Google PCA Analysing and Optimising Technical and Business Processes — 50 Questions

Question 1

A company has a BigQuery dataset with a growing fact table (500 million rows, added daily). Queries that filter on a date column and group by a product ID are slow. The team wants to optimise query performance without increasing slot costs. Which two actions should they take? (Choose TWO.)

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Cluster the table by the product ID column. Partitioning by date and clustering by product ID can significantly improve query performance: partitioning prunes scans to relevant days, and clustering co-locates rows with similar product IDs. Materialised views pre-aggregate data but increase storage costs. SELECT * is inefficient and should be avoided. Slots pricing does not help performance without changing configuration.

Answer

Switch to BigQuery Editions with Autoscaling slots

Answer

Use SELECT * only when necessary

Answer

Create a materialised view that pre-aggregates the data

Question 2

A company wants to reduce Cloud Storage costs for archival data that is accessed less than once a year. They have data stored in Standard storage class. Which TWO actions should they take? (Choose TWO.)

Accepted Answer

Use a lifecycle rule to delete objects that are older than 365 days. Option A is correct because a lifecycle rule that deletes objects older than 365 days removes archival data that is accessed less than once a year, eliminating ongoing storage charges for data the company no longer needs. Option D is correct because transitioning objects older than 365 days to the Archive storage class moves rarely accessed data to the lowest-cost Cloud Storage class, which is designed for data accessed less than once a year. Option B is not appropriate because Coldline is intended for data accessed less than once a quarter, not less than once a year, so it is more expensive than Archive for this use case. Option C is incorrect because renaming objects or prefixes does not change the storage class or reduce storage costs. Option E is incorrect because Nearline is for data accessed less than once a month, which is far more frequent than this scenario requires and would not minimize costs.

Answer

Set a lifecycle rule to transition objects older than 90 days to Coldline storage class

Answer

Rename objects to start with 'archive/' to reduce costs

Answer

Set a lifecycle rule to transition objects older than 30 days to Nearline storage class

Question 3

A company is designing a disaster recovery strategy for a critical application. They need to ensure that if the primary region fails, the application can fail over to a secondary region with a Recovery Point Objective (RPO) of 1 hour and a Recovery Time Objective (RTO) of 15 minutes. The application uses Cloud Spanner. Which Cloud Spanner configuration meets these requirements?

Accepted Answer

Multi-region configuration (e.g., nam3). Cloud Spanner multi-region configurations provide automatic replication across regions with synchronous replication, achieving RPO near zero and RTO in seconds. Single-region configurations do not provide geographic failover. A two-region setup like nam3 (US Central + US East) or eur3 (Europe) provides synchronous replication with strong consistency. Active/active is the default. Multi-region configs are recommended for DR.

Answer

Single-region configuration

Answer

Backup and restore in another region

Answer

Dual-region with asynchronous replication

Question 4

A company runs a Bigtable instance for real-time analytics. They notice increasing latency for point reads. The row key pattern is 'YYYYMMDD_userID_productID'. The team suspects read hotspots. Which row key design change would BEST distribute the load across tablets?

Accepted Answer

Reorder to 'userID_productID_YYYYMMDD'. Using a field prefix (like user ID) before the timestamp distributes reads across tablets because Bigtable orders rows lexicographically. If the timestamp is first, recent data falls into a single tablet, causing hotspots. Reversing the timestamp or hashing the row key are also common strategies. Salting with a random prefix is another approach.

Answer

Prepend a random number field: 'random_YYYYMMDD_userID_productID'

Answer

Keep the same order but use a shorter timestamp: 'YYMMDD_userID_productID'

Answer

Reverse the timestamp: 'DDMMYYYY_userID_productID'

Question 5

A company wants to monitor the performance of their application against a Service Level Objective (SLO) of 99.9% availability over a 30-day rolling window. They plan to use Google Cloud's operations suite (formerly Stackdriver). Which metric should they use as the Service Level Indicator (SLI) for availability?

Accepted Answer

Successful request ratio. Availability is typically measured as the fraction of successful requests (e.g., HTTP 200-499) vs total requests. The other options are valid SLIs for other aspects: latency, error rate, or saturation.

