high-throughput reads and writes. Its key-value model with timestamp-based versioning directly matches the time-series IoT sensor data pattern, and it can handle petabytes of data with millions of reads per second using its distributed, sorted key-value store (based on Google's Chubby and GFS).

Answer

Cloud Spanner

Answer

Firestore

Answer

BigQuery

Question 15

A company is designing a Cloud Bigtable row key for a time-series dataset of device readings. They want to avoid hotspotting (uneven load across tablets). Which TWO row key design patterns are effective? (Choose 2)

Accepted Answer

Reverse the timestamp string. (reverse timestamp string) and Option D (prepend a hash of device ID to timestamp) are both effective at avoiding hotspotting. Reversing the timestamp (e.g., using MAX_TIME - ts) spreads recent writes across tablets instead of concentrating them on a single tablet. Prepending a hash of device ID distributes writes uniformly across the keyspace. Option A (monotonically increasing counter) and Option B (timestamp directly) cause hotspotting because all new data goes to a single tablet. Option E (secondary index) is not a row key design pattern; it does not affect tablet distribution.

Answer

Use a monotonically increasing counter as row key

Answer

Use timestamp directly as the first part of the row key

Answer

Use a secondary index on the timestamp column