PCDE Practice Test 18 — 15 Questions

Question 1

A DevOps team is bootstrapping a new Google Cloud organization. They want to enforce that all Compute Engine instances must use Shielded VM features (Secure Boot, vTPM, Integrity Monitoring). Which organization policy should they set at the organization level?

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

Accepted Answer

Set the 'constraints/compute.requireShieldedVm' policy to true.. The organization policy constraint 'constraints/compute.requireShieldedVm' enforces that all Compute Engine instances are created with Shielded VM features enabled (Secure Boot, vTPM, Integrity Monitoring). Setting this at the organization level ensures the requirement applies to all projects and folders beneath it, preventing non-compliant instances from being created.

Answer

Set the 'constraints/compute.disableSerialPortAccess' policy to true.

Answer

Set the 'constraints/compute.requireOsLogin' policy to true.

Answer

Set the 'iam.allowedPolicyMemberDomains' policy to restrict membership.

Question 2

A company uses AlloyDB for an e-commerce platform. They want to achieve the highest availability within a single region. What configuration should they use, and what is the expected failover RTO?

Accepted Answer

Use a primary instance with a standby in a different zone in the same region; RTO less than 30 seconds. AlloyDB offers an HA configuration with a primary instance and a standby (read pool) in different zones within the same region. Automatic failover occurs in less than 30 seconds. Cross-zone failover is automatic. There is no cross-region HA in AlloyDB (you can use cross-region replicas but that requires manual promotion).

Answer

Use a primary instance with a standby in the same zone; RTO less than 10 seconds

Answer

Use a primary instance with multiple read pools; RTO less than 5 seconds

Answer

Use a primary instance with a cross-region read replica; RTO less than 1 minute

Question 3

You are running Cloud Bigtable for time-series analytics. Each row represents a metric and uses a row key of format 'metricID#timestamp' (e.g., 'cpu_usage#2023-08-01T00:00:00Z'). You notice that writes are concentrated on a small number of nodes. What is the most effective way to distribute writes more evenly?

Accepted Answer

Add a hash prefix of the metricID to the row key. Cloud Bigtable distributes data across nodes based on row key ranges, and rows with similar or sequential row keys land on the same tablet server, creating hotspots. Adding a hash prefix of the metricID to the row key scatters writes across many tablets, distributing load evenly across nodes. This is the canonical Bigtable row key design pattern for write-heavy time-series workloads.

Answer

Use a different column family for each metric

Answer

Increase the number of nodes

Answer

Reverse the timestamp in the row key

Question 4

In Cloud Spanner, which TWO strategies can help reduce read latency for globally distributed applications?

Accepted Answer

Enable follower reads. Option A (Enable follower reads) is correct because Cloud Spanner follower reads let a read be served by a replica in the same region as the client rather than being routed to the leader region, eliminating the cross-region round trip and thereby reducing read latency for globally distributed applications. Option B (Use stale reads) is correct because reading at a bounded staleness timestamp (e.g., a timestamp at least 15 seconds in the past, or an exact staleness) allows Spanner to serve the read from the closest up-to-date replica without waiting for the leader, which lowers latency at the cost of slightly older data. Option C is not correct because a multi-region configuration with a default leader still routes strong reads to the leader region, so it does not by itself reduce read latency for clients far from that leader. Option D is not correct because database partitioning is not a Cloud Spanner latency-reduction feature; Spanner scales via splits and does not expose user-managed partitioning for read latency. Option E is not correct because increasing the number of nodes adds compute capacity and throughput, but it does not change the geographic routing of reads and therefore does not reduce cross-region read latency.

Answer

Use a multi-region configuration with a default leader

Answer

Use database partitioning

Answer

Increase the number of nodes

Question 5

An application uses Firestore in Native mode. The query filters on two fields: 'status' (string) and 'created_date' (timestamp). The query returns results but the billing shows high document reads. What is the most likely cause?

