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100+ Free Tencent Cloud Expert Developer Practice Questions

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2026 Statistics

Key Facts: Tencent Cloud Expert Developer Exam

Expert

Highest Tier

Tencent Cloud (4 levels: Practitioner/Associate/Professional/Expert)

3 tracks

Expert Certifications

Tencent Cloud (Developer, Architect, Operations)

2 years

Validity Period

Tencent Cloud (all tiers)

Not published

Exam Fee (English)

Check cloud.tencent.com/edu for current pricing

120–180 hrs

Recommended Study

Based on exam depth and prerequisite experience

Chinese primary

Exam Language

Tencent Cloud (English availability unconfirmed)

TCE-Developer is the highest Tencent Cloud certification for developers. It tests expert-level design and implementation of serverless, microservices, service mesh, distributed systems, IaC, and security patterns on Tencent Cloud. Official exam format details (questions, time, fee, passing score) are not publicly published in English as of 2026. Preparation typically requires 120–180 hours and holding a Professional-level developer credential.

Sample Tencent Cloud Expert Developer Practice Questions

Try these sample questions to test your Tencent Cloud Expert Developer exam readiness. Each question includes a detailed explanation. Start the interactive quiz above for the full 100+ question experience with AI tutoring.

1Your SCF (Serverless Cloud Function) cold starts are causing p99 latency spikes above 3 seconds for a latency-sensitive API. The function uses a 512 MB memory allocation and initializes a large SDK on startup. Which approach BEST reduces cold-start impact without rewriting the function?
A.Switch the function trigger from API Gateway to CKafka to batch invocations
B.Enable SCF Provisioned Concurrency to keep a fixed number of instances pre-initialized
C.Increase the function timeout from 30 s to 900 s to absorb initialization time
D.Move all SDK initialization code inside the event handler rather than the global scope
Explanation: SCF Provisioned Concurrency pre-warms a configurable number of function instances, eliminating cold-start initialization for those instances and removing p99 spikes. Switching triggers changes invocation semantics but does not eliminate cold starts. Increasing timeout does not prevent cold starts—it only prevents timeouts. Moving initialization inside the handler makes cold starts worse, not better.
2A distributed microservice on TKE uses Tencent Cloud Service Mesh (TCM) for inter-service traffic management. You need to implement a canary release sending 5% of production traffic to a v2 deployment while keeping 95% on v1. Which TCM resource is the correct primary control point?
A.DestinationRule with subset definitions and a VirtualService with weighted routing
B.Kubernetes Deployment with a 5:95 replica ratio between v2 and v1 pods
C.Istio PeerAuthentication policy with mTLS mode set to PERMISSIVE
D.TCM IngressGateway annotation `nginx.ingress.kubernetes.io/canary-weight: 5`
Explanation: In TCM (which is built on Istio), a DestinationRule defines named subsets (v1, v2) and a VirtualService applies traffic-weight rules (5% to v2, 95% to v1) — the correct two-resource pattern for weighted canary releases. Replica-ratio canary is a poor approximation with no precision. PeerAuthentication controls mTLS, not traffic weights. The nginx annotation applies only to nginx ingress, not to service-mesh sidecar routing.
3You are designing an event-driven pipeline on Tencent Cloud. Upstream producers publish domain events at up to 50,000 messages/second with occasional burst spikes to 200,000/second. Downstream consumers must process messages in strict per-partition order. Which messaging service and configuration is MOST appropriate?
A.CMQ Queue in FIFO mode with message retention set to 7 days
B.CKafka (Tencent Cloud Kafka) with enough partitions, producer key-based routing, and a single consumer group per partition
C.TDMQ RabbitMQ with exclusive queues per consumer
D.SCF async invocation with COS as the event source trigger
