PCDE Practice Test 31 — 15 Questions

Question 1

A global application uses Cloud Trace to collect distributed traces. The current sampling rate of 100% is causing high costs and storage usage. The team wants to reduce sampling while still capturing representative data. Which THREE strategies should they consider?

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

Accepted Answer

Set a probabilistic sampling rate (e.g., 10%) in the OpenTelemetry SDK. Option A is correct because setting a probabilistic sampling rate (e.g., 10%) in the OpenTelemetry SDK directly reduces the volume of spans exported to Cloud Trace while still producing a statistically representative sample of traffic, which lowers cost and storage usage. Option C is correct because rate limiting based on request path lets the team sample only critical endpoints, preserving visibility into the most important transactions while dropping high-volume, low-value traffic. Option D is correct because tail-based sampling in the OpenTelemetry Collector buffers complete traces and retains those with errors or high latency, giving representative and diagnostically useful data without paying to store every successful trace. Option B is not appropriate because disabling automatic instrumentation and relying only on manual Cloud Trace API calls reduces coverage and increases engineering effort rather than providing a controlled sampling strategy. Option E is wrong because increasing sampling to 100% is the opposite of the goal and would worsen the cost and storage problem.

Answer

Disable automatic instrumentation and only use manual instrumentation with Cloud Trace API

Answer

Increase the sampling rate to 100% to ensure no traces are missed

Question 2

Your company runs an e-commerce platform on Google Cloud. The platform uses Cloud SQL for MySQL to store product inventory. The inventory table has the following schema: CREATE TABLE inventory (product_id INT PRIMARY KEY, quantity INT, last_updated TIMESTAMP) ENGINE=InnoDB. The application performs frequent updates on quantity for a subset of popular products. Recently, you have noticed increased deadlock errors during peak hours. The application uses REPEATABLE READ isolation level. You suspect that the schema design is contributing to locking contention. After analyzing the workload, you find that the updates often involve incrementing or decrementing quantity by small amounts and are mostly on the same set of popular products. What would be the best course of action to reduce deadlocks without compromising data integrity?

Accepted Answer

Rewrite the update query to use atomic operations (e.g., UPDATE inventory SET quantity = quantity - ? WHERE product_id = ?) without pre-fetching the current value.. The application's pattern of reading the current quantity before updating (e.g., SELECT quantity FROM inventory WHERE product_id = ?, then UPDATE inventory SET quantity = ? WHERE product_id = ?) causes gap locks and deadlocks under REPEATABLE READ. By using an atomic UPDATE (UPDATE inventory SET quantity = quantity - ? WHERE product_id = ?), the database performs the update without a prior read, reducing lock contention. This maintains data integrity because the subtraction is atomic and accurate. Option B is wrong because MyISAM does not support transactions or row-level locking, which compromises data integrity. Option C is wrong because partitioning does not reduce locking contention on the same rows; it only improves data management. Option D is wrong because reducing isolation to READ COMMITTED may reduce some locking but introduces non-repeatable reads and does not address the fundamental read-before-write pattern that causes deadlocks.

Answer

Change the engine to MyISAM to avoid row-level locking.

Answer

Partition the inventory table by product_id range to spread the load.

Answer

Reduce the isolation level to READ COMMITTED to reduce locking.

Question 3

An organization needs to define RTO and RPO for their database disaster recovery plan. They have a Cloud SQL for SQL Server instance with HA enabled. What are the typical RTO and RPO for an automatic HA failover?

Accepted Answer

RPO: near zero, RTO: under 60 seconds. Cloud SQL for SQL Server with HA enabled uses synchronous replication to a standby in a different zone, so RPO is near zero (no committed data loss) and RTO is typically under 60 seconds during an automatic failover. This is the standard HA behavior for regional Cloud SQL instances.

Answer

RPO: replication lag (seconds), RTO: minutes

Answer

RPO: zero, RTO: zero

Answer

RPO: minutes, RTO: hours

Question 4

A startup is building a mobile app that requires a highly available, globally distributed, low-latency NoSQL database. The data model is key-value with occasional queries on a secondary field. Which database service should they choose?

