PCD Practice Test 32 — 15 Questions

Question 1

You are migrating a MySQL application to Cloud SQL. The application uses a read-heavy workload with occasional writes. You need to offload read traffic to replicas and ensure high availability. Which THREE steps should you take? (Choose 3)

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

Accepted Answer

Create read replicas in the same region. Creating an HA instance provides automatic failover. Adding read replicas offloads read traffic. Enabling the proxy for connection pooling ensures efficient connections.

Answer

Use MySQL native replication instead of Cloud SQL replication

Answer

Enable cross-region replication for the primary

Question 2

A Cloud Spanner instance is configured with 1000 processing units. The workload has unpredictable traffic spikes. To ensure consistent performance, the team wants to automatically adjust capacity based on a high-priority CPU target of 60%. What should they configure?

Accepted Answer

Set up Cloud Spanner autoscaling with min=500, max=2000 processing units and high-priority CPU target=0.6.. Cloud Spanner’s autoscaler can automatically adjust processing units based on CPU utilization. Setting min and max processing units with a high-priority CPU target enables autoscaling. Node count is older, processing units are granular. Compute capacity is not the same as autoscaling. Bigtable autoscaler is for Bigtable.

Answer

Configure compute capacity with a fixed number of nodes.

Answer

Enable Bigtable autoscaling for the Spanner instance.

Answer

Manually adjust nodes when CPU exceeds 60%.

Question 3

A developer deploying a new version of a microservice sees a sudden increase in error logs in Cloud Logging. The errors are 500 responses from the service. What is the most efficient way to investigate the root cause?

Accepted Answer

Use Cloud Trace to view the trace of failed requests. Cloud Trace provides end-to-end latency data and can capture detailed spans for individual requests, including those that resulted in 500 errors. By filtering traces to failed requests, you can pinpoint the exact service or function call that caused the error, making it the most efficient root-cause investigation method without requiring code changes or additional instrumentation.

Answer

Revert to the previous version immediately

Answer

Check the CPU and memory metrics in Cloud Monitoring

Answer

Analyze the error logs using Log Analytics and create a log-based metric

Question 4

Which TWO of the following are valid ways to export Cloud Logging logs to BigQuery?

Accepted Answer

Use a Dataflow pipeline to stream logs from Pub/Sub to BigQuery. You can use a Dataflow pipeline to read Cloud Logging logs from a Pub/Sub topic (where logs are routed via a log sink) and stream them into BigQuery for real-time analysis. This is a common pattern for custom log processing and transformation before loading into BigQuery. Option C is correct because Cloud Logging allows you to create a log sink directly with a destination of a BigQuery dataset, which automatically exports logs in near real-time without additional infrastructure.

Answer

Use the Logging API to write logs directly to BigQuery

Answer

Use the BigQuery Data Transfer Service for Cloud Logging

Answer

Use Cloud Monitoring to send logs to BigQuery

Question 5

A company runs a global e-commerce site on Cloud Spanner. They notice that some queries on the Orders table by customer_id are slow because customer_id is not part of the primary key (order_id). They want to avoid full table scans. Which TWO actions will improve query performance? (Choose two)

Accepted Answer

Create a secondary index on customer_id. A secondary index on customer_id will speed up queries. The STORING clause can include additional columns to avoid index joins. A global index is needed because customer_id is not in the primary key. Interleaving would require a specific parent-child relationship.

Answer

Increase the number of nodes to improve scan speed

Answer

Re-create the table with customer_id as the first part of the primary key

Answer

Use interleaved tables to store customer data with orders

Question 6

A company wants to query data residing in Amazon S3 using BigQuery without copying the data. They also need to join this data with other tables in BigQuery. Which BigQuery feature should they use?

Accepted Answer

BigQuery Omni. BigQuery Omni allows querying data across clouds (including AWS S3) using BigQuery SQL. It supports federated queries of data in S3 without movement. BigQuery federated queries (standard) only work with GCP sources.

Answer

BigQuery federated queries (external tables on S3)

Answer

Cloud Storage transfer service to copy S3 data to GCS, then query

Answer

BigQuery Data Transfer Service for S3

Question 7

Which THREE metrics are commonly used to create a Service Level Indicator (SLI) for availability of an HTTP-based service?

Accepted Answer

Uptime check success rate. Uptime check success rate directly measures whether the service is reachable and responding, typically via periodic HTTP probes (e.g., GET /health). A successful response (e.g., HTTP 200) indicates availability, while failures (timeouts, connection errors) indicate unavailability. This is a standard SLI for availability in HTTP-based services.

Answer

CPU utilization

Answer

Request count

Question 8

Which Google Cloud database service is designed as a fully managed, in-memory data store for caching, session management, and pub/sub messaging?

Accepted Answer

Memorystore. Memorystore provides managed Redis and Memcached for caching, session storage, and pub/sub. It is the correct service for these use cases.

Answer

Cloud SQL

Answer

Firestore

Answer

Bigtable

Question 9

A company uses Cloud SQL for PostgreSQL with HA configuration. During a regional outage, the standby fails over to the primary. What is the expected recovery point objective (RPO) and recovery time objective (RTO)?

