PCD Practice Test 4 — 10 Questions

Question 1

An engineer is designing a Cloud Spanner schema for a social media application. The Users table and the Posts table have a parent-child relationship. To optimize read performance for fetching all posts of a user, which schema design approach should be used?

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

Accepted Answer

Use interleaved tables with Users as parent and Posts as child. Interleaved tables store child rows (Posts) with their parent row (User) in the same split, enabling locality and faster joins. Global or local indexes are separate structures.

Answer

Denormalize by storing post data as a repeated field in Users table

Answer

Use a local secondary index on user_id in the Posts table

Answer

Create a global secondary index on user_id in the Posts table

Question 2

A startup expects low and predictable traffic initially but wants to use containers with minimal operational overhead. Which compute service should they choose?

Accepted Answer

Cloud Run. Cloud Run is the correct choice because it runs containers in a fully managed, serverless environment that automatically scales from zero, requires no cluster management, and charges only for resources used during request processing. This matches the startup's need for minimal operational overhead and low, predictable traffic, as Cloud Run abstracts away infrastructure management entirely.

Answer

App Engine Flexible Environment

Answer

Google Kubernetes Engine (GKE)

Answer

Compute Engine

Answer

Cloud Functions

Question 3

A company wants to deploy a non-relational database for a real-time bidding application that requires low latency (under 10ms) and high throughput (millions of requests per second). The data model is key-value with a timestamp component. Which Google Cloud database is most appropriate?

Accepted Answer

Cloud Bigtable. Cloud Bigtable is designed for high-throughput, low-latency key-value workloads with time-series data, such as real-time bidding.

Answer

Cloud Spanner

Answer

Firestore

Answer

Cloud SQL

Question 4

An organization wants to design a serverless data processing pipeline that is highly available and can automatically scale based on the number of incoming requests. The pipeline processes JSON messages from a Cloud Pub/Sub topic and writes results to BigQuery. Which service should be used as the compute component?

Accepted Answer

Cloud Run. Cloud Run is the correct compute component because it is a fully managed serverless platform that automatically scales from zero based on incoming HTTP requests, supports event-driven processing via Pub/Sub push subscriptions, and integrates natively with BigQuery. It provides high availability by default across zones and can handle burst traffic without provisioning overhead, making it ideal for a serverless pipeline that processes JSON messages and writes results to BigQuery.

Answer

Cloud Dataflow

Answer

Cloud Functions

Answer

Compute Engine with managed instance groups

Question 5

You are designing a serverless application using Cloud Functions that processes events from Cloud Storage and Cloud Pub/Sub. The function must be idempotent and handle duplicate events. Which three best practices should you implement? (Choose THREE.)

Accepted Answer

Generate a unique idempotency key for each event and store processed keys in a database.. Generating a unique idempotency key for each event and storing processed keys in a database (such as Cloud Firestore) ensures that if the same event is delivered multiple times (e.g., due to at-least-once delivery semantics in Cloud Pub/Sub or Cloud Storage notifications), the function can check the key before processing and skip duplicates. This pattern is essential for idempotent serverless functions, as Cloud Functions may be retried on failure or receive duplicate events from the source.

Answer

Invoke the function synchronously to avoid duplicates.

Answer

Set the function timeout to maximum (540 seconds) to ensure processing completes.

Question 6

An application running on GKE uses a custom metric to track order processing time. The metric is exported via Prometheus and ingested by Cloud Monitoring using the Managed Service for Prometheus. The team wants to create an alert when the 95th percentile latency exceeds 2 seconds over a 5-minute window. Which PromQL query should be used?

Accepted Answer

histogram_quantile(0.95, rate(order_processing_duration_seconds_bucket[5m])). `histogram_quantile(0.95, rate(order_processing_duration_seconds_bucket[5m]))` computes the 95th percentile latency over a 5-minute window using Prometheus histogram buckets. The `rate()` function calculates the per-second increase of each bucket, which is required for accurate quantile estimation from cumulative histograms, and the result directly gives the latency threshold below which 95% of requests fall.

Answer

avg(rate(order_processing_duration_seconds_sum[5m])) / avg(rate(order_processing_duration_seconds_count[5m]))

Answer

histogram_quantile(0.95, sum(rate(order_processing_duration_seconds_bucket[5m])))

Answer

histogram_quantile(0.95, order_processing_duration_seconds_bucket)

Question 7

An organization has Cloud Bigtable for real-time ad serving and BigQuery for analytics. They want to synchronize data changes from Bigtable to BigQuery every hour with a Dataflow pipeline. However, they notice that some updates are missed. What is the most likely cause?

Accepted Answer

Bigtable does not support change data capture natively. Bigtable does not have built-in change data capture (CDC) like some databases. Dataflow can read Bigtable snapshots but not incremental changes unless using Dataflow's streaming insert, which is not native. The common approach is to use Bigtable replication or export, but missed updates often occur because Bigtable does not log changes. A custom solution using Cloud Bigtable change streams (beta) or using a separate logging table is needed.

Answer

The Dataflow pipeline reads from Bigtable using a snapshot read

Answer

The Bigtable table has no primary key

Answer

The Dataflow pipeline is using the wrong sink

Question 8

A developer is designing a chat application using Cloud Firestore. They need to ensure that updates to messages are propagated to all clients in real-time. Which feature should they use?

Accepted Answer

Real-time listeners. Real-time listeners (onSnapshot) in Cloud Firestore allow clients to subscribe to document or query changes, receiving updates immediately when data is modified. This ensures all connected clients see message updates in real-time without polling, which is essential for a chat application.

Answer

Firestore indexes

Answer

Security rules

Answer

Offline persistence

Question 9

A development team is building a containerized application on Google Cloud. They want to implement a CI/CD pipeline that automatically builds and tests their application on every push to the main branch. Which TWO actions should they take to achieve this?

Accepted Answer

Configure a Cloud Build trigger to run on push events to the main branch.. Cloud Build triggers can be configured to automatically start a build whenever a push event occurs on a specific branch, such as main. This is the standard way to initiate a CI/CD pipeline in response to code changes in Google Cloud.

Answer

Enable Cloud Run for Anthos to automatically deploy after build.

Answer

Use Cloud Scheduler to trigger a Cloud Build trigger every 5 minutes.

Answer

Create a Cloud Source Repository and use Cloud Functions to build on push.

Question 10

A company plans to migrate an on-premises Oracle database to Cloud SQL for PostgreSQL. They need to minimize downtime and ensure continuous sync during migration. Which migration approach should they use?

Accepted Answer

Use DMS with a continuous migration job, allowing CDC replication until cutover.. Database Migration Service (DMS) with a continuous migration job enables ongoing Change Data Capture (CDC) replication from the source Oracle database to Cloud SQL for PostgreSQL. This approach keeps the target database synchronized with minimal downtime, allowing a controlled cutover when the migration is complete. It is the only option that satisfies the requirements of continuous sync and minimal downtime.

Answer

Export the Oracle database as a dump file and import it into Cloud SQL manually.

Answer

Use DMS with a one-time migration job, then manually sync changes before cutover.

Answer

Use Cloud SQL Auth Proxy to connect Oracle to Cloud SQL and replicate data using a custom script.