Accepted Answer

Cloud Firestore. Cloud Firestore is a fully managed, globally distributed NoSQL document database that provides strong consistency, automatic multi-region replication, and low-latency queries on key-value pairs as well as secondary fields (via composite indexes). It is ideal for mobile apps requiring high availability and real-time data synchronization across the globe, directly matching the requirements of a key-value model with occasional secondary queries.

Answer

Cloud SQL (PostgreSQL)

Answer

Cloud Spanner

Answer

Cloud Bigtable

Question 5

A company is designing a BigQuery data warehouse for BI dashboards. They have a fact table with billions of rows and need to optimize query performance for common filters on date and customer_id. Which table design strategy is most effective?

Accepted Answer

Use a partitioned table on date with clustering on customer_id.. Partitioning the table on `date` allows BigQuery to prune entire partitions when filtering by date, drastically reducing the data scanned. Clustering on `customer_id` then sorts data within each partition, enabling block-level pruning for queries that filter on `customer_id`. This combination minimizes both I/O and cost for the described BI workload.

Answer

Use a clustered table on date only.

Answer

Use a non-partitioned table with indexing on customer_id.

Answer

Use a materialized view that aggregates by date.

Question 6

A financial services firm needs a database for real-time fraud detection that requires single-digit millisecond latency for lookups on a precomputed feature set. The dataset is 5 TB and grows by 1 TB per month. Which database service BEST meets these requirements?

Accepted Answer

Cloud Bigtable. Cloud Bigtable is a fully managed, high-performance NoSQL database designed for low-latency, high-throughput workloads, making it ideal for real-time fraud detection with precomputed features. It scales linearly to handle large datasets (5 TB growing by 1 TB/month) and provides single-digit millisecond latency for key-based lookups. BigQuery is an analytics data warehouse optimized for OLAP, not low-latency point reads; Cloud Spanner is a globally distributed relational database with higher latency; Cloud SQL is a relational database not suited for massive scale or low-latency NoSQL access patterns.

Answer

BigQuery

Answer

Cloud Spanner

Answer

Cloud SQL (MySQL)

Question 7

An organization wants to implement a landing zone with shared VPC, centralized logging, and security projects. Which folder structure best follows Google Cloud's recommended landing zone design?

Accepted Answer

Create a 'common' folder for shared projects (shared VPC, logging, security) and environment folders (dev, staging, prod) for workload projects.. Google Cloud's recommended landing zone design places shared infrastructure projects (shared VPC host, centralized logging, security, networking) in a dedicated 'common' folder, while workload projects are organized into environment folders such as dev, staging, and prod. This separation enforces least-privilege IAM at the folder level and prevents environment-specific policies from accidentally affecting shared services.

Answer

Create a folder per product with subfolders for environments. Place shared projects under the product folder.

Answer

Create one folder per team with subfolders for each environment. Place shared projects under the corresponding environment folder.

Answer

Create a flat structure with all projects at the organization level.

Question 8

A developer needs to push a Docker image to Artifact Registry from their local machine. They have installed the gcloud CLI. Which command should they run first to authenticate Docker with Artifact Registry?

Accepted Answer

gcloud auth configure-docker. The gcloud auth configure-docker command is the required first step: it writes a Docker credential helper configuration into ~/.docker/config.json so that the Docker CLI automatically uses gcloud credentials when pushing to or pulling from Artifact Registry (or Container Registry) hosts. Without this, Docker has no credentials for the registry and the push will fail with an authentication error.

Answer

gcloud container clusters get-credentials

Answer

gcloud auth login

Answer

docker login -u oauth2accesstoken -p "$(gcloud auth print-access-token)" https://LOCATION-docker.pkg.dev

Question 9

A company wants to implement granular cost tracking for their cloud resources. They need to attribute costs to specific teams and environments. Which approach should they use?

Accepted Answer

Apply labels to resources, such as 'team:engineering' and 'environment:prod', and use billing export to BigQuery for cost analysis.. Labels in Google Cloud are key-value pairs that can be attached to resources and are propagated to billing data, enabling granular cost breakdowns by dimensions like team and environment. By exporting billing data to BigQuery, you can run custom queries to attribute costs precisely to teams and environments. This approach is flexible, scalable, and doesn't require restructuring your project hierarchy or billing accounts.

Answer

Use separate projects for each team and environment, and analyze costs per project.

