PCDE Practice Test 34 — 15 Questions

Question 1

A financial services company is designing a Cloud Spanner schema for a trading system. They have two main entities: 'accounts' and 'transactions'. Each account has many transactions, and queries almost always retrieve transactions for a specific account. Which TWO schema design strategies should they employ?

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

Accepted Answer

Ensure the primary key of transactions includes the account_id as the first part.. Option B is correct because making account_id the first part of the transactions primary key co-locates and sorts all of an account's transactions together, so queries filtering by account_id become efficient primary-key range scans rather than full-table scans. Option E is correct because interleaving transactions as a child of accounts physically stores each account's transactions with the parent row, giving locality and enabling efficient joins/reads for account-specific transaction queries. Option A is not needed since a secondary index on transactions.account_id is redundant when account_id already leads the primary key, and it would add write amplification. Option C is wrong because foreign keys in Spanner enforce referential integrity but do not improve query performance or data locality. Option D is wrong because storing transactions as a JSON array inside the account row is an anti-pattern that limits transaction size, prevents efficient per-transaction updates, and breaks Spanner's relational model.

Answer

Use a secondary index on transactions.account_id.

Answer

Define a foreign key constraint from transactions to accounts.

Answer

Store transactions as a JSON array of repeating fields within the account record.

Question 2

A company is designing a Cloud Bigtable schema for time-series data. The data is written by millions of devices every second. The query patterns are: (1) retrieve the most recent reading for a specific device, (2) retrieve all readings for a device in a time range. Which TWO row key design techniques should the team use to optimize for these patterns? (Choose two.)

Accepted Answer

Use field promotion: put device_id as the first component of the row key. Option B is correct because field promotion places the high-cardinality device_id as the leading row key component, which co-locates all readings for a given device in a contiguous key range so that both the latest-reading lookup and the time-range scan for a specific device can be served by a single efficient range scan. Option C is correct because using a reverse timestamp (MAX_TIMESTAMP - timestamp) as the second component orders each device's readings so the most recent reading sorts first, letting the 'most recent reading' query be satisfied by reading just the first row of that device's prefix rather than scanning to the end of the range. Option A is not appropriate here because salting/hash prefixes would scatter a single device's readings across many tablets, breaking the contiguous range needed for the per-device time-range query. Option D is wrong because a monotonically increasing timestamp as the first component causes sequential write hotspots and does not group data by device. Option E is wrong because column family layout does not address row key ordering or query access patterns.

Answer

Use salting (hash prefix) to distribute writes

Answer

Use a monotonically increasing timestamp as the first part of the row key

Answer

Store the entire row as a single column family to reduce overhead

Question 3

Which tool is best for identifying hot spots in a Cloud Spanner database?

Accepted Answer

Key Visualizer. Key Visualizer is the correct tool because it is specifically designed to visualize access patterns in Cloud Spanner and identify hot spots—keys or ranges that receive a disproportionate share of reads or writes. Unlike generic monitoring tools, Key Visualizer provides a heatmap of key-space activity, enabling you to pinpoint and mitigate performance bottlenecks caused by uneven distribution of workload across splits.

Answer

Query Insights

Answer

Cloud Trace

Answer

Cloud Monitoring

Question 4

You are using Cloud Memorystore for Redis as a caching layer. You notice that cache hit ratio is below 50%. What is the best action to improve it?

Accepted Answer

Increase the TTL (time-to-live) for cached data.. A low cache hit ratio indicates that a large proportion of requests are not finding their data in the cache, forcing the application to fetch from the primary database. Increasing the TTL (time-to-live) for cached data keeps valid entries in Redis longer, reducing the frequency of evictions and cache misses. This directly improves the hit ratio by ensuring that more requests can be served from the cache before the data expires.

Answer

Flush the cache periodically to remove stale data.

Answer

Enable persistence to avoid data loss.

Answer

Increase the instance memory size.

Question 5

A company is using Cloud Run for a service that performs background computation even when there are no incoming requests. They find that the service is being throttled and not completing the background work. What is the most likely cause and solution?

