PCDE Practice Exam 51 — 20 Questions

Question 1

A global gaming company uses Cloud Spanner for player profiles and game state. The schema includes a table 'PlayerStats' with a primary key (PlayerId, GameId, Timestamp). The table stores millions of rows per player. The application frequently runs a query to fetch the most recent stats for a given player across all games, using ORDER BY Timestamp DESC LIMIT 10. This query is slow, taking several seconds. The team adds a secondary index on (PlayerId, Timestamp) but still sees high CPU usage and latency. They need to redesign the schema to optimize this query without changing the application logic significantly. What should they do?

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

Accepted Answer

Change the primary key to (PlayerId, Timestamp, GameId) and drop the secondary index.. The correct answer. By reordering the primary key to (PlayerId, Timestamp, GameId), Spanner can efficiently perform a range scan for a given PlayerId with results sorted by Timestamp because the primary key order determines the storage order. This eliminates the need for a secondary index and reduces CPU usage and latency. Option A is incorrect because migrating to Bigtable is a different database technology and not a schema redesign. Option C is incorrect because stored procedures are not a schema change and may not integrate well with the existing application logic. Option D is incorrect because Spanner does not natively support materialized views.

Answer

Migrate the PlayerStats table to Cloud Bigtable for better time-series performance.

Answer

Create a stored procedure that aggregates data per player and caches results.

Answer

Add a materialized view that pre-computes the latest stats per player.

Question 2

You have a Cloud SQL for MySQL table that stores user logins with columns: user_id, login_time, ip_address. You frequently run queries to count logins by user for a specific date range. Which index would be most efficient?

Accepted Answer

A composite index on (login_time, user_id). A composite index on (login_time, user_id) because the query filters by login_time range and then groups by user_id. The index can be used for both the WHERE clause (range scan on login_time) and then user_id is available for grouping without accessing the table. Option A (no index) would require a full table scan, which is inefficient. Option B (separate indexes) may allow index merge but is less efficient than a single composite index. Option D puts user_id first, which is less efficient for range filtering on login_time because the index would need to scan all user_id values within the range.

Answer

No index; rely on full table scan

Answer

Separate indexes on user_id and login_time

Answer

A composite index on (user_id, login_time)

Question 3

A multinational corporation uses Cloud Spanner with a multi-region configuration. The schema includes a table that is updated frequently by users in two distant regions. They are experiencing high commit latencies due to distributed transactions. Which schema change would most reduce latency?

Accepted Answer

Use a table-level leader placement configuration to keep the table's splits in a single region.. Table-level leader placement ensures that all writes for the table are processed in a single region, minimizing cross-region coordination and reducing commit latency. Option A is wrong: reducing replicas can hurt availability but doesn't address transaction distribution. Option C is wrong: interleaving tables does not affect region placement. Option D is wrong: more granular primary keys increase splits, which can exacerbate distributed transactions and latency.

Answer

Reduce the number of replicas in the Spanner configuration.

Answer

Convert the table into an interleaved child of a parent table.

Answer

Increase the number of splits by using a more granular primary key.

Question 4

A Cloud Bigtable instance stores time-series data with a row key format: [metric_id]#[timestamp]. The team notices read throughput is low when scanning a metric over a time range. What is the likely cause?

Accepted Answer

All rows for a given metric are stored in a single tablet causing a hotspot.. The row key format [metric_id]#[timestamp] causes all rows for the same metric_id to share the same lexicographic prefix. Cloud Bigtable stores rows in sorted order by row key, so all rows for a given metric are co-located in a single tablet. When scanning a time range for that metric, all read requests hit the same tablet, creating a hotspot that limits throughput to the capacity of a single node.

Answer

Too many column families in the schema.

Answer

The number of nodes is insufficient.

Answer

Replication factor is set too low.

Question 5

A company is designing a Cloud Spanner database for a global user base. They need to support strong consistency and low-latency reads across multiple regions. Which schema design practice is most important?

Accepted Answer

Use interleaved tables to co-locate related rows that are queried together.. Interleaved tables in Cloud Spanner physically co-locate parent and child rows on the same split, enabling local joins with strong consistency and low latency across regions. This design minimizes cross-node communication, which is critical for global workloads that require both strong consistency and fast reads.

Answer

Denormalize data into wide tables to reduce the number of joins.

Answer

Use a single table with composite primary key to avoid joins.

Answer

Create secondary indexes on every column to optimize read queries.

Question 6

A company is running a Cloud SQL for PostgreSQL instance for an e-commerce application. They need to enable point-in-time recovery (PITR) with a 7-day retention period. What configuration steps must be taken?

Accepted Answer

Enable WAL archiving and configure backup retention to 7 days using gcloud sql instances patch --backup-retention-days 7.. Cloud SQL for PostgreSQL uses Write-Ahead Log (WAL) archiving to enable point-in-time recovery (PITR). You must enable automated backups (which automatically enables WAL archiving) and then set the backup retention period to 7 days using the `gcloud sql instances patch --backup-retention-days 7` command. This ensures that WAL logs are retained for the specified duration, allowing PITR within that window.