Explanation: CKafka (managed Kafka) handles millions of messages per second, supports partition-level ordering when producers route by message key, and allows consumer groups to read each partition sequentially. CMQ FIFO does not scale to 200 k/s bursts. RabbitMQ exclusive queues cannot maintain ordering across rebalances at that throughput. SCF+COS triggers are batch-oriented and do not provide per-key ordering semantics.
4Your Terraform configuration uses the tencentcloud provider to manage TKE clusters. After upgrading the cluster Kubernetes version, `terraform plan` shows a force-replace for the cluster resource even though only the `cluster_version` attribute changed. What is the MOST likely cause and remediation?
A.Tencent Cloud Terraform provider does not support in-place cluster upgrades; you must use the console
B.The `cluster_version` attribute is marked `ForceNew` in the provider schema; pin the version and upgrade via the tctl CLI outside Terraform
C.You must run `terraform taint` on the cluster resource before upgrading the version
D.The provider requires a `lifecycle { ignore_changes = [cluster_version] }` block to permit in-place version changes
Explanation: In older versions of the tencentcloud Terraform provider, `cluster_version` was annotated `ForceNew`, meaning any change triggers resource destruction and re-creation. The standard remediation is to pin the version in Terraform state and perform the actual Kubernetes upgrade through the TKE console or tctl, then update the state with `terraform refresh` or a state import. Using `ignore_changes` suppresses the diff but does not perform the upgrade.
5An application uses TencentDB for Redis (cluster mode) to cache session data. Developers report that KEYS * commands issued during load testing cause production latency spikes. What is the BEST architectural fix?
A.Increase Redis maxmemory-policy to allkeys-lru so eviction prevents key accumulation
B.Replace KEYS * with SCAN using a cursor with a small COUNT to iterate keys incrementally
C.Shard session data into multiple Redis instances to reduce per-instance key count
D.Enable TencentDB Redis slow-query log and set slowlog-log-slower-than to 0 to capture all commands
Explanation: KEYS * is an O(N) blocking command that holds the Redis event loop for its entire execution, causing latency spikes proportional to database size. Replacing it with SCAN (cursor-based, non-blocking) iterates keys incrementally without blocking other commands. Changing eviction policy affects memory management, not command blocking. Sharding reduces key count per shard but does not prevent KEYS * from blocking within each shard. Enabling slow-query logging diagnoses the problem but does not fix it.
6You are implementing a CI/CD pipeline using CODING DevOps (Tencent Cloud's integrated DevOps platform). Your pipeline needs to build a Docker image, push it to TCR (Tencent Container Registry), and deploy it to TKE via kubectl. Which CODING pipeline artifact configuration correctly authenticates to TCR without embedding credentials in pipeline code?
A.Store the TCR password as a CODING environment variable and reference it in the docker login command
B.Configure a TCR Service Connection in CODING and reference the connection ID in the pipeline Docker plugin step
C.Generate a permanent API key and encode it in base64 in a Kubernetes Secret, then mount it in the build pod
D.Use a public TCR instance namespace so no authentication is required for push operations
Explanation: CODING DevOps supports native TCR Service Connections that store credentials securely and inject them without exposing secrets in pipeline YAML. The Docker plugin step references the connection by ID, handles login automatically, and avoids any credential leakage. Storing passwords as environment variables risks exposure in logs. Embedding API keys in base64 Kubernetes Secrets is not encrypted by default and still embeds credentials in code. Public TCR namespaces allow unauthenticated pulls, not pushes.
7A microservice architecture on TKE uses gRPC for internal communication. The service mesh (TCM) must enforce mTLS between all services AND allow external HTTPS traffic from the internet through the IngressGateway. Which combination of TCM policies achieves this?
A.PeerAuthentication mode=STRICT cluster-wide and a Gateway resource with TLS mode=PASSTHROUGH for external traffic
B.PeerAuthentication mode=STRICT cluster-wide and a Gateway resource with TLS mode=SIMPLE (server-side TLS) terminating at the IngressGateway