Answer

Use separate billing accounts for each team.

Answer

Enable cost attribution in Cloud Monitoring.

Question 10

An SRE team notices that a routine database cleanup task takes 30 minutes of manual effort each week. The task does not add enduring value and scales linearly with the number of databases. How should the team classify this work?

Accepted Answer

Toil.. Toil is defined in SRE practice as work that is manual, repetitive, automatable, tactical, devoid of enduring value, and scales linearly with service growth. The weekly database cleanup task matches every criterion: it's manual, takes 30 minutes, adds no lasting value, and grows with the number of databases. That makes 'toil' the precise classification, and the SRE team should target it for automation.

Answer

Technical debt.

Answer

Operational overhead.

Answer

Engineering project.

Question 11

You are managing a Cloud SQL for MySQL instance that is experiencing high replication lag. The instance uses semi-synchronous replication. Which THREE actions could reduce the replication lag?

Accepted Answer

Change replication mode from semi-synchronous to asynchronous.. Switching from semi-synchronous to asynchronous replication removes the requirement for the primary to wait for at least one replica to acknowledge receipt of each transaction. In semi-synchronous mode, the primary waits for acknowledgment, which can introduce latency and contribute to replication lag under high write loads. Asynchronous replication allows the primary to commit transactions immediately without waiting, reducing the time the primary spends waiting for replica acknowledgment and thus lowering replication lag.

Answer

Reduce the binlog retention period on the primary.

Answer

Enable binary log compression on the primary.

Question 12

Your GKE cluster runs a stateful application that requires persistent storage. You want to use Vertical Pod Autoscaler (VPA) to optimize resource requests, but you notice that pods are being terminated and recreated when VPA updates resource recommendations. You want to avoid pod restarts. Which VPA updateMode should you use?

Accepted Answer

updateMode: Initial. VPA updateMode 'Initial' applies resource recommendations only when pods are first created, and does not update running pods afterward. This means existing pods are not terminated and recreated when VPA recalculates recommendations, which directly satisfies the requirement to avoid pod restarts.

Answer

updateMode: Recreate

Answer

updateMode: Off

Answer

updateMode: Auto

Question 13

A BI analyst needs to calculate a running total of sales by region over time in BigQuery. Which SQL window function should be used?

Accepted Answer

SUM(sales) OVER (PARTITION BY region ORDER BY date). The SUM() window function with an ORDER BY clause in the OVER() clause computes a running total (cumulative sum) over the specified partition. In BigQuery, when you include ORDER BY inside a window function's OVER() clause, the default window frame is RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW, which produces the running total for each region ordered by date.

Answer

RANK() OVER (PARTITION BY region ORDER BY date)

Answer

ROW_NUMBER() OVER (PARTITION BY region ORDER BY date)

Answer

COUNT(sales) OVER (PARTITION BY region ORDER BY date)

Question 14

A company runs a Cloud SQL for PostgreSQL instance that is nearing its storage limit of 500 GB. The database administrator needs to increase storage capacity online without any downtime. What should they do?

Accepted Answer

Use the gcloud sql instances patch command with --storage-size flag. The `gcloud sql instances patch` command with the `--storage-size` flag allows you to increase the storage capacity of a Cloud SQL for PostgreSQL instance online without any downtime. Cloud SQL supports live storage increases by automatically resizing the underlying persistent disk while the instance remains available, so no restart or maintenance window is required.

Answer

Enable automatic storage increase and wait for it to trigger

Answer

Delete and recreate the instance with a larger disk

Answer

Shut down the instance, increase the disk size, and restart

Question 15

A data analyst is running a BigQuery query that joins multiple tables to generate a BI report. The query is slow and uses many LEFT JOINs. What is the best approach to improve performance without changing the business logic?

Accepted Answer

Denormalize the data using nested repeated fields to avoid joins. Denormalizing data using nested repeated fields in BigQuery reduces the number of JOIN operations, which are expensive in a distributed, columnar storage system. By storing related data in a single table with REPEATED fields, the query avoids shuffling large datasets across slots, directly improving performance while preserving the original business logic.

Answer

Add indexes on the join columns

Answer

Replace LEFT JOINs with INNER JOINs where possible

Answer

Increase the number of BigQuery slots