Java Microservices MCQs Practice Test

Last Updated on September 17th, 2024

Solving Practice test plays a crucial role in becoming expert in any technology. It helps in thorough revision of the subject and make the concept crystal clear. Here in this article, we will focus on Java Microservices MCQs Practice Test with a variety of questions such as concept-based, code-based, and scenario-based. At the end of each question, we will explore the explanation of each option whether it is correct or incorrect. In fact, we should also pay attention on the incorrect option as it can have another useful concept.

Table of Contents

Java Microservices MCQs Practice Test

Q#1: (Scenario-Based Question, Multi-Select)

Your team is adopting a microservices architecture for a new application. Which of the following principles should you follow to effectively implement microservices?

A) Each microservice should be responsible for a specific business capability.
B) Microservices should share the same database to simplify data management.
C) Microservices should be independently deployable and scalable.
D) Microservices should communicate synchronously to ensure data consistency.

Answer & Explanation:

A) Correct. Each microservice should focus on a single business capability or domain, making it easier to manage and scale.
B) Incorrect. Sharing databases between microservices can lead to tight coupling and scalability issues; instead, each microservice should manage its own database.
C) Correct. Microservices should be designed to be independently deployable and scalable to maximize flexibility and efficiency.
D) Incorrect. Microservices often use asynchronous communication to reduce dependencies and improve scalability, although synchronous communication may be used in some cases.

Q#2: (Concept-Based Question, Multi-Select)

Which of the following statements accurately differentiate Monolithic architecture from Microservices architecture?

A) Monolithic applications are built as a single unified unit, while Microservices are built as independent, loosely-coupled services.
B) Monolithic applications scale easily by adding more instances, whereas Microservices require more complex scaling strategies.
C) Microservices applications are typically easier to maintain and evolve due to their modular nature.
D) Monolithic applications are more fault-tolerant due to their unified design.

Answer & Explanation:

A) Correct. Monolithic applications are unified and often tightly coupled, while Microservices are divided into independent, loosely-coupled services.
B) Incorrect. Scaling Monolithic applications can be simpler, but Microservices require sophisticated strategies for scaling and coordination.
C) Correct. Microservices are modular and can be updated independently, making them easier to maintain and evolve.
D) Incorrect. Monolithic applications can be less fault-tolerant due to their single point of failure, whereas Microservices can offer better fault isolation.

Q#3: (Code-Based Question, Single-Select)

Which one is the standard way to define a REST client using OpenFeign in a Spring Boot Microservices application?

        @FeignClient(name = "example-service")
        public interface ExampleClient {
    
           @GetMapping("/endpoint")
           String getEndpointData();
        }

       @FeignClient("example-service")
       public interface ExampleClient {

          @RequestMapping(method = RequestMethod.GET, value = "/endpoint")
          String getEndpointData();

       }

       @FeignClient(name = "example-service", url = "http://localhost:8080")
       public interface ExampleClient {

          @GetMapping("/endpoint")
          String getEndpointData();
       }

        @FeignClient(name = "example-service")
        public interface ExampleClient {
           
           @PostMapping("/endpoint")
           String postEndpointData(@RequestBody String data);
        }

Answer & Explanation:

A) Correct. This is the standard way to define a REST client using OpenFeign, specifying the service name and using @GetMapping for a GET request.
B) Incorrect. While valid, this option uses @RequestMapping which is less specific compared to @GetMapping.
C) Incorrect. Adding the url property is not typical for defining a REST client with OpenFeign, as it is usually configured via application properties.
D) Incorrect. This example uses @PostMapping, but the question specifies a GET request, so it does not match the requirement.

Q#4: (Concept-Based Question, Multi-Select)

What are some key benefits of using an API Gateway in a Java microservices application?

A) Provides a single entry point for all client requests, simplifying client interaction.
B) Handles service-to-service communication within the microservices architecture.
C) Directly interacts with the database to fetch and update data.
D) Manages load balancing and routing requests to the appropriate microservice.

Answer & Explanation:

A) Correct. An API Gateway consolidates all client interactions into a single entry point, reducing the complexity for clients.
B) Incorrect. The API Gateway does not handle service-to-service communication; this is typically managed by other components within the microservices architecture.
C) Incorrect. The API Gateway does not interact directly with databases; it interacts with microservices which may, in turn, interact with databases.
D) Correct. The API Gateway is responsible for routing requests and managing load balancing across microservices.

Q#5: (Concept-Based Question, Multi-Select)

What are some common load balancing strategies implemented by tools like Ribbon and OpenFeign Load Balancer?

A) Round-Robin: Distributes requests evenly across all available instances.
B) Least Connections: Routes requests to the instance with the fewest active connections.
C) Random: Selects a random instance to handle each request.
D) Fixed Ratio: Routes a fixed ratio of requests to specific instances.

Answer & Explanation:

A) Correct. Round-Robin distributes incoming requests evenly across available instances, helping to balance the load.
B) Correct. Least Connections sends requests to the instance with the fewest active connections, optimizing resource utilization.
C) Correct. Random load balancing chooses an instance at random for each request, providing simplicity and basic distribution.
D) Incorrect. Fixed Ratio is not a commonly used load balancing strategy; it is more complex and less typical compared to the others listed.

Q#6: (Concept-Based Question, Multi-Select)

Which of the following are characteristics of API Gateway patterns like Netflix Zuul and Spring Cloud Gateway?

A) API Gateways provide a single entry point for managing requests to multiple microservices.
B) They are responsible for performing backend service logic and business processing.
C) API Gateways can handle cross-cutting concerns such as authentication, logging, and rate limiting.
D) Both Netflix Zuul and Spring Cloud Gateway support routing and load balancing.

