PCD Practice Questions

Question 1

Refer to the exhibit. A team is using Cloud Monitoring with MQL to alert on CPU utilization per zone. They notice that the alert fires even when no single instance in a zone has CPU>80%, because the average across instances in the zone exceeds 80%. What change should they make to the MQL query to alert only when any individual instance exceeds 80%?

Accepted Answer

Change the group_by to group_by [instance_id] and remove zone grouping.. The current MQL query uses `group_by [zone]` to compute the mean CPU utilization per zone, which averages all instances in a zone together. By changing the grouping to `group_by [instance_id]` and removing the zone grouping, the alert will evaluate each instance individually, firing only when a single instance's CPU exceeds 80%, rather than when the zone-wide average exceeds the threshold.

Answer

Remove the group_by and use a filter instead.

Answer

Add a filter for each instance individually.

Answer

Use a ratio instead of mean.

Question 2

A company is deploying a three-node Bigtable cluster for production. They anticipate growth in read throughput and want to plan for scaling. Which two actions can they take to increase read throughput? (Choose TWO.)

Accepted Answer

Add more nodes to the existing cluster. Option A is correct because adding more nodes to a Bigtable cluster increases the cluster's compute capacity, and Bigtable distributes tablet serving across nodes, so additional nodes directly raise the aggregate read throughput available to the cluster. Option E is correct because adding a second cluster in a different zone (a multi-cluster routing setup) lets the application serve reads from multiple clusters, increasing overall read throughput and resilience; with multi-cluster routing, reads can be served from the nearest or all clusters depending on the app profile. Option B is wrong because a development instance is a single-node, non-production, non-HA instance with much lower performance and cannot be scaled for production read throughput. Option C is wrong because HDD storage in Bigtable is cheaper but has higher latency and lower throughput than SSD, so it would not increase read throughput. Option D is wrong because Key Visualizer is a monitoring/diagnostic tool that visualizes access patterns and row-key hot spots; it does not itself increase performance, though it can inform schema or key design changes.

Answer

Switch to a development instance type

Answer

Change storage to HDD

Answer

Use Key Visualizer to increase performance

Question 3

A database administrator is using Liquibase for schema versioning during a migration to Cloud SQL. What is the primary benefit of using Liquibase?

Accepted Answer

Version control and rollback of schema changes. Liquibase provides version control of database schema changes, allowing rollbacks and tracking in a declarative manner (changelogs).

Answer

Real-time replication of data

Answer

Automated performance tuning

Answer

Automatic data type conversion

Question 4

A developer deploys a containerized application on Cloud Run. The application needs to access a Cloud SQL instance securely without exposing it to the internet. What is the best practice?

Accepted Answer

Use Cloud Run's VPC connector and configure private IP for Cloud SQL.. Cloud Run services cannot directly connect to Cloud SQL using private IP without a VPC connector. The VPC connector allows Cloud Run to send traffic to a VPC network, where the Cloud SQL instance with a private IP resides, ensuring traffic never traverses the public internet. This is the recommended pattern for secure, low-latency access to Cloud SQL from serverless environments.

Answer

Whitelist the Cloud Run IP range in Cloud SQL authorized networks.

Answer

Use a Cloud SQL proxy sidecar container in the same pod.

Answer

Use Cloud NAT to route traffic.

Question 5

You are designing a CI/CD pipeline using Cloud Build. You want to automatically trigger a build when code is pushed to a specific branch in Cloud Source Repositories. What is the correct configuration?

Accepted Answer

Create a build trigger in Cloud Build with a regex pattern for the branch name.. Cloud Build natively supports build triggers that can be configured to automatically start a build when code is pushed to a specific branch in Cloud Source Repositories. The trigger uses a regex pattern to match the branch name, and Cloud Build listens for repository push events directly without requiring additional services.

Answer

Create a Cloud Function that invokes Cloud Build via API when a push event occurs.

Answer

Add a build step in cloudbuild.yaml that polls the repository.

Answer

Configure a Cloud Pub/Sub topic to notify Cloud Build on push events.

Question 6

An engineer needs to create a Cloud SQL instance with SSD storage. Which storage type should they select for best performance?

Accepted Answer

SSD (solid-state drive). SSD (solid-state drive) is the recommended storage type for Cloud SQL instances when best performance is required. SSD provides lower latency and higher IOPS compared to HDD, making it suitable for transactional workloads and real-time applications. Cloud SQL supports both SSD and HDD, but SSD is the default and optimal choice for production databases.

Answer

HDD (hard disk drive)

Answer

Local SSD

Answer

Persistent disk standard

Question 7

A team notices that a Cloud Run service occasionally returns HTTP 500 errors. They have enabled Cloud Error Reporting. What is the best way to rapidly diagnose the root cause of these errors?

Accepted Answer

Examine the error group details in Cloud Error Reporting.. Cloud Error Reporting automatically groups similar errors (like HTTP 500s) into error groups, providing stack traces, request details, and occurrence timelines. Examining the error group details is the fastest path to root cause because it surfaces the exact exception and context without requiring manual log filtering or metric setup.

Answer

Search Cloud Logging manually for '500' events.

Answer

View the Cloud Trace dashboard for error traces.

Answer

Create a Cloud Monitoring dashboard for 5xx metrics.

Question 8

A team is investigating increased latency in a web application deployed on Google Kubernetes Engine (GKE). They want to identify which specific service calls are slow. Which Google Cloud tool should they use?

Accepted Answer

Cloud Trace. Cloud Trace is the correct tool because it provides end-to-end latency tracking for requests in distributed systems, including GKE. It captures detailed spans for each service call, allowing the team to pinpoint which specific microservice or API call is causing the increased latency. This aligns directly with the need to identify slow service calls in a web application.