C.PeerAuthentication mode=PERMISSIVE cluster-wide and RequestAuthentication with JWT for internal services
D.AuthorizationPolicy with action=ALLOW for all principals plus a VirtualService rewriting HTTPS to HTTP
Explanation: PeerAuthentication STRICT enforces mTLS for all pod-to-pod traffic in the mesh. For the IngressGateway (which is outside the mesh peer plane), configuring a Gateway with TLS mode=SIMPLE terminates HTTPS externally and the gateway then communicates internally over mTLS. PASSTHROUGH mode does not terminate TLS at the gateway, so the backend service must handle TLS itself. PERMISSIVE mode allows plaintext, defeating mTLS enforcement. JWT RequestAuthentication is for user identity, not service-to-service mTLS.
8When configuring auto-scaling for a TKE node pool, you want to scale out when average CPU utilization across all pods in a Deployment exceeds 70% and scale in gracefully waiting for active connections to drain. Which Kubernetes and TKE features should you configure together?
A.HorizontalPodAutoscaler targeting CPU 70%, node pool min/max bounds, and a preStop lifecycle hook with a sleep to allow connection draining
B.VerticalPodAutoscaler in Auto mode to resize pod CPU requests, plus node pool cluster-autoscaler with scale-down-delay-after-add
C.Custom metrics API with application QPS metric, KEDA ScaledObject, and a PodDisruptionBudget
D.TKE scheduled scaling action at peak hours and a Kubernetes readiness probe to delay traffic during startup
Explanation: HPA targeting CPU 70% handles pod-level scale-out; the node pool cluster-autoscaler adds nodes when pods are pending. A preStop lifecycle hook with a sleep (e.g., 30 s) gives the load balancer time to deregister the pod before the container stops, allowing active connections to drain. VPA resizes CPU requests vertically rather than scaling out. KEDA is for custom/event metrics. Scheduled scaling cannot react to dynamic load.
9Your application stores large binary blobs in COS. A client SDK uploads 5 GB files using multipart upload. Uploads sometimes fail mid-way and restart from scratch. What COS feature eliminates the need to restart large uploads after failures?
A.COS Versioning — each partial upload creates a new object version that can be resumed
B.COS multipart upload resume: store the UploadId from InitiateMultipartUpload and ListParts to identify completed parts, then continue uploading only missing parts
C.Enable COS Transfer Acceleration so the upload completes faster and the window for failure is shorter
D.Set the COS object ACL to private and use a presigned URL with a 24-hour expiry to avoid authentication interruptions
Explanation: COS multipart upload allows resumability: the UploadId returned by InitiateMultipartUpload persists server-side. If the upload fails, you call ListParts with that UploadId to see which parts succeeded, then re-upload only the missing parts before calling CompleteMultipartUpload. Versioning tracks complete object versions, not partial uploads. Transfer Acceleration reduces latency but does not provide resume capability. Presigned URLs handle authentication, not resume.
10You are building a distributed transaction spanning TencentDB MySQL and a downstream microservice call. You need atomicity across both operations. Which pattern is MOST appropriate on Tencent Cloud?
A.Use a two-phase commit (2PC) XA transaction across TencentDB MySQL and the microservice HTTP endpoint
B.Implement the Saga pattern: each service executes a local transaction and publishes a domain event to CKafka; compensating transactions handle rollback
C.Use TencentDB global transaction manager (GTM) with distributed XA across all participating MySQL instances
D.Wrap both operations in a single SCF function and rely on SCF's built-in transaction retry mechanism
Explanation: The Saga pattern is the recommended approach for distributed transactions in microservice architectures: each participant executes a local ACID transaction, publishes an event (e.g., to CKafka), and listens for events to trigger compensating actions on failure. 2PC with HTTP endpoints couples services tightly and has poor failure recovery. TencentDB GTM is for distributed MySQL sharding scenarios, not cross-service HTTP transactions. SCF has no built-in database transaction semantics.