Accepted Answer

The query is using an inequality filter on 'created_date' which requires a composite index.. In Firestore Native mode, queries that apply an inequality filter (e.g., >=, >, <, !=) on a field automatically require a composite index on both the equality filter field and the inequality filter field to avoid a full collection scan. Without that composite index, Firestore performs a back-end scan of all documents matching the equality filter, then applies the inequality filter in memory, resulting in high document reads. Option A correctly identifies that the inequality filter on 'created_date' is the most likely cause of the excessive reads because it forces Firestore to read and discard many documents that do not satisfy the timestamp condition.

Answer

The query is using 'array-contains' which always scans the entire collection.

Answer

The 'status' field is not indexed because single-field indexes are not automatic.

Answer

The query is missing an ORDER BY clause causing a full scan.

Question 6

A company is migrating CI/CD pipelines to Google Cloud. They want to deploy containerized applications to GKE using GitOps principles with Config Sync. Which two components are required? (Choose 2)

Accepted Answer

Config Sync operator (reconciler) installed on the GKE cluster. Option C is correct because Config Sync works through an operator/reconciler (the Config Sync component, historically the nomos reconciler) running in the GKE cluster; it continuously watches the declared source of truth and applies/reconciles the cluster state to match it, which is the core mechanism of GitOps. Option D is correct because Config Sync requires a Git repository (or OCI image) that holds the desired Kubernetes manifests as the single source of truth; without a declared source containing the manifests, there is nothing for the reconciler to sync. Option A (Artifact Registry) is a container/image registry for storing artifacts, which is useful for CI/CD but not a required component for Config Sync's GitOps reconciliation. Option B (Cloud Build trigger) is a CI automation mechanism that can build images, but Config Sync does not require Cloud Build to function. Option E (Binary Authorization) is a deploy-time policy control for verifying signed images, which is a security feature and not a prerequisite for Config Sync.

Answer

Artifact Registry

Answer

Cloud Build trigger

Answer

Binary Authorization

Question 7

Which Git branching strategy is recommended for infrastructure as code in a DevOps environment to enable continuous delivery?

Accepted Answer

Trunk-based development. Trunk-based development is recommended for infrastructure as code in a DevOps environment because it encourages developers to merge small, frequent changes into the main branch (trunk) at least daily. This practice reduces merge conflicts, enables continuous integration and continuous delivery (CI/CD), and supports rapid feedback. It aligns with the goal of continuous delivery by keeping the main branch always deployable.

Answer

Forking workflow

Answer

Feature branch workflow without merging to main often

Answer

GitFlow

Question 8

You are designing a CI/CD pipeline for a microservice application that uses feature flags to decouple deployment from release. The team wants to automatically enable a feature flag after the deployment is verified in production for a subset of users. Which tool or approach should you integrate into the pipeline to manage feature flags?

Accepted Answer

Use Cloud Deploy hooks to call the LaunchDarkly API after the deployment succeeds.. It uses Cloud Deploy hooks to trigger a post-deployment action that calls the LaunchDarkly API, enabling the feature flag for a subset of users after the deployment has been verified in production. This decouples deployment from release, allowing the feature flag to be toggled dynamically without redeploying or modifying the application code.

Answer

Embed the feature flag logic in the application code and use environment variables to enable it.

Answer

Use Cloud Build substitution variables to set the feature flag status at build time.

Answer

Use Config Sync to update a ConfigMap with the flag status.

Question 9

A Cloud SQL for PostgreSQL instance is experiencing high CPU usage due to many short-lived connections. Which configuration change can help without application changes?

Accepted Answer

Enable connection pooling with pgBouncer. Enabling connection pooling with pgBouncer reduces the CPU overhead of creating and tearing down many short-lived connections. Option A (read replicas) helps with read load, not CPU from connection churn. Option B (increase max_connections) allows more concurrent connections but does not reduce per-connection overhead and can increase CPU usage. Option C (increase tier to more vCPUs) adds resources but does not address the root cause of connection churn and may be cost-inefficient.

Answer

Use read replicas

Answer

Increase the max_connections parameter

Answer

Increase the tier to more vCPUs

Question 10

A company needs to run complex analytical queries on large datasets (petabytes) with SQL support and high scalability. The data is stored in CSV files in Cloud Storage. Which Google Cloud service is MOST suitable?

