PCD Practice Test 39 — 15 Questions

Question 1

A company runs a multi-service application on GKE and wants to create a Service Level Indicator (SLI) for request latency. They have set up Cloud Service Mesh (Anthos Service Mesh) with Istio. Which metric should they use for the SLI?

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

Accepted Answer

istio_request_duration_milliseconds_bucket metric from Cloud Monitoring.. `istio_request_duration_milliseconds_bucket` is a native Istio metric automatically exported by Cloud Service Mesh (Anthos Service Mesh) to Cloud Monitoring. It provides a histogram of request latencies, which is the standard data source for building a latency-based SLI (e.g., the proportion of requests under a threshold). This metric is pre-configured and requires no custom instrumentation, making it the most direct and reliable choice for an SLI in this environment.

Answer

Custom metric exported by the application using OpenTelemetry.

Answer

Cloud Trace latency distribution from traces.

Answer

Cloud HTTP Load Balancer latency metric.

Question 2

A company is using Cloud SQL for MySQL and wants to offload read traffic to a replica for better performance. They also need the replica to be available for disaster recovery in a different region. What should they do?

Accepted Answer

Create a cross-region read replica in another region.. Cross-region read replicas serve both read scaling and disaster recovery. They can be promoted to primary in a different region. Same-region replicas do not protect against regional failures. External replicas are not necessary; Cloud SQL supports cross-region replicas natively.

Answer

Enable high availability on the primary instance.

Answer

Create a same-region read replica and use failover.

Answer

Use an external replica in another region via MySQL replication.

Question 3

A company runs a high-traffic web application that reads session data frequently. They want to reduce latency and offload database load. Which Google Cloud service should they use as a caching layer?

Accepted Answer

Memorystore for Redis. Memorystore (Redis) is the managed caching service on GCP, ideal for session stores and reducing database load. Cloud SQL, Bigtable, and Firestore are not suitable as low-latency caches.

Answer

Cloud SQL

Answer

Firestore

Answer

Cloud Bigtable

Question 4

Which Google Cloud database is serverless, supports offline data synchronization for mobile applications, and provides real-time updates via listeners?

Accepted Answer

Firestore. Firestore is a serverless NoSQL document database designed for mobile and web apps. It provides offline persistence, real-time listeners, and Security Rules for access control. Cloud SQL and Spanner are not serverless; Bigtable requires cluster management.

Answer

Cloud SQL

Answer

Cloud Spanner

Answer

Bigtable

Question 5

An online gaming company uses Cloud SQL for MySQL for player data and wants to synchronise some of that data to a BigQuery dataset daily for analytics. The data transformations are complex (e.g., aggregations, joins with other sources). Which service should they use to build this pipeline?

Accepted Answer

Dataflow pipeline using JDBC source and BigQuery sink. Dataflow provides a fully managed, serverless execution environment for Apache Beam pipelines, which can read from Cloud SQL via a JDBC source, perform complex transformations (aggregations, joins with other sources) in a distributed manner, and write the results to BigQuery as a sink. This directly addresses the requirement for complex, daily transformations that are beyond the capabilities of simpler services.

Answer

Datastream with BigQuery as destination

Answer

BigQuery Data Transfer Service for Cloud SQL

Answer

Cloud Functions triggered by Cloud Scheduler

Question 6

A company is migrating a 5 TB Oracle database to AlloyDB for PostgreSQL. They need to validate the migration with a test replica without impacting the source. After testing, they plan to promote the replica to become the primary. Which migration strategy should they use?

Accepted Answer

Use Database Migration Service to create a continuous migration job with a full dump and CDC, then test the replica and promote it. Database Migration Service (DMS) with a continuous migration job using a full dump and Change Data Capture (CDC) is the correct choice because it allows you to create a fully synchronized test replica of the 5 TB Oracle database in AlloyDB for PostgreSQL without impacting the source. After validating the replica, you can promote it to become the primary with minimal downtime, as CDC ensures near-real-time consistency.

Answer

Use Ora2Pg to migrate the schema, then perform a manual bulk data export/import

Answer

Use Datastream to replicate to AlloyDB, then promote the target

Answer

Use Cloud Data Fusion to create an ETL pipeline from Oracle to AlloyDB

Question 7

A company is designing a cross-database solution with eventual consistency. They need to update data in Cloud SQL (orders) and Bigtable (inventory) as part of a single business transaction. Which two patterns can help maintain eventual consistency? (Choose two.)

Accepted Answer

Implement a saga pattern with compensating transactions in case of failure.. Option B is correct because the saga pattern breaks a cross-database business transaction into a sequence of local transactions, each updating one database (Cloud SQL orders or Bigtable inventory), and uses compensating transactions to undo prior steps if a later step fails, which is the standard way to achieve eventual consistency without distributed locking. Option D is correct because in an event-driven, eventually consistent design, messages or events can be delivered more than once (at-least-once delivery), so idempotent receivers ensure repeated updates do not corrupt data and retry logic ensures transient failures eventually propagate the changes to both databases. Option A is not appropriate because two-phase commit provides atomic, strongly consistent distributed transactions and requires a coordinator plus locking across both databases, which conflicts with the eventual-consistency goal and is not supported across Cloud SQL and Bigtable in this manner. Option C is wrong because global transactions imply strong consistency, which is the opposite of the stated eventual-consistency requirement. Option E is wrong because a single synchronous call writing to both databases does not provide atomicity or consistency guarantees and creates tight coupling and failure-handling problems rather than eventual consistency.

