PCDE Practice Exam 65 — 20 Questions

Question 1

An SRE team uses Cloud Monitoring SLOs with an error budget policy. They want to receive an alert when the error budget is consumed at a fast burn rate, using a 1-hour lookback window. The SLO is 99.9% over 30 days. The error budget is 43.2 minutes. What burn rate threshold should be used?

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

Accepted Answer

14. In Google SRE practice, a fast burn alert typically uses a 1-hour lookback window and a burn rate threshold of 14 for a 99.9% SLO over 30 days. The burn rate threshold indicates how many times faster the error budget is being consumed compared to the allowed rate. With a burn rate of 14, the error budget would be exhausted in approximately 30 days / 14 ≈ 2.14 days. The 1-hour reference in the question pertains to the alert's lookback window, not the total time to exhaust the budget. A burn rate of 720 would exhaust the budget in exactly 1 hour, but that is not a standard alerting threshold and would be too sensitive. Therefore, 14 is the correct threshold for a fast burn alert in this context.

Answer

5

Answer

720

Answer

1

Question 2

A team uses GitOps with Config Sync to manage multiple GKE clusters. They want to ensure that if a configuration change is applied to the git repository, it is automatically synced to all clusters within minutes. What must be configured?

Accepted Answer

Configure Config Sync with a webhook from Cloud Source Repositories or GitHub to trigger re-sync. Config Sync relies on a polling mechanism by default, which can introduce delays. Configuring a webhook from Cloud Source Repositories or GitHub triggers an immediate re-sync when changes are pushed to the repository, ensuring that configurations are applied to all GKE clusters within minutes without waiting for the next polling interval.

Answer

Use Anthos Service Mesh to distribute configurations

Answer

Set the `syncWait` parameter in the Config Sync operator to 1 second

Answer

Set up a Cloud Build trigger that runs `gcloud container clusters update` on each cluster

Question 3

A company runs a BigQuery data warehouse. They notice that query performance has degraded over time. The data is loaded daily from Cloud Storage using batch loads. Which action is most likely to improve query performance?

Accepted Answer

Partition and cluster tables based on common query filters.. Partitioning and clustering tables based on common query filters directly reduces the amount of data scanned per query by organizing data into physical segments. In BigQuery, this allows the query engine to prune entire partitions and clusters, significantly lowering I/O and improving performance without additional cost or complexity.

Answer

Increase the number of slots in the reservation.

Answer

Create materialized views for all frequent queries.

Answer

Migrate the data to Cloud SQL for better performance.

Question 4

A retail company stores sales transactions in BigQuery. They want to create a materialized view that aggregates daily sales by product category, but they need the view to refresh automatically within 5 minutes of new data being inserted. The source table is partitioned by transaction_date and has a streaming buffer. What should they do to ensure the materialized view refreshes quickly enough?

Accepted Answer

Set the materialized view's max_staleness interval to 5 minutes and allow relaxed consistency.. Setting the `max_staleness` interval on the materialized view to 5 minutes allows BigQuery to serve query results from the view even if the underlying base table's streaming buffer hasn't fully committed, as long as the data is within the staleness window. This enables the materialized view to reflect near-real-time data without waiting for the streaming buffer to fully materialize, meeting the 5-minute refresh requirement.

Answer

Set max_staleness on the base table to 5 minutes.

Answer

Disable streaming inserts and use batch loads only.

Answer

Increase the streaming buffer size on the base table.

Question 5

A Cloud Spanner database is experiencing high latency for point reads. The table has a primary key of (CustomerID, OrderDate). Most reads are by CustomerID only. What should the engineer do?

Accepted Answer

Add a secondary index on CustomerID.. Point reads by CustomerID only are inefficient on the primary key (CustomerID, OrderDate) because Cloud Spanner requires the full primary key for direct lookup. Adding a secondary index on CustomerID allows Spanner to perform an index scan followed by a point read, drastically reducing latency for these queries.

Answer

Use interleaved tables.

Answer

Reorder primary key to (OrderDate, CustomerID).