Accepted Answer

BigQuery. BigQuery is Google Cloud's fully managed, serverless data warehouse designed for running complex analytical queries on petabyte-scale datasets using standard SQL. It natively supports querying external data sources, including CSV files in Cloud Storage, through federated queries or external tables. Its columnar storage and distributed architecture provide high scalability and performance for analytical workloads.

Answer

Cloud Bigtable

Answer

Cloud SQL

Answer

Cloud Spanner

Question 11

A company wants to enforce that no Compute Engine instances are created with external IP addresses unless explicitly allowed. Which organization policy constraint should be used?

Accepted Answer

constraints/compute.vmExternalIpAccess. The 'constraints/compute.vmExternalIpAccess' policy restricts external IP usage on VMs. It can be set at the organization, folder, or project level.

Answer

constraints/compute.disableSerialPortAccess

Answer

constraints/compute.setCommonInstanceMetadata

Answer

constraints/compute.requireOsLogin

Question 12

Which TWO actions improve query performance and reduce cost in BigQuery for BI workloads?

Accepted Answer

Cluster tables on columns used in GROUP BY. Option A is correct because clustering a BigQuery table on columns used in GROUP BY physically co-locates related rows, so the engine scans fewer blocks and shuffles less data during aggregation, cutting both slot time and bytes billed. Option B is correct because partitioning on columns frequently referenced in WHERE clauses enables partition pruning, so BigQuery reads only the relevant partitions rather than the whole table, directly reducing query cost and latency. Option C is not a query-performance optimization: batch loading versus streaming affects ingestion cost and availability (streaming has extra per-row charges), not the performance of BI queries. Option D is wrong because CSV is a non-columnar, untyped format; BigQuery performs best with columnar formats like Parquet or Avro (or native tables), which reduce scanned bytes. Option E is wrong because SELECT * reads every column, increasing bytes processed and cost, whereas selecting only needed columns minimizes the data scanned.

Answer

Load data using batch loads instead of streaming

Answer

Store data in CSV format

Answer

Use SELECT * in all queries

Question 13

A Cloud SQL for MySQL instance requires a patch update. How can you minimize downtime during the update?

Accepted Answer

Schedule the maintenance during a maintenance window. Cloud SQL for MySQL uses a maintenance window to schedule patching during a period of low traffic, minimizing user impact. While a restart is typically required for patch application, the maintenance window allows you to control when that restart occurs, reducing downtime exposure. This is the standard recommended approach for managed database patching in Google Cloud.

Answer

Apply the patch without restart

Answer

Use database migration to a new instance

Answer

Failover to a read replica during the update

Question 14

Which THREE considerations are important when designing a schema for Cloud Firestore to ensure scalability?

Accepted Answer

Design collections to avoid high read/write rates on a single document.. Cloud Firestore scales by distributing data across multiple documents. A single document with high read/write rates creates a hotspot, leading to contention and degraded performance. Designing collections to spread load evenly avoids this bottleneck and ensures linear scalability.

Answer

Nest subcollections up to 10 levels deep to model complex hierarchies.

Answer

Use collection group indexes for all queries to avoid manual index creation.

Question 15

Which THREE are valid Cloud Deploy deployment strategies? (Choose three.)

Accepted Answer

Blue/Green. Google Cloud Deploy supports three built-in deployment strategies: Standard (D), which deploys all at once to a target; Canary (E), which gradually shifts traffic to a new revision in configurable percentage increments with optional verification; and Blue/Green (B), which deploys a new version alongside the old and switches traffic after verification, allowing rollback. These three map directly to the strategy types defined in Cloud Deploy's delivery pipeline configuration (strategy.standard, strategy.canary, strategy.blueGreen). Rolling update (A) is a Kubernetes Deployment strategy, not a Cloud Deploy pipeline strategy, and Shadow (C) is a traffic-mirroring technique used in tools like Istio or Spinnaker, not a Cloud Deploy strategy.

Answer

Rolling update

Answer

Shadow