About the Tencent Cloud Expert Developer Exam

The Tencent Cloud Certified Expert – Developer (TCE) is the highest developer-track certification in Tencent Cloud's four-tier program (Practitioner → Associate → Professional → Expert). It validates expert-level mastery of cloud-native application engineering on Tencent Cloud, including advanced serverless architecture with SCF, microservices and service mesh with TCM (Istio-based), Kubernetes advanced patterns on TKE, event-driven distributed systems using CKafka and TDMQ, infrastructure as code with the Terraform tencentcloud provider, security engineering (KMS, SSM, CAM ABAC), and observability with APM and CLS. The exam is primarily administered in Chinese; English availability is not confirmed — verify at the official certification portal before scheduling.

Assessment

Question count not published by the exam provider

Time Limit

Not publicly published

Passing Score

Not publicly published

Exam Fee

Not publicly published in English (Tencent Cloud)

Tencent Cloud Expert Developer Exam Content Outline

22%

Advanced Serverless & Event-Driven Architecture

SCF Provisioned Concurrency, cold-start optimization (bundle size, dynamic imports, memory sizing), async DLQ failure handling, VPC-bound SCF with NAT Gateway, CKafka/TDMQ trigger patterns, Saga distributed transactions, and idempotency design with Redis SETNX

20%

Containers & Kubernetes (TKE) — Expert Patterns

HPA custom metrics with Prometheus Adapter, VPA recommendation mode, cluster-autoscaler capacity constraints, rolling update strategy (maxSurge, maxUnavailable), blue-green deployment via Service selector switch, ephemeral containers (kubectl debug), multi-tenant namespace isolation with NetworkPolicy and ResourceQuota, OOMKilled root cause analysis

18%

Service Mesh (TCM) & Microservices

VirtualService weighted canary routing and retry policies, DestinationRule outlier detection circuit breaker, PeerAuthentication STRICT mTLS enforcement, AuthorizationPolicy zero-trust service identity, ServiceEntry for external service registration, distributed tracing via TCM sidecar and APM

15%

Infrastructure as Code (Terraform)

Implicit dependency graph via attribute references, ForceNew attribute handling for TKE upgrades, terraform import for state recovery, terraform state rm, COS remote backend with locking, targeted apply (-target), workspace vs separate-backend strategy, module input variables and .tfvars, state recovery after destroy, OPA/Sentinel policy-as-code in CI/CD

10%

Security Engineering

KMS envelope encryption (CMK + DEK), SSM secret rotation with CSI driver injection, CAM ABAC tag-based conditions (qcs:resource_tag), SAML federation AssumeRoleWithWebIdentity for on-premises auth, CAM instance role for TKE workload identity, COS Block Public Access account guardrail, PCI-DSS CLS log masking, HMAC webhook verification

8%

Distributed Systems & Data

Kafka transactional producer for atomic multi-topic produce, CKafka event sourcing with long retention and consumer group offset replay, Redis cluster CROSSSLOT errors and hash tags, TDSQL-C global tables for multi-region active-active, DTS table-to-COS ETL for large-scale parallel processing

7%

Observability & CI/CD

APM end-to-end trace propagation via TCM sidecar, CLS field indexing and sensitive data masking, DBbrain slow-query analysis and index recommendations, CODING pipeline Service Connections and manual approval gates, DevSecOps SAST placement in CI build stage, cost tagging compliance with CloudAudit + SCF remediation

How to Pass the Tencent Cloud Expert Developer Exam

What You Need to Know

  • Passing score: Not publicly published
  • Assessment: Question count not published by the exam provider
  • Time limit: Not publicly published
  • Exam fee: Not publicly published in English

Keys to Passing

  • Work through all 100 available questions
  • Review every answer and explanation
  • Track weak areas and revisit them
  • Use our AI tutor for tough concepts