Accepted Answer

The service must have CPU always-on enabled.. Cloud Run throttles CPU when a container instance is not handling a request, which prevents background work from completing. Enabling CPU always-on ensures the container receives CPU even outside of request processing, allowing background computation to finish. This is the correct fix for background tasks that run without incoming requests.

Answer

The service needs more memory; increase memory limit.

Answer

The service needs to set min instances to 1.

Answer

The service should be migrated to GKE.

Question 6

An engineer needs to migrate a PostgreSQL database to Cloud SQL. They have used pg_dump to create a dump file. Which flags should they use to avoid issues with ownership and ACLs, since Cloud SQL does not support those?

Accepted Answer

--no-owner --no-acl. --no-owner and --no-acl prevent pg_dump from including ownership and ACL commands, which are not supported on Cloud SQL. --no-privileges is not a standard flag (use --no-acl). --clean drops objects before recreating, not recommended.

Answer

--no-privileges --no-owner

Answer

--no-owner --clean

Answer

--no-acl --if-exists

Question 7

You are designing a monitoring strategy for a GKE cluster. You need to collect application metrics, traces, and logs in a vendor-neutral way, and export them to Google Cloud. Which TWO components should you use? (Choose two.)

Accepted Answer

OpenTelemetry SDK. OpenTelemetry SDK (D) is correct because it is the vendor-neutral instrumentation library that generates application metrics, traces, and logs in a standardized format, allowing you to instrument your code once and export telemetry to Google Cloud without vendor lock-in. OpenTelemetry Collector (E) is correct because it is the vendor-neutral agent/gateway that receives, processes, and exports that telemetry to Google Cloud backends such as Cloud Monitoring, Cloud Trace, and Cloud Logging, fulfilling the requirement to collect and export all three signal types in a vendor-neutral way. Together, the SDK and Collector form the standard OpenTelemetry pipeline for GKE workloads. The Cloud Logging agent (A) is not correct because it only ships logs to Cloud Logging and is not vendor-neutral for metrics and traces. The Stackdriver Monitoring agent (B) is not correct because it collects system/host metrics only and is tied to Google's monitoring stack, not a vendor-neutral telemetry pipeline. OpenCensus agent (C) is not correct because OpenCensus is the legacy, deprecated predecessor to OpenTelemetry and does not cover logs as required.

Answer

Cloud Logging agent

Answer

Stackdriver Monitoring agent

Answer

OpenCensus agent

Question 8

You want to automatically group and track similar errors in your application logs, and see trends over time. Which Google Cloud service should you use?

Accepted Answer

Error Reporting. Google Cloud Error Reporting automatically aggregates and groups similar errors from application logs, then tracks their frequency and trends over time. It is purpose-built for error grouping, counting, and alerting, making it the right choice for the stated requirement.

Answer

Cloud Monitoring

Answer

Cloud Logging

Answer

Cloud Trace

Question 9

A company uses Firestore in Native mode. They have a collection with 1 million documents and frequently run queries that filter on two fields: status and createdAt. The queries are slow. What should the team do?

Accepted Answer

Create a composite index on (status, createdAt). Firestore creates single-field indexes automatically but for multi-field queries, a composite index must be created manually. Without it, queries may be slow or fail.

Answer

Create an index exemption for the status field

Answer

Use the Datastore mode instead

Answer

Add an index exemption for the createdAt field

Question 10

An application running on Google Kubernetes Engine (GKE) emits structured logs in JSON format. The DevOps team wants to count the number of log entries that contain a specific error code (e.g., 'error_code': 500) in the last hour and use that count to trigger an alert if it exceeds a threshold. What is the most efficient way to achieve this?

Accepted Answer

Create a log-based counter metric in Cloud Logging with the filter jsonPayload.error_code=500, then set up an alerting policy on that metric.. Creating a log-based counter metric in Cloud Logging with the filter jsonPayload.error_code=500 allows you to count occurrences efficiently, and then you can set an alerting policy on that metric in Cloud Monitoring. This is the native, serverless way to achieve the goal without external orchestration.