Answer

Error rate

Answer

Request latency

Answer

CPU utilization

Question 6

A company wants to optimise costs for their BigQuery workloads. They have a predictable set of queries that run every hour, with occasional ad-hoc queries from analysts. They want to reduce the cost of the scheduled queries while keeping the ad-hoc queries cost-effective. Which pricing model should they choose?

Accepted Answer

Use Editions with a baseline of reserved slots for scheduled queries and allow autoscaling for ad-hoc. BigQuery slot reservations (editions) allow allocating dedicated slots for predictable workloads, with separate pricing for ad-hoc queries (on-demand). Flat-rate pricing is cheaper for large steady-state usage but may waste slots during idle time. On-demand is pay-per-query; for predictable queries, it may be expensive. The best approach is to use a hybrid: reserved slots for scheduled queries and on-demand for ad-hoc.

Answer

On-demand pricing

Answer

Use committed use discounts on slots

Answer

Flat-rate pricing (slot reservations) for all queries

Question 7

A company running a globally distributed application uses Firestore in Native mode. They need strong consistency for reads and writes across multiple regions. Which deployment strategy should they use?

Accepted Answer

Use Cloud Spanner. Cloud Spanner is the correct choice because it is Google Cloud's globally distributed, strongly consistent database that supports multi-region replication with external consistency. Firestore in Native mode, even in multi-region configuration, provides strong consistency only within a region and eventual consistency across regions for some operations.

Answer

Use Bigtable with replication

Answer

Use Firestore in Datastore mode

Answer

Use Firestore in multi-region (nam5 or eur3)

Question 8

A company uses Cloud CDN to serve static content from a Cloud Storage bucket. They update the content every 2 hours, but users sometimes see stale content for up to 24 hours. They need users to see the latest content within 5 minutes of an update. Which action should they take?

Accepted Answer

Set a short max-age TTL (e.g., 5 minutes) on the objects. Setting a short max-age TTL (e.g., 5 minutes) on the objects controls how long Cloud CDN caches them, ensuring users see updates within 5 minutes. This is the correct, scalable solution because it aligns cache freshness with the update frequency without manual intervention. Cache invalidation is a manual/operational workaround, not a configuration best practice for frequent updates.

Answer

Use cache invalidation after each update

Answer

Use custom cache keys

Answer

Change cache mode to CACHE_ALL_STATIC

Question 9

A company needs to choose between Cloud Spanner and Firestore for a global inventory application that requires strong consistency and horizontal scaling. The application has a fixed schema with complex joins. Which database is most appropriate?

Accepted Answer

Cloud Spanner. Cloud Spanner is a globally distributed relational database with strong consistency and supports complex joins. Firestore is a NoSQL document database with eventual consistency (strong consistency only within a single document).

Answer

Firestore

Answer

Cloud SQL

Answer

Cloud Bigtable

Question 10

A financial services company requires a storage solution for transaction logs that must be retained for 7 years due to regulatory compliance. After 90 days, the data is rarely accessed but must be retrievable within 1 hour. After 1 year, it must be archived with retrieval within 24 hours. Which Cloud Storage lifecycle policy should they implement?

Accepted Answer

Transition to Nearline after 90 days, then to Archive after 1 year. Option B is correct because Nearline storage is designed for data accessed less than once a month with retrieval typically within milliseconds to seconds, satisfying the 1-hour retrieval requirement after 90 days, and Archive storage provides the required 24-hour retrieval for long-term retention after 1 year. Coldline in option A would also meet the retrieval time, but Nearline is the more appropriate and cost-effective fit for the stated access pattern of rare access after 90 days. Option C is wrong because moving directly to Archive after 90 days would not meet the 1-hour retrieval requirement at that stage. Option D is wrong because keeping data in Standard after 90 days is unnecessary and more expensive for rarely accessed data, and it does not align with the required retrieval performance tiering.

