PCD Practice Test 3 — 25 Questions

Question 1

A financial services firm needs a database for real-time fraud detection. The workload requires sub-millisecond read latency on transactions and the ability to run complex SQL analytics on the same data. Which database should they choose?

Accepted Answer

AlloyDB. AlloyDB is correct because it combines PostgreSQL-compatible SQL analytics with sub-millisecond read latency for transactional workloads, using a columnar engine for analytical queries and a memory-optimized row store for real-time transactions. This dual-engine architecture allows the same data to serve both OLTP and OLAP requirements without data movement or latency trade-offs.

Answer

BigQuery

Answer

Cloud Bigtable

Answer

Cloud Spanner

Question 2

You are designing a Bigtable schema for an ad-tech platform that tracks ad impressions. Each impression has a unique ID, timestamp, user ID, and campaign ID. Queries frequently filter by user ID and time range. Which row key design is MOST appropriate to avoid hotspots and support efficient range scans?

Accepted Answer

Use a composite key: hashed user ID prefix + timestamp. Option B is correct because using a hashed user ID prefix distributes writes across Bigtable tablets, preventing hotspots from sequential user IDs, while appending the timestamp enables efficient range scans within a user's data. Bigtable's lexicographic ordering on row keys means that the hashed prefix ensures load balancing, and the timestamp suffix allows scanning a specific time range for a given user without scanning irrelevant rows.

Answer

Use the user ID as the row key

Answer

Use a composite key: campaign ID + timestamp

Answer

Use the impression ID as the row key

Question 3

A company is migrating from Oracle to Cloud SQL for PostgreSQL using Database Migration Service. They need to ensure that the migration is validated before cutover. Which TWO actions should they take?

Accepted Answer

Run comparison queries (e.g., row counts, checksums) between source and target.. Option A is correct because running comparison queries (e.g., row counts, checksums) between the source Oracle database and the target Cloud SQL for PostgreSQL instance is a standard validation technique to ensure data consistency and completeness before cutover. Database Migration Service (DMS) handles continuous replication, but manual validation using queries like SELECT COUNT(*) or checksum functions (e.g., MD5 on concatenated columns) catches any discrepancies that might have been introduced during migration or replication lag.

Answer

Take a snapshot of the source database before migration.

Answer

Disable foreign key constraints on the target to speed up migration.

Answer

Implement shadow writes from the application to both databases simultaneously.

Question 4

An organization is migrating a 5 TB MySQL database from on-premises to Cloud SQL. The source uses MyISAM tables and the utf8 charset. The target must support full Unicode (emojis). The migration should have minimal downtime. What should the organization do?

Accepted Answer

Convert MyISAM to InnoDB, change charset to utf8mb4, then use DMS continuous migration.. Option C is correct because Cloud SQL requires InnoDB tables (MyISAM is not supported) and utf8mb4 charset for full Unicode support including emojis. By first converting the tables and charset on-premises, then using Database Migration Service (DMS) continuous migration, you achieve minimal downtime through ongoing replication.

Answer

Export the database using mysqldump and import into Cloud SQL; change charset after import.

Answer

Use DMS continuous migration directly; DMS automatically converts MyISAM to InnoDB and changes charset.

Answer

Use DMS one-time migration, convert MyISAM to InnoDB during migration.

Question 5

A company uses Cloud Run for a serverless application. They notice that cold starts are causing high latency for some requests. What is the best strategy to reduce cold starts?

Accepted Answer

Set a minimum number of instances to keep containers always warm. Option B is correct because setting a minimum number of instances ensures that Cloud Run keeps a baseline of container instances always warm and ready to serve requests. This eliminates cold starts for the first requests that hit those pre-warmed instances, directly addressing the latency issue. Cloud Run automatically scales to zero when idle, but a minimum instance setting overrides that behavior for the specified number of containers.

Answer

Increase the max instances setting

Answer

Migrate the application to Cloud Functions

Answer

Reduce the container concurrency setting

Question 6

A company uses Cloud Bigtable for AdTech clickstream data. They notice that some tables have many deleted rows and the storage cost is increasing. What should they configure to automatically remove deleted data and reduce storage?

Accepted Answer

Configure a garbage collection policy based on time or number of versions. Cloud Bigtable automatically compacts data in the background, but deleted rows are not immediately removed; they remain as tombstones until a garbage collection policy is applied. By configuring a garbage collection policy based on time or number of versions, you instruct Bigtable to permanently delete expired data and reclaim storage space, reducing costs.

Answer

Set up cluster replication to a secondary cluster

Answer

Enable compaction on the table

Answer

Use IAM conditions to restrict write access

Question 7

You want to migrate an on-premises MySQL database to Cloud SQL with minimal downtime. The source database is 500 GB. Which approach is recommended?