Answer

Use Cloud Logging's Logs Explorer to run a query every minute and use Cloud Scheduler to trigger a Cloud Function that checks the count.

Answer

Export logs to BigQuery and run a scheduled query to count error codes, then use the result to trigger an alert via Cloud Monitoring.

Answer

Configure a metric threshold alert directly on the log entries in Cloud Monitoring without creating a metric.

Question 11

An organization runs a Cloud SQL for MySQL instance for its e-commerce platform. During a load test, they notice the CPU utilization consistently exceeds 80% and queries are slowing down. The instance is using 2 vCPUs. The team needs to improve performance with minimal downtime. What should they do?

Accepted Answer

Increase the number of vCPUs and memory via vertical scaling. Vertical scaling (increasing vCPUs and memory) directly addresses CPU utilization exceeding 80% by providing more compute resources to handle the load. Cloud SQL supports vertical scaling with minimal downtime, typically a brief restart, making it the most straightforward solution for a CPU-bound workload. This approach is recommended when the instance is consistently resource-constrained.

Answer

Create a cross-region read replica

Answer

Enable automatic storage increase

Answer

Shard the database using application-level sharding

Question 12

A BI developer needs to write a query that calculates total sales by month for the current year. They create a Common Table Expression (CTE) to define monthly aggregates, then reference it in a final SELECT. What is the main benefit of using a CTE over a subquery in this scenario?

Accepted Answer

CTEs enhance query readability and maintainability.. CTEs improve query readability and maintainability by allowing you to define a named temporary result set once and reference it multiple times in the final SELECT. In this scenario, the CTE clearly separates the monthly aggregation logic from the final output, making the query easier to understand and modify compared to nesting subqueries.

Answer

CTEs are always faster than subqueries.

Answer

CTEs reduce the amount of memory used by the query.

Answer

CTEs automatically cache results for subsequent queries.

Question 13

A company is migrating a PostgreSQL database to Cloud SQL using DMS. The source database has logical replication enabled for the migration. What is the purpose of logical replication in this context?

Accepted Answer

To capture changes for continuous replication after the initial dump.. Logical replication in a DMS migration is used to capture ongoing changes (INSERT/UPDATE/DELETE) from the source PostgreSQL database after the initial dump, so that the destination Cloud SQL instance stays synchronized until cutover. It enables continuous replication and minimizes downtime during the migration.

Answer

To enable point-in-time recovery on the source.

Answer

To improve performance of the initial dump.

Answer

To convert data types between source and destination.

Question 14

You have a Cloud Monitoring alerting policy that triggers when CPU usage exceeds 80% for 5 minutes. You want to add documentation to the alert that includes a link to a runbook and labels indicating the affected service. How can you achieve this?

Accepted Answer

Edit the alerting policy and add documentation with the runbook URL and labels under the 'Documentation' section.. Cloud Monitoring alerting policies have a built-in 'Documentation' section where you can add free-form Markdown content, including runbook URLs, and apply user-defined labels to the policy itself. Editing the alerting policy and populating the Documentation field is the native, supported way to attach runbook links and service labels so they appear in every notification. This keeps the documentation tied to the policy rather than to a notification channel or external store.

Answer

Create a separate Cloud Storage bucket and reference it in the alert.

Answer

Use Cloud Logging to store runbook URLs and attach them to alerts via log-based metrics.

Answer

Use Cloud Monitoring's notification channel to include a link in the email body.

Question 15

A data engineer needs to run complex analytical queries on terabytes of data with sub-second query latency. The data is stored in Google Cloud Storage and updated daily. Which database service should they use?

Accepted Answer

BigQuery. BigQuery is a serverless, highly scalable data warehouse designed for analytical queries on large datasets, offering sub-second query latency on terabytes of data. It integrates seamlessly with Google Cloud Storage, allowing daily updates and complex SQL queries. This makes it the ideal choice for the described requirements.

Answer

Cloud Bigtable

Answer

Cloud SQL

Answer

Cloud Spanner