Answer

Use two-phase commit (2PC) across both databases.

Answer

Design for strong consistency by using global transactions.

Answer

Write directly to both databases in a single synchronous call.

Question 8

Which of the following is NOT a valid use case for Cloud Bigtable?

Accepted Answer

Serving as a transaction processing database for an e-commerce website. Cloud Bigtable is a NoSQL, wide-column database optimized for high-throughput, low-latency analytical and operational workloads, but it does not support multi-row transactions or ACID guarantees. Therefore, it is not suitable as a transaction processing database for an e-commerce website, which requires strong consistency and transactional integrity for order management and inventory updates.

Answer

Analyzing ad-tech clickstream data

Answer

Storing time-series data from IoT sensors

Answer

Powering a financial market data feed with millisecond latency

Question 9

A developer needs to query data from Cloud Spanner and Bigtable together in a single SQL statement without moving data. Which Google Cloud feature allows this?

Accepted Answer

Federated queries. BigQuery federated queries allow querying external data sources like Cloud Spanner and Bigtable directly from BigQuery using SQL, without importing data.

Answer

Spanner change streams

Answer

BigQuery Omni

Answer

Cloud SQL external tables

Question 10

A company wants to deploy a database for a global application that requires strong consistency across continents, with the ability to handle millions of reads and writes per second. Which Google Cloud database should they choose?

Accepted Answer

Cloud Spanner. Cloud Spanner is the only globally distributed, strongly consistent relational database that can handle millions of operations per second across multiple continents.

Answer

Firestore

Answer

Cloud SQL

Answer

Cloud Bigtable

Question 11

A company is deploying a new financial application on Google Cloud that requires strong consistency, global scale, and the ability to handle millions of transactions per second across multiple regions. The application data has a relational structure. Which database service should they choose?

Accepted Answer

Cloud Spanner. Cloud Spanner is the only Google Cloud database that provides global strong consistency, horizontal scaling, and relational schema support at massive transaction volumes. Cloud SQL is limited to a single region, Bigtable does not support relational data, and Firestore is document-based with limited transaction support.

Answer

Firestore

Answer

Cloud SQL for MySQL

Answer

Cloud Bigtable

Question 12

A company uses Cloud Logging to store application logs. They need to keep logs for 3 years for compliance. What is the most cost-effective way to store logs for this duration?

Accepted Answer

Create a sink to export logs to Cloud Storage with object lifecycle rules. Cloud Logging's default retention is limited (e.g., 30 days for logs, with some exceptions up to 400 days), so it cannot meet a 3-year compliance requirement. Exporting logs to Cloud Storage and applying object lifecycle rules allows you to automatically transition objects to lower-cost storage classes (e.g., from Standard to Nearline, Coldline, or Archive) and delete them after the retention period, minimizing cost while meeting the 3-year retention need.

Answer

Use Cloud Logging's default retention

Answer

Create a sink to export logs to Pub/Sub

Answer

Create a sink to export logs to BigQuery

Question 13

You deployed a new version of your application that uses Cloud Pub/Sub for asynchronous messaging. After deployment, you notice that messages are accumulating in the subscription backlog. You suspect the subscriber is too slow. Which tool should you use to diagnose?

Accepted Answer

Cloud Monitoring to check subscriber's processing latency and throughput.. Cloud Monitoring is the correct tool because it provides metrics such as subscriber processing latency, throughput, and backlog size for Pub/Sub subscriptions. By examining these metrics, you can quantify how slow the subscriber is and identify whether the issue is due to high latency or insufficient throughput, directly addressing the suspicion of a slow subscriber.

Answer

Cloud Trace to trace message processing.

Answer

Cloud Logging to view subscriber logs.

Answer

Cloud Profiler to profile subscriber code.

Question 14

A team notices that their application's latency has increased after a recent deployment. They suspect a specific code path is slower. Which Google Cloud tool should they use to identify the most time-consuming functions in their code?

Accepted Answer

Cloud Profiler. Cloud Profiler is the correct tool because it performs continuous, low-overhead profiling of CPU and memory usage, pinpointing the specific functions or methods that consume the most resources. Unlike tracing, which focuses on request latency across services, profiling identifies the most time-consuming code paths within a single application process, making it ideal for diagnosing a slow code path after a deployment.

Answer

Cloud Debugger

Answer

Cloud Trace

Answer

Cloud Logging

Question 15

An application deployed on Google Kubernetes Engine is experiencing intermittent latency spikes. The team has enabled Cloud Trace and sees that a specific gRPC call to a backend service occasionally takes >500ms. However, the backend service's logs show no errors. What is the most likely cause that the team should investigate further?

Accepted Answer

The gRPC client is not using connection pooling, causing frequent TLS handshakes.. GRPC relies on HTTP/2, which multiplexes multiple requests over a single persistent connection. If the client does not reuse connections, each new request triggers a new TLS handshake, which can add significant latency (often 100-500ms) due to certificate exchange and cryptographic operations. This intermittent behavior occurs when the client creates a new channel per request rather than reusing a connection pool, leading to sporadic high-latency calls without backend errors.

Answer

The Cloud Trace sampling rate is too low, causing statistical noise.

Answer

The backend service is under-provisioned and experiencing resource contention only during peak traffic.

Answer

The network latency between the client and backend is high due to a misconfigured VPC firewall.