Accepted Answer

RPO = 0, RTO < 60 seconds. Cloud SQL HA provides synchronous replication to a standby in a different zone within the same region, achieving 0 RPO. Automatic failover typically completes within 60 seconds, so RTO is less than 60 seconds.

Answer

RPO = 5 minutes, RTO = 10 minutes

Answer

RPO = 0, RTO = 5 minutes

Answer

RPO = 1 minute, RTO = 5 minutes

Question 10

You are configuring Cloud Bigtable for a financial market data application. Write throughput is critical, and row keys are based on stock symbols. You notice significant write latency on a few nodes. What is the most effective way to improve write distribution?

Accepted Answer

Prepend a hashed prefix to the row key (salted keys). Salted keys (prepending a hash prefix) distribute writes across nodes. Reversed domain keys help with read patterns but not write distribution. Column families and GC policies do not affect write distribution.

Answer

Separate frequently accessed columns into a different column family

Answer

Increase the garbage collection (GC) grace period

Answer

Use reversed domain keys (e.g., com.example.stock)

Question 11

You are managing a microservices application deployed on Google Kubernetes Engine (GKE) that uses Cloud Monitoring and Cloud Logging. Recently, users have reported intermittent slow response times, especially during peak hours. You have enabled the Ops Agent on GKE nodes and configured custom metrics for your services. The application consists of a frontend service, a backend API service, and a database service. The frontend calls the backend, which in turn queries the database. You notice that when the response time spikes, the frontend service's CPU utilization remains low, but the backend service's CPU utilization increases. The database service shows normal latency and no errors. You have examined the logs and found no application errors. The GKE cluster has three node pools: one for each service, with autoscaling enabled. The backend service is configured with a HorizontalPodAutoscaler (HPA) based on CPU utilization, but the HPA does not seem to scale up quickly enough during traffic spikes. You want to identify the root cause of the performance degradation. Which course of action should you take first?

Accepted Answer

Analyze the backend service's request latency distribution using Cloud Monitoring metrics to identify whether the issue is due to increased request volume or slow request processing.. The intermittent slow response times during peak hours, combined with low frontend CPU but high backend CPU and normal database latency, strongly suggest the backend service is struggling to process requests quickly under load. Analyzing the backend's request latency distribution using Cloud Monitoring metrics (e.g., 99th percentile latency) will reveal whether the issue stems from increased request volume (which would show a shift in latency distribution) or from individual requests taking longer to process (e.g., due to inefficient code or resource contention). This diagnostic step directly addresses the symptom without making assumptions about scaling or network issues.

Answer

Check the network latency between the frontend and backend services using Cloud Monitoring's network metrics.

Answer

Configure the backend service's HPA to use custom metrics based on request latency instead of CPU utilization.

Answer

Increase the minimum number of replicas for the backend service to handle peak traffic.

Question 12

Which Google Cloud database is a serverless data warehouse designed for analytical queries on petabyte-scale data, with SQL interface and built-in machine learning capabilities?

Accepted Answer

BigQuery. BigQuery is the serverless, highly scalable data warehouse for analytics. It supports SQL and BigQuery ML for in-database ML.

Answer

AlloyDB

Answer

Cloud Bigtable

Answer

Cloud Spanner

Question 13

A company is deploying a microservices architecture on Google Kubernetes Engine (GKE). They need to monitor inter-service latency and error rates. Which set of Google Cloud services should they use to collect and visualize these metrics?

Accepted Answer

Cloud Monitoring, Cloud Logging, and Cloud Trace. Cloud Monitoring collects metrics like latency and error rates, Cloud Logging aggregates logs for deeper analysis, and Cloud Trace provides distributed tracing to track requests across microservices. Together, they enable end-to-end observability of inter-service performance in GKE, with Cloud Monitoring visualizing the data via dashboards and alerts.

Answer

Cloud Trace, Cloud Debugger, and Cloud Profiler

Answer

Cloud Logging, Cloud Run, and Cloud Build

Answer

Cloud Monitoring, Cloud Functions, and Cloud Pub/Sub

Question 14

A startup is building a mobile application with real-time synchronization across user devices. They expect millions of users and need a NoSQL database with offline support and real-time listeners. Which database should they choose?

Accepted Answer

Firestore. Firestore is a flexible, scalable NoSQL database for mobile, web, and server development. It offers real-time synchronization, offline data persistence, and integrates with Firebase and Google Cloud. It is designed for this exact use case.

Answer

Cloud Bigtable

Answer

BigQuery

Answer

Cloud SQL

Question 15

You need to create a custom dashboard in Cloud Monitoring that shows the number of 500 errors from your application, along with the average latency. What is the correct way to create this?

Accepted Answer

Use the Cloud Monitoring Metrics Explorer to add two charts from different metric types.. Cloud Monitoring's Metrics Explorer allows you to add multiple charts from different metric types (e.g., a custom log-based metric for 500 error count and a built-in metric for average latency) within a single custom dashboard. This approach keeps all relevant data in one view without needing separate dashboards or switching between services.

Answer

Create two separate dashboards and export to a single view.

Answer

Use Cloud Logging's metrics explorer.

Answer

Use Log Analytics to query logs and chart the error count, then use Cloud Monitoring metrics for latency.