Answer

Create a cross-region backup replica with a 7-day retention.

Answer

Enable binary logging and set backup retention to 7 days.

Answer

Enable automated backups with a 7-day retention; WAL archiving is automatic.

Question 7

A Cloud Bigtable instance is experiencing high read latency due to hot spots. The operations team uses the Key Visualizer tool and identifies that a small set of row keys are being read disproportionately. Which action should they take to mitigate the hot spotting?

Accepted Answer

Redesign the row key scheme to distribute the workload across more tablets.. Hot spotting in Cloud Bigtable is typically caused by a suboptimal row key design that concentrates read/write traffic on a small number of tablets. Redesigning the row key scheme to distribute the workload—for example, by adding a hash prefix or using a more granular key—spreads requests across multiple tablets, alleviating the hotspot. Key Visualizer explicitly identifies the skewed row keys, guiding the redesign effort.

Answer

Increase the number of nodes in the Bigtable cluster to handle the load.

Answer

Change the storage type from HDD to SSD to improve performance.

Answer

Enable replication across multiple zones.

Question 8

An application uses Firestore in Native mode. The query filters on two fields: 'status' (string) and 'created_date' (timestamp). The query returns results but the billing shows high document reads. What is the most likely cause?

Accepted Answer

The query is using an inequality filter on 'created_date' which requires a composite index.. In Firestore Native mode, queries that apply an inequality filter (e.g., >=, >, <, !=) on a field automatically require a composite index on both the equality filter field and the inequality filter field to avoid a full collection scan. Without that composite index, Firestore performs a back-end scan of all documents matching the equality filter, then applies the inequality filter in memory, resulting in high document reads. Option A correctly identifies that the inequality filter on 'created_date' is the most likely cause of the excessive reads because it forces Firestore to read and discard many documents that do not satisfy the timestamp condition.

Answer

The query is using 'array-contains' which always scans the entire collection.

Answer

The 'status' field is not indexed because single-field indexes are not automatic.

Answer

The query is missing an ORDER BY clause causing a full scan.

Question 9

An SRE team wants to define an SLI for service availability. Which metric correctly represents the availability SLI?

Accepted Answer

Total requests that succeed / Total requests. Availability as an SLI is measured as the proportion of successful requests to total requests. Option A correctly defines this as 'Total requests that succeed / Total requests'. Option B is incorrect because it measures latency (response time within 200 ms), not availability. Option C is a measure of uptime, not service availability from the user's perspective. Option D only counts errors, which is not a ratio.

Answer

Total requests that complete within 200 ms / Total requests

Answer

Total minutes the service is up / Total minutes in the window

Answer

Number of requests that return a 5xx status code

Question 10

A news website uses Cloud SQL for MySQL for content management. They experience slow reads during breaking news events. They have a single primary instance in us-east1. They need to improve read scalability globally. They also want to ensure data is backed up in another region. What should they do?

Accepted Answer

Add cross-region read replicas in multiple regions and use replica read for queries.. Adding cross-region read replicas in multiple regions allows the website to offload read queries to replicas located closer to global users, reducing latency during traffic spikes. Cloud SQL for MySQL supports cross-region replicas, which also provide a backup copy of data in another region for disaster recovery, meeting both scalability and backup requirements without changing the database engine.

Answer

Use Cloud Spanner with multi-region configuration.

Answer

Enable automatic failover to a standby instance in another region.

Answer

Use Bigtable for content storage.

Question 11

An organization is implementing GitOps using Config Sync. They have a Git repository containing Kubernetes manifests for multiple GKE clusters. They want to ensure that only authorized engineers can modify the configuration and that changes are automatically applied to clusters. What is the recommended way to secure Config Sync?

Accepted Answer

Store the Git repository in Cloud Source Repositories and use IAM to control access. Use branch protection rules to require pull request approvals before merging to the main branch.. Config Sync uses a Git repo. The best practice is to use a private repo and grant least-privilege access. To prevent unauthorized changes, enforce branch protection rules (e.g., require PR approvals) and use a service account with read-only access to the repo. Config Sync's reconciler runs in the cluster with read-only permissions.

Answer

Use a public Git repository and rely on Kubernetes RBAC to control who can modify Config Sync objects.

Answer

Use a separate Git repository for each cluster and restrict access via SSH keys.

Answer

Encrypt the Kubernetes secrets in the repo and use a KMS key to decrypt them in the cluster.

Question 12

An organization is moving to a GitOps model for managing both application deployments and GCP infrastructure. They want to use a single tool to sync Kubernetes manifests and manage GCP resources like Cloud SQL and Pub/Sub. Which two Google Cloud services should they combine?