Answer

Cloud Monitoring dashboards

Answer

Cloud Profiler

Answer

Cloud Logging

Question 9

A DevOps engineer is designing a row key for a Bigtable table storing user activity logs. The pattern is: user_id (UUID) + timestamp. On a cluster with 10 nodes, they observe severe hotspotting on a single node during peak writes. Which row key design change would likely resolve this issue?

Accepted Answer

Use a hash of the user_id as the prefix. The correct approach to mitigate hotspotting in Bigtable. Using a hash of the user_id as the row key prefix ensures uniform distribution of writes across tablets because the hash function randomizes the prefix, eliminating sequential patterns. Option D (timestamp + user_id) can still cause hotspotting if timestamps are strictly monotonically increasing, as all writes would target the same tablet at any given moment. While timestamps from multiple distributed sources may vary, this design is less robust than hashing and not a guaranteed solution. Therefore, only Option A reliably resolves the hotspotting issue.

Answer

Use a single table with no row key change

Answer

Increase the number of nodes to 20

Answer

Use a composite key: timestamp + user_id

Question 10

A company is running a critical application on Google Kubernetes Engine (GKE) that stores state in a Cloud SQL PostgreSQL instance. The application's latency-sensitive frontend needs to read data from Cloud SQL with minimal latency. The team wants to reduce read latency and offload read traffic from the primary database. What should they do?

Accepted Answer

Create a read replica of the Cloud SQL instance and direct read traffic to the replica.. Creating a read replica of the Cloud SQL PostgreSQL instance allows read-heavy, latency-sensitive traffic to be offloaded from the primary database. The replica handles SELECT queries independently, reducing load on the primary and lowering read latency for the frontend, as replicas are typically in the same region and can serve data with minimal additional delay.

Answer

Migrate the database to Cloud Spanner for better read scalability.

Answer

Use Memorystore for Redis as a cache layer between the application and Cloud SQL.

Answer

Use Cloud CDN to cache database responses.

Question 11

A company is using Cloud Monitoring to track custom metrics published from an on-premises application using the Monitoring API. The metrics are published every 30 seconds. The team wants to create an alert that fires if the metric goes below a threshold for more than 1 minute. Which alert condition type should they use?

Accepted Answer

Metric type: custom.googleapis.com, condition: metric threshold (below value), duration: 60s. The requirement is to fire an alert when the metric value goes below a threshold for more than 1 minute. The 'metric threshold (below value)' condition type directly monitors a metric against a lower bound and evaluates the condition over the specified duration (60s) before triggering. This aligns with the need to detect sustained low values, not just a single data point.

Answer

Metric type: custom.googleapis.com, condition: metric lower bound, duration: 60s

Answer

Metric type: custom.googleapis.com, condition: metric change, duration: 60s

Answer

Metric type: custom.googleapis.com, condition: metric absence, duration: 60s

Question 12

A Cloud Spanner query that uses a secondary index is taking longer than expected. The index includes a large payload column. How can the query performance be improved?

Accepted Answer

Add the payload column to the index using the STORING clause. In Cloud Spanner, a secondary index that includes a large payload column can cause the query to do a back-join to the base table to fetch that column, adding latency. Adding the payload column to the index using the STORING clause makes the index covering, so the query can be satisfied entirely from the index without touching the base table. This eliminates the back-join and improves performance.

Answer

Use a composite key instead of an index

Answer

Change the index to a global index

Answer

Remove the index and use a full table scan

Question 13

Your Cloud Run service is experiencing 5xx errors. You have enabled Cloud Logging and Cloud Error Reporting. How can you quickly identify the most common error type?

Accepted Answer

Open Cloud Error Reporting to see grouped error counts.. Cloud Error Reporting automatically groups similar errors (e.g., same stack trace or error message) and shows a count for each group, making it the fastest way to identify the most common 5xx error type without manual log inspection. It is purpose-built for this exact use case, aggregating errors from Cloud Logging and presenting them in a dashboard sorted by frequency.

Answer

Use Cloud Trace to analyze the traces of failing requests.

Answer

View the logs in Cloud Logging and manually count error messages.

Answer

Create a Cloud Monitoring alert on 5xx response codes.

Question 14

An engineer is deploying AlloyDB for PostgreSQL and needs to support read scaling with automatic scaling of read replicas based on load. They also want to ensure minimal operational overhead. What should they use?

Accepted Answer

Create a read pool instance with autoscaling enabled.. AlloyDB read pool instances are designed specifically for horizontal read scaling. When autoscaling is enabled, AlloyDB automatically adds or removes read replicas in the pool based on CPU utilization and other load metrics, eliminating manual capacity management. This directly satisfies both the read-scaling and minimal-operational-overhead requirements.

Answer

Manually create additional read replicas and set up a load balancer.

Answer

Create multiple cross-region replicas.

Answer

Use AlloyDB Omni to deploy replicas on-premises.

Question 15

A company runs a Java microservice on GKE that processes financial transactions. The service is critical and must meet a 99.9% availability SLO. They have set up Cloud Monitoring alerting policies based on request latency and error rate. Recently, the team noticed that the alerting policy for high latency fires too frequently with false positives, causing alert fatigue. They want to reduce false positives without compromising real issues. The latency metric is collected from the application's custom metric via Prometheus. Which approach should they take?

Accepted Answer

Increase the alert duration to require sustained latency over a longer period.. Increasing the alert duration requires the high latency to be sustained over a longer period, which filters out transient spikes that cause false positives. This approach preserves the ability to detect genuine, prolonged performance degradation that could impact the 99.9% availability SLO, without raising the threshold and risking missed real issues.

Answer

Change the metric to use median instead of average.

Answer

Increase the alert threshold to a higher latency value.

Answer

Disable the alert and rely on manual checks.

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