Answer

Increase the number of nodes.

Question 6

A Cloud Memorystore for Redis instance needs to ensure data is not lost during a maintenance event. The team decides to enable persistence. However, Memorystore does not support Redis persistence. What is the recommended alternative?

Accepted Answer

Use the Standard tier with replication and take regular snapshots to Cloud Storage using a scheduled script.. Cloud Memorystore for Redis does not support native Redis persistence (RDB or AOF). To prevent data loss during maintenance events, the recommended alternative is to use the Standard tier (which provides replication and automatic failover) and supplement it with scheduled snapshots exported to Cloud Storage. This approach ensures a durable backup outside the Redis instance that can be restored if needed.

Answer

Migrate to Cloud SQL for MySQL and use its backup feature.

Answer

Enable AOF persistence in Memorystore.

Answer

Increase the maxmemory setting and rely on the allkeys-lru eviction policy.

Question 7

You have a Cloud Spanner database that needs to be migrated from one region to another. You want to ensure no data loss and minimal downtime. Which approach should you use?

Accepted Answer

Create a backup of the database and restore it to a new instance in the target region.. Cloud Spanner does not support in-place region changes or read replicas in different regions. The only supported method to migrate a Cloud Spanner database between regions with no data loss and minimal downtime is to create a backup of the database and restore it to a new instance in the target region. This approach ensures a consistent snapshot of the data and allows you to plan the cutover window to minimize downtime.

Answer

Use gcloud spanner instances move to change the region of the existing instance.

Answer

Create a read replica in the target region and promote it after replication catches up.

Answer

Use gcloud command 'gcloud spanner databases export' to export the database to CSV files, then import into the new instance.

Question 8

A data engineer is designing a schema for Cloud Spanner to store a hierarchy of customers and their orders. Customers have many orders, and queries often retrieve orders for a specific customer. To optimize performance and reduce cross-node reads, which schema design pattern should the engineer use?

Accepted Answer

Use a parent-child interleaved table structure where Orders are interleaved in Customers.. Spanner's interleaved tables allow storing child rows (orders) physically co-located with their parent row (customer) using the same primary key prefix. This enables efficient joins and reduces cross-node reads. A secondary index with STORING is useful for other access patterns but not for hierarchical queries. Denormalization is not recommended in Spanner.

Answer

Denormalize by embedding order details as a repeated field within the customer row.

Answer

Normalize customers and orders into separate tables without any relationship.

Answer

Create a secondary index with STORING on the order ID.

Question 9

A DevOps team is adopting trunk-based development for their Terraform configurations. They want to ensure that all changes are tested before being applied to production. Which THREE practices should they implement?

Accepted Answer

Require manual approval before applying changes from main to production.. Trunk-based development involves short-lived branches merged frequently to main. Running `terraform plan` in a CI pipeline on feature branches provides early feedback. Running `terraform apply` from the main branch only after review ensures production changes are controlled. Running `terraform validate` ensures syntax correctness.

Answer

Create long-lived feature branches to develop infrastructure changes.

Answer

Use a single environment (production) only to reduce complexity.

Question 10

A financial services company uses Cloud Spanner for transaction processing. They notice increased latency during peak hours. They suspect a hot spot. What is the best way to diagnose the issue?

Accepted Answer

Review Key Visualizer to identify hot keys. Key Visualizer is a Cloud Spanner tool that provides a heatmap of access patterns across keys and time ranges, directly revealing hot spots (e.g., monotonically increasing keys or skewed read/write distributions). This allows you to identify the specific keys causing contention without guesswork, making it the most targeted diagnostic approach for hot spot issues.

Answer

Add more nodes to the instance

Answer

Switch to Cloud SQL

Answer

Check Cloud Spanner's CPU utilization metrics

Question 11

You are designing a Spanner schema for a global inventory system. The table Products has primary key (ProductId STRING). The table Inventory has primary key (ProductId STRING, WarehouseId INT64) and is interleaved in Products. You expect high write throughput on Inventory. Which TWO design choices will help avoid hotspots?