Accepted Answer

Config Sync. Config Sync (D) is correct because it continuously reconciles the state of Kubernetes resources in a cluster with manifests stored in a Git repository, enabling a GitOps workflow for application deployments. Config Connector (E) is correct because it allows managing GCP resources (e.g., Cloud SQL, Pub/Sub) declaratively using Kubernetes custom resource definitions (CRDs), which can also be synced via Config Sync. Together, they provide a single Git-based toolchain for both Kubernetes and GCP infrastructure.

Answer

Deployment Manager

Answer

Cloud Build

Answer

Cloud Deploy

Question 13

You are configuring error budget burn rate alerts for an SLO with a 30-day window. The SLO target is 99.9%. You want to set up a fast burn rate alert to quickly detect high consumption. Which alerting policy configuration should you use?

Accepted Answer

Burn rate threshold: 14, lookback window: 1 hour. A burn rate of 14 with a 1-hour lookback is the standard configuration for a fast burn rate alert in Google Cloud SRE practices. It detects when the error budget is being consumed significantly faster than allowed, providing rapid alerting. Option A uses a 6-hour window (delaying the alert), option C uses burn rate 5 (slow burn rate, exhaustion in ~6 days), and option D uses both wrong settings.

Answer

Burn rate threshold: 14, lookback window: 6 hours

Answer

Burn rate threshold: 5, lookback window: 1 hour

Answer

Burn rate threshold: 5, lookback window: 6 hours

Question 14

Which two practices are characteristic of a blameless postmortem? (Choose TWO.)

Accepted Answer

Creating action items with owners and deadlines. Blameless postmortems focus on systemic causes and create action items to prevent recurrence. They avoid blaming individuals.

Answer

Assigning punitive measures to prevent recurrence

Answer

Identifying the employee who made the error

Answer

Keeping the postmortem confidential within the incident response team

Question 15

An SRE team uses Cloud Monitoring to alert on error budget burn rate. They configure a slow burn alert with a 6-hour lookback window and a burn rate factor of 5. What is the purpose of this slow burn alert?

Accepted Answer

To detect a gradual increase in error rate that could exhaust budget over time. Slow burn alerts detect sustained, slower consumption of error budget that could still exhaust the budget before the SLO period ends. They give early warning for gradual degradation.

Answer

To trigger when error budget is completely exhausted

Answer

To detect rapid spikes in error rate that could exhaust budget quickly

Answer

To calculate the remaining error budget

Question 16

An SRE team wants to define an SLO for a microservice that processes HTTP requests. They need an SLI that measures the proportion of requests that are answered within 200ms with a non-5xx status code. Which type of SLI should they use?

Accepted Answer

Request-based SLI counting good requests divided by total requests. A latency-based SLI with a threshold (200ms) combined with availability (non-5xx) is a request-based SLI measuring good requests (fast+successful) over total requests.

Answer

Availability-based SLI using a rolling window of 30 days

Answer

Window-based SLI counting good minutes

Answer

Throughput-based SLI measuring requests per second

Question 17

A service has an SLO of 99.9% availability over a 30-day rolling window. The team wants to use Cloud Monitoring to create a request-based SLO. Which configuration is correct?

Accepted Answer

Use a request-based metric: good request count / valid request count. For a request-based SLO, you define two metrics: good request count and valid request count. The SLO is the ratio of good to valid requests.

Answer

Use an error budget metric: total allowed errors over 30 days

Answer

Use a latency metric: proportion of requests under threshold

Answer

Use a window-based metric: good minutes / total minutes

Question 18

A team notices that a critical microservice often fails when the downstream database is slow. They want to test the service's resilience by injecting latency into the database dependency. Which GCP tool should they use?

Accepted Answer

Traffic Director with HTTP fault filter. Chaos Engineering practices on GCP can be implemented using Traffic Director's fault injection or Chaos Mesh. Traffic Director's HTTP fault filter can inject latency into traffic.

Answer

Cloud Load Balancing logging

Answer

VPC Flow Logs

Answer

Cloud Armor

Question 19

A company has an SLA of 99.95% availability for its service. The SRE team defines an SLO of 99.99% availability. The error budget is calculated as 0.01% over a 30-day window. How much downtime is allowed per month according to the error budget?

Accepted Answer

4.32 minutes. Error budget = 100% - SLO. For 99.99% SLO, error budget = 0.01%. Over 30 days (720 hours), 0.01% of 720 hours = 0.072 hours = 4.32 minutes. Alternatively, compute: 30 days * 24 * 60 minutes = 43200 minutes; 0.01% of that = 4.32 minutes.

Answer

432 minutes

Answer

0.432 minutes

Answer

43.2 minutes

Question 20

An SRE team wants to automate a repetitive manual task that involves moving files from Cloud Storage to BigQuery and then deleting the source files. Which GCP service is BEST suited for this toil reduction?

Accepted Answer

Cloud Functions. Cloud Functions is ideal for event-driven automation. It can trigger on Cloud Storage events, process data, and delete files.

Answer

Cloud Build

Answer

Cloud Scheduler

Answer

Compute Engine