Accepted Answer

Create a Cloud SQL read replica from the on-premises source using Database Migration Service. Database Migration Service (DMS) is specifically designed for minimal-downtime migrations from on-premises MySQL to Cloud SQL. It uses continuous replication (CDC) to keep the source and target synchronized, allowing a short cutover window after the initial 500 GB data load. This approach avoids the downtime required by export/import or file-copy methods.

Answer

Use BigQuery Data Transfer Service to migrate the data

Answer

Copy the data files to a Cloud Storage bucket and use the Cloud SQL import

Answer

Export the database using mysqldump and import to Cloud SQL using gcloud sql import

Question 8

You are designing a Cloud Bigtable schema for a time-series application that stores metrics from millions of devices. The row key is currently deviceID#timestamp. You want to avoid hotspotting on writes and optimize scan performance for reading all data from a specific device within a time range. Which two row key design strategies should you apply? (Choose 2)

Accepted Answer

Add a salt prefix to the row key (e.g., hash of deviceID). Option A is correct because adding a salt prefix (e.g., a hash of the deviceID) distributes write load across multiple tablet servers, preventing hotspotting on a single node when many devices write concurrently. This ensures that sequential timestamps for the same device are not all written to the same tablet, which would otherwise cause a bottleneck.

Answer

Store data in multiple tables per device

Answer

Use a single column family for all metrics

Answer

Promote the device type to the start of the row key

Question 9

You are using Cloud Spanner and need to read data that is consistent as of a specific point in time, but you can tolerate staleness up to 10 seconds. Which read type should you use to achieve the best performance?

Accepted Answer

Stale read with a staleness bound of 10 seconds. Option D is correct because a stale read with a staleness bound of 10 seconds allows Cloud Spanner to serve the read from any replica that has data within that staleness window, avoiding the latency of a strong read that must contact the leader. This provides the best performance while still guaranteeing consistency as of a point in time up to 10 seconds ago, meeting the requirement of tolerating staleness up to 10 seconds.

Answer

Partitioned read

Answer

Read with a timestamp bound exactly 10 seconds in the past

Answer

Strong read

Question 10

A social media app uses Firestore in Native mode. The app has a feature that shows a user's recent posts. The query sorts by timestamp descending and limits to 10 results. As the user base grows, the queries become slow. Which optimization should you implement FIRST?

Accepted Answer

Create a composite index on (user_id, timestamp) for the posts collection. Option B is correct because the query filters by `user_id` and sorts by `timestamp` descending, which requires a composite index on `(user_id, timestamp DESC)` to avoid a full collection scan. Without this index, Firestone performs a sort in memory, which becomes slow as the dataset grows. Creating the composite index allows Firestore to serve the query directly from the index, dramatically improving performance.

Answer

Increase the Firestore quota for reads

Answer

Use Cloud SQL for this query instead of Firestore

Answer

Denormalize the user’s recent posts into a subcollection

Question 11

A financial services company is building a global payment system that requires strong ACID transactions across multiple regions, with 99.999% availability and automatic failover. Which Google Cloud database should they choose?

Accepted Answer

Cloud Spanner. Cloud Spanner is the only Google Cloud database that provides strong ACID transactions across multiple regions with 99.999% availability and automatic failover. It uses a globally distributed architecture with synchronous replication and the TrueTime API to ensure external consistency, making it ideal for a global payment system that requires strict consistency and high availability.

Answer

AlloyDB with cross-region replicas

Answer

Cloud SQL with cross-region replicas

Answer

Firestore in Native mode

Question 12

A company needs to analyze terabytes of log data for business intelligence. The queries are complex SQL aggregations and run periodically. The data is read-only after ingestion. Which Google Cloud database is most suitable?

Accepted Answer

BigQuery. BigQuery is the correct choice because it is a serverless, highly scalable data warehouse designed for petabyte-scale analytics using SQL. It excels at complex aggregations over terabytes of read-only log data, with automatic partitioning and columnar storage that optimize query performance for periodic analytical workloads.

Answer

Cloud SQL

Answer

Cloud Spanner

Answer

Cloud Bigtable

Question 13

A developer deploys the above app.yaml to App Engine standard environment. The deployment succeeds, but the application fails to connect to the database. What is the most likely reason?

Accepted Answer

The application is trying to connect to a local database on localhost, which is not available in the App Engine sandbox.. Option C is correct because in App Engine standard environment, applications run in a sandboxed environment that does not support connections to a local database on localhost. The application code is attempting to connect to a database at 127.0.0.1 or localhost, which is not available in the sandbox. Instead, the application must connect to a Cloud SQL instance using a Unix socket or a private IP, or use a fully managed database service like Firestore.

Answer

The runtime 'python39' is not supported in App Engine standard environment.

Answer

The $PORT environment variable is not set in App Engine standard environment.

Answer

The entrypoint command is incorrect because gunicorn is not allowed.

Question 14

You want to configure Cloud SQL for MySQL with high availability (HA). The application requires that failover be automatic and that the failover replica is in a different zone within the same region. Which configuration should you use?