Answer

Transition to Coldline after 90 days, then to Archive after 1 year

Answer

Transition to Archive after 90 days

Answer

Transition to Standard after 90 days, then to Archive after 1 year

Question 11

A company is designing a disaster recovery plan for a critical application running on Compute Engine. The application requires a Recovery Point Objective (RPO) of 15 minutes and a Recovery Time Objective (RTO) of 4 hours. Which TWO strategies meet these requirements? (Choose 2)

Accepted Answer

Warm standby: run a scaled-down version of the environment in another region. Option B (warm standby: run a scaled-down version of the environment in another region) is correct because the standby environment is already running and continuously receiving replicated data, so it can meet the 15-minute RPO and be scaled up to full capacity well within the 4-hour RTO. Option D (pilot light: replicate data to another region and have a minimal footprint ready) is correct because continuous data replication keeps the RPO at or under 15 minutes, and the pre-provisioned minimal core services can be expanded into a full production environment within the 4-hour RTO. Option A (multi-region deployment with Cloud Load Balancing) is not one of the marked answers, and while it provides very low RTO/RPO, it is an active-active high-availability design rather than a disaster recovery strategy and is typically far more costly than the stated objectives require. Option C (backup and restore: daily backups to Cloud Storage) fails because daily backups give an RPO of up to 24 hours, far exceeding the 15-minute requirement, and restoring from Cloud Storage usually takes longer than 4 hours. Option E (cold standby: periodic snapshots to another region) also fails because periodic snapshots cannot guarantee a 15-minute RPO and rebuilding the environment from scratch typically exceeds the 4-hour RTO.

Answer

Multi-region deployment with Cloud Load Balancing

Answer

Backup and restore: daily backups to Cloud Storage

Answer

Cold standby: periodic snapshots to another region

Question 12

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 designed for exactly this use case — petabyte-scale, low-latency (single-digit ms), high-throughput NoSQL storage for time-series, IoT, and financial data. It scales horizontally by adding nodes. BigQuery is optimised for analytics (seconds-to-minutes latency), Cloud SQL is for OLTP (limited to tens of thousands of QPS), and Firestore is for document data with hierarchical structure.

Answer

Cloud Spanner

Answer

BigQuery

Answer

Firestore

Question 13

A team uses BigQuery for analytics. They notice that queries against a table with billions of rows are slow and expensive. The table is partitioned by ingestion time and has no clustering. Queries frequently filter on a 'customer_id' column. Which optimization would MOST reduce query cost and latency?

Accepted Answer

Cluster the table on customer_id. Clustering on 'customer_id' will physically co-locate rows with the same customer_id within each partition. This allows BigQuery to prune blocks, reducing the amount of data scanned for queries that filter on customer_id. Partitioning alone is not enough; clustering on the filter column is key.

Answer

Use a materialized view pre-aggregated by customer_id

Answer

Switch to on-demand pricing

Answer

Add a clustered index on customer_id

Question 14

A company runs a global e-commerce platform on Google Kubernetes Engine (GKE) with a Cloud SQL for MySQL database. During promotions, traffic spikes cause database connection limits to be exceeded, leading to errors. The application uses the Cloud SQL Auth Proxy. What is the MOST scalable and cost-effective solution?

Accepted Answer

Use a connection pooler such as ProxySQL deployed in the GKE cluster. Connection pooling (e.g., using PgBouncer for PostgreSQL, but for MySQL, similar poolers like ProxySQL or built-in connection pooling) allows the application to reuse connections and handle many concurrent requests without hitting database connection limits. Increasing the database tier or proxy instances adds cost and may not address the underlying issue of connection churn.

Answer

Configure the Cloud SQL Auth Proxy to use private IP and increase pool size

Answer

Upgrade the Cloud SQL instance to a higher tier with more connections

Answer

Increase the number of Cloud SQL Auth Proxy replicas

Question 15

A team wants to automatically move data from Cloud Storage Standard class to Nearline class after 30 days, and to Archive class after 365 days. Which GCP feature should be used?

Accepted Answer

Cloud Storage lifecycle management policies. Cloud Storage lifecycle management policies allow you to define rules for automatic transitions between storage classes based on object age. Option A refers to the specific `gsutil lifecycle set` command used to apply these policies, but the feature itself is called lifecycle management policies (option D). Cloud Storage Transfer Service is for moving data between buckets or from external sources, not for lifecycle transitions. Google Cloud Armor is a security service, unrelated to storage class transitions.