Tencent Cloud Expert Developer Study Tips from Top Performers

1Master SCF lifecycle: understand Provisioned Concurrency, warm instance reuse, VPC networking with NAT Gateway, and DLQ configuration for async failure capture
2TCM requires fluency in all four resource types: Gateway (ingress), VirtualService (routing), DestinationRule (load balancing + circuit breaking), and AuthorizationPolicy (zero-trust access control)
3For Terraform, understand the difference between implicit dependency (attribute references), explicit depends_on, terraform import, state rm, and workspace isolation strategies
4Redis cluster mode requires understanding slot assignment, CROSSSLOT errors on multi-key commands, hash tags for co-location, and per-shard SCAN vs KEYS
5Know CKafka durability settings cold: replication factor, min.insync.replicas, and producer acks=all together provide durable exactly-once-producible pipelines
6Study Kafka transactional producer for atomic multi-topic produce and CKafka consumer offset commit frequency — both are expert-level operational concerns
7For TKE rolling updates: understand maxSurge + maxUnavailable capacity math, graceful shutdown with preStop hook + terminationGracePeriodSeconds, and CLB deregistration propagation delay
8Review CAM ABAC conditions (qcs:resource_tag), SAML federation, and the difference between CAM instance role (metadata service) and hard-coded API keys for workload identity
9Envelope encryption: understand why the DEK is encrypted by the CMK rather than passing large data through KMS, and how this reduces KMS API calls and limits CMK exposure
10Complete all 100 expert practice questions and trace every wrong answer back to the Tencent Cloud documentation section covering that service

Frequently Asked Questions

What is the Tencent Cloud Certified Expert – Developer (TCE) certification?

The TCE (Tencent Cloud Certified Expert) Developer is the highest-tier developer certification in Tencent Cloud's four-level program. It validates expert-level mastery of cloud-native application architecture and engineering on Tencent Cloud, including advanced serverless design, microservices with service mesh, Kubernetes expert patterns, distributed systems, and infrastructure as code. The certification is primarily designed for experienced developers and architects who build production-grade applications on Tencent Cloud.

What are the official exam details (questions, time, fee, passing score) for the TCE Developer?

Official exam format details for the Tencent Cloud Certified Expert – Developer are not publicly published in English as of 2026. Tencent Cloud's certification program is primarily documented in Chinese Simplified. Candidates should check the official certification portal at cloud.tencent.com/edu for current exam format, fee, and scheduling information. Expert-tier exams are generally more involved than Associate or Professional tiers based on the depth of content.

What prerequisites does Tencent Cloud recommend before attempting the TCE Developer exam?

Tencent Cloud recommends candidates hold a Professional-level Developer credential and have extensive hands-on experience (typically 2+ years) building production workloads on Tencent Cloud. Expert-tier candidates are expected to understand advanced patterns across SCF, TCM service mesh, TKE Kubernetes, CKafka/TDMQ, TencentDB, Terraform, and security services — not just basic familiarity.

Which topics are most important for the TCE Developer exam?

Based on the TCE program structure, the highest-weight areas are: (1) Advanced Serverless and event-driven architecture (SCF Provisioned Concurrency, DLQ, Saga pattern); (2) Expert TKE patterns (HPA custom metrics, VPA, rolling update edge cases, multi-tenancy); (3) Service Mesh with TCM (mTLS, circuit breaking, zero-trust AuthorizationPolicy); (4) Terraform tencentcloud provider (state management, module design, policy-as-code); and (5) Security engineering (KMS envelope encryption, CAM ABAC, SAML federation).

Is the Tencent Cloud Expert Developer exam available in English?

Tencent Cloud certifications are primarily delivered in Chinese (Simplified) through the official certification portal at cloud.tencent.com/edu. English availability for Expert-tier exams is not confirmed as of 2026. Candidates who prefer English should check the official portal and may find that international exams are gradually being introduced through tencentcloud.com/edu/training.

How should I prepare for the TCE Developer Expert exam?

Recommended preparation: (1) Hold a Professional Developer credential first; (2) Study official Tencent Cloud documentation deeply for SCF, TKE, TCM, CKafka, TDMQ, Terraform tencentcloud provider, KMS, and SSM; (3) Build real advanced projects using these services; (4) Practice 100+ expert-difficulty scenario questions covering distributed systems, service mesh, IaC state management, and security engineering; (5) Plan 120–180 hours of preparation over 8–16 weeks.