Accepted Answer

Enable the high availability option when creating the Cloud SQL instance. Option B is correct because enabling the high availability (HA) option when creating a Cloud SQL for MySQL instance automatically provisions a standby replica in a different zone within the same region. This configuration provides automatic failover without manual intervention, meeting the requirement for HA with zone-level redundancy.

Answer

Use Cloud SQL with a cross-region replica and configure failover with gcloud

Answer

Set up a Cloud SQL instance with multiple read replicas and use failover via the Cloud SQL Proxy

Answer

Create a read replica in a different zone and configure automatic failover using a load balancer

Question 15

A team is designing a disaster recovery plan for a critical application on Google Cloud. The application runs on Compute Engine with a regional persistent disk. They want to minimize data loss in case of a regional outage. Which strategy should they use?

Accepted Answer

Use persistent disk snapshot replication to another region. Persistent disk snapshot replication to another region is the correct strategy because snapshots are stored in Cloud Storage and can be replicated across regions. This allows you to restore the disk from a snapshot in a different region if the primary region experiences an outage, minimizing data loss by ensuring the backup is geographically separate. Regional persistent disks are synchronous within a region but do not provide cross-region replication, so snapshots are the recommended approach for cross-region disaster recovery.

Answer

Create a snapshot schedule and store snapshots in the same region

Answer

Use synchronous replication across regions

Answer

Configure a managed instance group with autohealing

Question 16

A company is designing a global e-commerce platform on Google Cloud. The application requires low-latency access for users worldwide and must be highly available. Which load balancing solution should they use?

Accepted Answer

External HTTP(S) Load Balancer. The External HTTP(S) Load Balancer is the correct choice because it is a global, proxy-based Layer 7 load balancer that terminates HTTP/HTTPS traffic at Google's edge points of presence (PoPs) and routes requests to the nearest healthy backend. This provides low-latency access for users worldwide by leveraging Google's global network and anycast IPs, while also offering built-in high availability, SSL offloading, and content-based routing.

Answer

External TCP/UDP Network Load Balancer

Answer

Cloud CDN

Answer

Internal TCP/UDP Load Balancer

Question 17

A company is deploying a Cloud Spanner instance for a global application. They need to minimize write latency for users in Asia, Europe, and North America while maintaining strong consistency. Which two configurations should they choose? (Choose two.)

Accepted Answer

Use nodes for compute capacity. Option A is correct because nodes provide dedicated compute and storage resources in Cloud Spanner, which are necessary to achieve low write latency and high throughput for a global application. Nodes scale linearly with performance, ensuring that write operations are processed quickly across multiple regions. For a multi-region instance, nodes are the only supported compute capacity option, as processing units are limited to regional instances.

Answer

Use a regional instance configuration

Answer

Use processing units for compute capacity

Answer

Enable auto-scaling with a high-priority CPU target

Question 18

You are designing a Cloud Spanner database for a global user application that must enforce strong consistency across regions. The primary key of the main table is a UUID. You notice that write latency is high and suspect hotspotting. Which design change is MOST likely to reduce hotspotting?

Accepted Answer

Use a composite primary key with a leading hash prefix of the UUID. Option B is correct because using a composite primary key with a leading hash prefix distributes writes evenly across all Cloud Spanner splits, preventing hotspotting. A UUID primary key alone can still cause hotspots if the UUID generation is not perfectly random or if the application uses sequential UUIDs; a hash prefix ensures uniform distribution regardless of the UUID's characteristics.

Answer

Use a monotonically increasing integer as the primary key

Answer

Switch to regional Spanner instance instead of multi-region

Answer

Use a secondary index on the UUID column and keep the primary key as a UUID

Question 19

You need a database that provides 99.999% availability SLA, global distribution, and supports ACID transactions across regions. Which Google Cloud database meets these requirements?

Accepted Answer

Cloud Spanner. Cloud Spanner is the only Google Cloud database that provides 99.999% availability SLA, global distribution via automatic synchronous replication across regions, and full ACID transactions across regions using TrueTime and Paxos-based consensus. It combines horizontal scalability with strong consistency, making it ideal for globally distributed applications requiring transactional integrity.

Answer

Bigtable

Answer

Cloud SQL

Answer

Firestore

Question 20

You need to design a database for a global inventory management system that requires strong consistency across regions and can handle millions of updates per day. The system must maintain ACID transactions across multiple warehouses. Which database service and configuration would you choose?

Accepted Answer

Cloud Spanner multi-region instance. Cloud Spanner is the only Google Cloud database service that provides ACID transactions across regions with strong external consistency, making it ideal for a global inventory system requiring millions of updates per day. Its multi-region instance configuration uses synchronous replication and the TrueTime API to ensure linearizable transactions, meeting the need for strong consistency across warehouses.

Answer

Bigtable with cluster replication

Answer

Firestore in multi-region mode

Answer

Cloud SQL with cross-region replicas