Accepted Answer

Use UUID for ProductId. To avoid hotspots in Spanner, avoid monotonically increasing keys. ProductId should be a UUID or hash. Also, using a hash prefix on ProductId as the first part of the key can help distribute writes. Interleaving is fine. Secondary indexes with STORING are for reads. Reverse timestamps are for Bigtable. The correct choices are using UUID and adding a hash prefix.

Answer

Reverse the timestamp in the row key

Answer

Use a monotonically increasing sequence for ProductId

Answer

Create a secondary index on WarehouseId with STORING

Question 12

Your company runs a global e-commerce platform on Google Cloud Spanner. The database schema includes an 'Orders' table with primary key (OrderId, CustomerId) and an 'OrderItems' table with primary key (OrderId, CustomerId, ItemId), interleaved in parent Orders on delete cascade. During peak shopping hours, you notice that queries retrieving all items for a specific order are performing full table scans on the OrderItems table, leading to increased latency and higher CPU utilization. The queries use the OrderId as the filter condition. The database administrators have already checked that the query plans show table scans instead of using the interleaved index. You are tasked with resolving this performance issue. Which of the following actions should you take?

Accepted Answer

Remove CustomerId from the Orders primary key (making it just OrderId) and update OrderItems to have primary key (OrderId, ItemId), maintaining interleaving.. The interleaved index in Cloud Spanner requires that the parent table's primary key columns be a prefix of the child table's primary key. With the original schema, queries filtering only on OrderId cannot use the interleaved index because CustomerId is missing from the filter, forcing a full table scan. By removing CustomerId from the primary key of Orders and OrderItems, OrderId becomes the leading column, allowing the interleaved index to be used for efficient point lookups.

Answer

Change the primary key of Orders to (OrderId, CustomerId) and update OrderItems accordingly.

Answer

Create a secondary index on OrderItems(OrderId).

Answer

Increase the number of Spanner nodes to improve throughput.

Question 13

A Cloud SQL for PostgreSQL database experiences lock contention during heavy concurrent writes on a single table. Which schema design change can most effectively reduce contention?

Accepted Answer

Partition the table by a key that spreads write load. Partitioning the table by a key that spreads write load (e.g., a hash of the user ID or timestamp) reduces lock contention because each partition is a separate physical storage unit with its own lock manager. Concurrent writes targeting different partitions can proceed in parallel without blocking each other, directly addressing the contention on a single table.

Answer

Deploy read replicas to offload reads

Answer

Use a connection pooler like PgBouncer

Answer

Create materialized views for read queries

Question 14

A company uses Cloud Deploy to manage deployments to multiple GKE clusters across environments. They want to implement a canary deployment strategy where 10% of traffic goes to the new version initially, and after manual approval, the remaining 90% rolls out. What should be included in the delivery pipeline configuration?

Accepted Answer

Define a 'canary' strategy with a first phase of 10% and a second phase of 90% with an approval gate. Cloud Deploy's 'canary' strategy allows defining multiple phases with incremental traffic percentages, and an approval gate can be inserted between phases to require manual sign-off before proceeding. This directly matches the requirement: a first phase sending 10% traffic to the new version, then a manual approval, followed by a second phase rolling out the remaining 90%.

Answer

Configure a 'canary' strategy with a single phase of 10% and rely on external traffic management

Answer

Set the rollout strategy to 'standard' and use skaffold.yaml for traffic splitting

Answer

Use a 'blueGreen' strategy with a 10% preview phase

Question 15

An e-commerce platform uses Cloud Bigtable for real-time analytics on customer behavior. The table uses a row key of 'customer_id#timestamp' (customer ID followed by reverse timestamp). Queries for a specific customer's recent events are fast, but queries that filter by event type (e.g., 'purchase') across many customers are slow. What schema change can improve query performance for event-type filtering?