Answer

Cloud Storage Object Lifecycle Management (via gsutil lifecycle set)

Answer

Cloud Storage Transfer Service

Answer

Google Cloud Armor

Question 16

A company wants to analyze the total cost of ownership (TCO) for migrating their on-premises data center to Google Cloud. They need to compare compute, storage, and network costs, including operational expenses like maintenance and power. Which tool should they use?

Accepted Answer

Google Cloud TCO Calculator. Google Cloud's TCO calculator allows you to input on-premises infrastructure details and estimate cloud costs, including compute, storage, network, and operational savings. The Pricing Calculator is for estimating cloud costs but does not compare on-prem vs cloud. Active Assist is for optimization recommendations, not TCO analysis.

Answer

Google Cloud Pricing Calculator

Answer

Cloud Billing reports

Answer

Active Assist cost optimization recommendations

Question 17

A startup is building a real-time multiplayer game with global players. They need a database that provides strong consistency across regions and can handle millions of concurrent users. They anticipate rapid growth and want to avoid downtime for scaling. Which database service meets these requirements?

Accepted Answer

Cloud Spanner. Cloud Spanner is a globally distributed, strongly consistent relational database with automatic horizontal scaling. It provides strong consistency across regions and can handle millions of transactions per second. Firestore is eventually consistent (or strong within a region) and not designed for relational data. Bigtable does not support strong consistency across regions. Cloud SQL is regional with limited scalability.

Answer

Cloud SQL with cross-region replication

Answer

Cloud Bigtable

Answer

Cloud Firestore

Question 18

A company runs a web application behind a Cloud HTTP(S) Load Balancer. Static content (images, CSS, JS) is served from Cloud Storage. They want to reduce latency for users worldwide. Which action is MOST effective?

Accepted Answer

Enable Cloud CDN on the backend bucket. Cloud CDN caches content at global edge locations, reducing latency for users. Enabling Cloud CDN on the backend bucket serves static content from the edge. Using a multi-region bucket provides regional redundancy but does not reduce latency as effectively as CDN. Increasing machine size does not help with static content serving.

Answer

Increase the number of frontend instances

Answer

Use Cloud Armor to block high-latency requests

Answer

Use a multi-regional Cloud Storage bucket

Question 19

A company runs a critical application on Compute Engine with a 1-year commitment for cost savings. They want to also optimize for performance by using machine types with more memory. They plan to update to a different machine series during the commitment term. Which committed use discount type allows this flexibility?

Accepted Answer

Spend-based committed use discount. Spend-based committed use discounts apply to a dollar amount of spend on eligible instance types, allowing flexibility to change machine types as long as the spend commitment is met. Resource-based committed use discounts are tied to specific machine types (e.g., n1-standard-4) and cannot be changed without breaking the commitment.

Answer

Preemptible discount

Answer

Sustained use discount

Answer

Resource-based committed use discount

Question 20

A company is migrating a large on-premises data warehouse to BigQuery. The data includes sensitive customer information that must be encrypted at rest and in transit. They also need to mask credit card numbers for analysts who do not have a need to see the full number. Which TWO Google Cloud services should they use? (Choose 2.)

Accepted Answer

Cloud Data Loss Prevention (DLP). Cloud Data Loss Prevention (DLP) (B) is correct because it can discover and de-identify sensitive data such as credit card numbers using built-in infoTypes and transformation methods like masking, which directly satisfies the requirement to mask card numbers for analysts. Cloud Data Catalog with policy tags (C) is correct because policy tags applied to BigQuery columns enforce column-level access control via fine-grained permissions, ensuring analysts without need-to-know cannot view the full credit card values. Together, DLP handles the detection/masking of sensitive data while policy tags govern who can access specific columns. Cloud IAP (A) is not appropriate because it controls access to web applications and backend services, not data masking in BigQuery. Cloud Audit Logs (D) only records administrative and data access activity; it does not encrypt or mask data. Cloud KMS (E), while relevant for encryption key management, is not one of the two services that specifically address the masking requirement in this scenario.

Answer

Cloud Identity-Aware Proxy (IAP)

Answer

Cloud Audit Logs

Answer

Cloud Key Management Service (KMS)