PCDE Practice Test 2 — 10 Questions

Question 1

A team is adopting GitOps for infrastructure. They want to ensure that all Terraform configuration changes are automatically applied after merging to the main branch. Which CI/CD approach best supports this?

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

Accepted Answer

Use a CI/CD pipeline that runs `terraform apply` after a merge to the main branch.. GitOps requires that the desired state in the main branch be the single source of truth, and that merges to main automatically trigger reconciliation. A CI/CD pipeline that runs `terraform apply` after a merge to main implements this declarative, automated flow. This is the canonical GitOps pattern for Terraform.

Answer

Run `terraform plan` on every commit and require manual approval before apply.

Answer

Have developers apply changes locally using `terraform apply`.

Answer

Use Cloud Deployment Manager with a trigger on Cloud Source Repository commits.

Question 2

A team uses Helm charts to deploy applications to GKE. They need to manage environment-specific configurations (e.g., dev, staging, prod) using a single chart. Which tool should they use?

Accepted Answer

Helm with values files. Helm with values files is the standard way to manage environment-specific configurations with a single chart. The chart contains templated Kubernetes manifests, and separate values files (values-dev.yaml, values-staging.yaml, values-prod.yaml) override defaults per environment, allowing one chart to deploy consistently across dev, staging, and prod.

Answer

Kustomize

Answer

Skaffold

Answer

Config Connector

Question 3

Which THREE components are required to compute a 7-day moving average of daily sales using a window function? (Choose three.)

Accepted Answer

AVG() function. The AVG() function is the aggregate function that computes the arithmetic mean of the sales values over the specified window frame. In a moving average calculation, AVG() is applied to the rows defined by the window frame to produce the average for each row.

Answer

PARTITION BY product

Answer

WINDOW clause

Question 4

Your company runs a critical application on Google Kubernetes Engine (GKE) with a StatefulSet using persistent volumes backed by Compute Engine persistent disks. The application performs frequent small random writes to a MySQL database stored on the persistent disks. You notice that the disk write latency has increased significantly, and the application's throughput has dropped. Monitoring shows that the disk queue depth is consistently high. The current disk type is pd-standard. What is the most cost-effective way to reduce write latency and improve throughput?

Accepted Answer

Change the persistent disk type from pd-standard to pd-ssd.. The application is experiencing high write latency due to insufficient IOPS from pd-standard disks, which are HDD-based and optimized for sequential reads, not small random writes. Changing to pd-ssd (SSD-based) provides significantly higher IOPS and lower latency for random write workloads, directly addressing the high queue depth and throughput drop. This is the most cost-effective solution because pd-ssd offers the necessary performance improvement without requiring architectural changes or over-provisioning capacity.

Answer

Use a regional persistent disk for higher availability and performance.

Answer

Add more replicas of the StatefulSet to distribute writes across multiple disks.

Answer

Increase the size of the persistent disks to improve IOPS limits.

Question 5

An engineer is migrating a workload from a relational database to Bigtable. The current schema has a Customers table (1M rows) and an Orders table (100M rows) with a foreign key. Queries often fetch all orders for a customer. What is the best row key design for the Bigtable orders table?

Accepted Answer

Use customer ID + order ID as the row key (e.g., cust123#ord456).. Using customer ID + order ID as the row key ensures that all orders for a single customer are stored in contiguous rows, enabling efficient range scans. Bigtable orders rows lexicographically by row key, so a prefix scan on the customer ID retrieves all related orders in a single read operation, avoiding expensive joins or scatter-gather patterns.

Answer

Use the order ID as the row key and store customer ID as a column.

Answer

Use a hash of the customer ID as the row key.

Answer

Use a random UUID as the row key.

Question 6

An organization is migrating an Oracle database to Cloud SQL for PostgreSQL. They have numerous stored procedures written in PL/SQL. Which tool should they use to automatically convert these procedures to PL/pgSQL?

Accepted Answer

Ora2Pg. Ora2Pg is a schema conversion tool that translates Oracle PL/SQL code to PostgreSQL PL/pgSQL, handling data type mappings and object conversions.

Answer

BigQuery Data Transfer Service

Answer

Database Migration Service (DMS)

Answer

gcloud sql import

Question 7

A financial application requires zero data loss (RPO=0) and automatic failover within 60 seconds in the event of a zone failure. The database must support ACID transactions. Which Google Cloud service meets these requirements?

Accepted Answer

Cloud SQL with HA configuration. Cloud SQL with HA configuration uses synchronous replication to a standby instance in a different zone. If the primary zone fails, Cloud SQL automatically fails over to the standby. Because replication is synchronous, RPO is effectively zero. Automatic failover typically completes within 60 seconds.

Answer

Cloud SQL cross-region read replica

Answer

Cloud SQL single-zone instance with automatic backups

Answer

Cloud Bigtable multi-cluster replication

Question 8

A financial services company uses Cloud Spanner for a global ledger application. The 'Transactions' table has a primary key of 'TransactionId' (a UUID string) and includes columns 'AccountId', 'Amount', and 'Timestamp'. The application frequently runs queries that retrieve all transactions for a specific account within a date range, sorted by timestamp. These queries are currently slow because they require a full table scan. The team wants to optimize the schema for these queries without changing the application's write pattern, which inserts transactions with random UUIDs. What should they do?

Accepted Answer

Create a secondary index on (AccountId, Timestamp) and include the 'Amount' column as a stored column.. Creating a secondary index on (AccountId, Timestamp) allows Cloud Spanner to quickly find all transactions for a given account within a time range, and the index is sorted by timestamp, satisfying the sort order. Storing 'Amount' in the index makes it covering, so the query can be answered entirely from the index without fetching the base row. This approach optimizes read performance while leaving the primary key and write pattern unchanged.

Answer

Change the primary key to (AccountId, Timestamp, TransactionId) to cluster transactions by account and time.

Answer

Create a secondary index on (Timestamp, AccountId) and rely on the query optimizer to use it for account-specific queries.

Answer

Add a foreign key from 'Transactions' to an 'Accounts' table and interleave 'Transactions' in 'Accounts'.

Question 9

A company is designing a database schema for a global e-commerce platform. Orders are created with high frequency, and order status updates occur frequently. The team needs to choose a primary key strategy for the orders table in Spanner. Which approach minimizes hot-spotting?

Accepted Answer

Use a universally unique identifier (UUID) as the primary key. In Spanner, monotonically increasing or time-ordered primary keys cause hot-spotting because all new writes are directed to the same tablet server, creating a single point of contention. UUIDs are randomly distributed, ensuring writes are spread evenly across the entire key space, which minimizes hot-spotting and maximizes write throughput.

Answer

Use a monotonically increasing integer (e.g., auto-increment)

Answer

Use a timestamp as the primary key

Answer

Use a composite key with user_id and order_date

Question 10

A BI dashboard query is slow and high cost. The query does multiple joins on large tables and uses window functions. The data engineer suggests using materialized views. However, the query uses non-deterministic functions. What is the limitation?

Accepted Answer

Materialized views cannot include non-deterministic functions. Materialized views store the result set of a query physically, like a table. If the query includes non-deterministic functions (e.g., NOW(), RAND(), CURRENT_TIMESTAMP), the stored result would become stale immediately because the function's output changes each time it is evaluated. Most SQL databases (e.g., PostgreSQL, Oracle, Snowflake) explicitly forbid non-deterministic functions in materialized view definitions to prevent this logical inconsistency.

Answer

Materialized views cannot be updated automatically

Answer

Materialized views cannot be created with joins

Answer

Materialized views only support simple aggregation