Accepted Answer

Change the row key to 'event_type#customer_id#timestamp'.. Cloud Bigtable's performance depends heavily on row key design for efficient scans. By changing the row key to 'event_type#customer_id#timestamp', queries filtering by event type can use a single row key prefix scan, which is fast and avoids full table scans. This leverages Bigtable's lexicographic ordering to group all events of the same type together, making event-type filtering a range scan rather than a filter across unrelated rows.

Answer

Create a separate column family for each event type.

Answer

Add a secondary index on the event_type column.

Answer

Use a separate Bigtable instance for each event type.

Question 16

You are deploying a containerized application to Cloud Run using gcloud run deploy. You need to ensure that revisions are tagged with a custom identifier for testing purposes, without allocating any traffic to them. Which flag should you use?

Accepted Answer

--tag=test. The `--tag` flag in `gcloud run deploy` assigns a custom tag (e.g., `test`) to a specific revision, allowing it to be accessed via a dedicated URL without receiving any live traffic. This is the correct way to create a tagged revision for testing or staging purposes while keeping the primary revision serving user requests.

Answer

--no-traffic

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--traffic=test=0

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--revision-suffix=test

Question 17

You manage a Cloud SQL for PostgreSQL instance that handles OLTP workloads. Users in a different region report slow query response times. You notice that the database CPU utilization is below 30%, but network latency is high. What is the most cost-effective solution to reduce query latency without migrating the database?

Accepted Answer

Create cross-region read replicas and route read queries to the nearest replica.. The issue is high network latency for users in a different region, not local resource contention. Creating cross-region read replicas allows read queries to be served from a replica closer to the users, reducing network round-trip time without migrating the database. This is the most cost-effective solution as it avoids expensive instance upgrades or a full migration to Cloud Spanner.

Answer

Add more memory to the instance to increase cache hit ratio.

Answer

Increase the instance's vCPUs to handle more concurrent connections.

Answer

Migrate the database to Cloud Spanner using a live migration service.

Question 18

A company is designing a BigQuery data warehouse for sales analytics. They want to minimize query costs when aggregating daily sales by region and product. Which two methods are effective? (Select TWO).

Accepted Answer

Creating a materialized view with GROUP BY region, product, day. A materialized view in BigQuery pre-computes and stores the results of the GROUP BY query on region, product, and day. When the underlying data changes, the materialized view is incrementally refreshed, so queries that match the view's aggregation are served directly from the stored results, avoiding full table scans and reducing query costs (bytes processed). This is ideal for recurring aggregation patterns like daily sales summaries.

Answer

Using a view that queries the raw data with WHERE clause

Answer

Storing pre-aggregated results in a separate table and updating nightly

Answer

Creating indexes on the raw table

Question 19

An engineer is configuring a cloudbuild.yaml file. They want two build steps (unit tests and linting) to run simultaneously, and after both complete, a third step (package) should run. How should they configure waitFor in the package step?

Accepted Answer

Set waitFor: ['-'] on the unit tests and linting steps, and waitFor: ['unit-tests', 'linting'] on the package step.. In Cloud Build, steps run sequentially by default. To run unit tests and linting in parallel, you set `waitFor: ['-']` on each of those steps, which tells Cloud Build they have no dependencies and can start immediately. Then, setting `waitFor: ['unit-tests', 'linting']` on the package step ensures it only runs after both parallel steps have completed successfully.

Answer

Do not set waitFor on any step.

Answer

Set waitFor: ['-'] on the package step.

Answer

Set waitFor: ['unit-tests', 'linting'] on all three steps.

Question 20

A startup is building a mobile app with a relational database backend. They expect moderate traffic and need strong consistency, automatic backups, and point-in-time recovery. Which two Google Cloud database services meet these requirements? (Choose TWO.)

Accepted Answer

Cloud Spanner. Cloud Spanner is correct because it provides strong consistency across globally distributed data using TrueTime and synchronous replication, along with automatic backups and point-in-time recovery. Cloud SQL is correct because it offers fully managed relational databases (MySQL, PostgreSQL, SQL Server) with automated backups and point-in-time recovery, meeting the need for strong consistency and moderate traffic.

Answer

Firestore

Answer

Cloud Bigtable

Answer

BigQuery