Answer & Explanation:

A) Correct. API Gateways act as a single entry point for requests, which simplifies client interaction with multiple microservices.
B) Incorrect. API Gateways are not responsible for business logic or backend processing but handle routing and cross-cutting concerns.
C) Correct. They manage concerns like authentication, logging, and rate limiting across multiple services.
D) Correct. Both Zuul and Spring Cloud Gateway offer features like routing and load balancing to distribute requests among services.

Q#7: (Concept-Based Question, Multi-Select)

What are the advantages of using the Database per Service pattern in a microservices architecture?

A) Ensures data autonomy by allowing each service to manage its own data schema.
B) Simplifies transactions by using a single database for all services.
C) Enhances scalability by enabling each service to scale its data store independently.
D) Facilitates data consistency across services by using a single data source.

Answer & Explanation:

A) Correct. Database per Service pattern allows each microservice to manage its own schema, ensuring data autonomy and separation of concerns.
B) Incorrect. Using a single database for all services can complicate transactions and reduce the benefits of microservices.
C) Correct. Each service can independently scale its own data store, improving overall scalability.
D) Incorrect. Database per Service pattern does not guarantee data consistency across services; it can introduce challenges in data synchronization.

Q#8: (Concept-Based Question, Multi-Select)

What are the key benefits of implementing the Circuit Breaker pattern in microservices architecture?

A) The Circuit Breaker pattern helps prevent cascading failures by isolating faults in service calls.
B) It ensures that all service calls are completed successfully, even in failure scenarios.
C) The Circuit Breaker pattern provides automatic retries for failed service calls.
D) It allows for graceful degradation and fallback options when a service fails.

Answer & Explanation:

A) Correct. The Circuit Breaker pattern isolates faults and prevents them from propagating across the system, helping to avoid cascading failures.
B) Incorrect. The Circuit Breaker pattern does not ensure all service calls are successful but provides mechanisms to handle failures gracefully.
C) Incorrect. While retries can be part of a Circuit Breaker strategy, they are not the primary function of the pattern itself.
D) Correct. The pattern supports fallback options and graceful degradation to maintain functionality even when some services are failing.

Q#9: (Concept-Based Question, Multi-Select)

What are some key features of service discovery tools like Eureka and Consul?

A) Automatic registration and deregistration of services.
B) Centralized management of service configurations and secrets.
C) Support for dynamic service discovery and load balancing.
D) Direct integration with container orchestration platforms like Kubernetes.

Answer & Explanation:

A) Correct. Eureka and Consul automatically handle service registration and deregistration, facilitating dynamic service management.
B) Incorrect. Service discovery tools do not manage configurations and secrets; this is handled by other tools or platforms.
C) Correct. Both tools support dynamic discovery of services and can be used to enable load balancing across service instances.
D) Incorrect. Integration with container orchestration platforms like Kubernetes is typically not a primary feature of service discovery tools but rather managed by the orchestration platform itself.

Q#10: (Concept-Based Question, Single-Select)

Below are three statements in the context of OpenFeign used in Java Microservice:

OpenFeign provides a declarative REST client for Spring applications.
OpenFeign does not support load balancing for REST calls.
OpenFeign integrates with Ribbon for client-side load balancing.

Which of the following option is correct?

A) Statements 1 & 2 are correct
B) Statements 1 & 3 are correct
C) Statements 2 & 3 are correct
D) All statements are correct

Answer: B) Statements 1 & 3 are correct

Explanation:

Statement 1: Correct – OpenFeign is a declarative REST client, simplifying HTTP request creation.
Statement 2: Incorrect – OpenFeign can support load balancing when integrated with tools like Ribbon.
Statement 3: Correct – OpenFeign works well with Ribbon for client-side load balancing.

Q#11: (Concept-Based Question, Multi-Select)

Which of the following statements are true about using message queues like Kafka and RabbitMQ for asynchronous communication in microservices?

A) Kafka is designed for high-throughput, durable event streaming and is suitable for real-time data processing.
B) RabbitMQ is optimized for high-throughput message delivery and is more suited for event streaming.
C) Both Kafka and RabbitMQ support message acknowledgment and persistence.
D) Kafka supports exactly-once message delivery semantics.

Answer & Explanation:

A) Correct. Kafka is known for its high-throughput and durable event streaming capabilities, making it suitable for real-time data processing.
B) Incorrect. RabbitMQ is more optimized for high-throughput message delivery, but it is not as suited for high-throughput event streaming as Kafka.
C) Correct. Both Kafka and RabbitMQ support message acknowledgment to ensure messages are processed, and they provide message persistence features.
D) Correct. Kafka provides exactly-once delivery semantics when configured properly, ensuring that messages are neither lost nor duplicated.

Q#12: (Concept-Based Question, Multi-Select)

What are the main characteristics of stateless and stateful services in microservices architecture?

A) Stateless services do not retain information about client sessions between requests, while stateful services maintain session state across requests.
B) Stateful services are easier to scale horizontally compared to stateless services.
C) Stateless services are typically more resilient and easier to scale due to their lack of session management.
D) Stateful services rely on external storage or databases to maintain state information.

Answer & Explanation:

A) Correct. Stateless services do not store session information, which simplifies their design, while stateful services keep track of session information.
B) Incorrect. Stateful services are generally harder to scale horizontally due to the complexities of managing session state.
C) Correct. Stateless services are more resilient and easier to scale because they do not require session management.
D) Correct. Stateful services often use external storage or databases to persist session state.

Q#13: (Concept-Based Question, Single-Select)

How does the Bulkhead pattern help in improving the resilience of microservices?

A) By isolating failures within a specific component to prevent them from affecting other parts of the system.
B) By replicating services across multiple data centers to ensure high availability.
C) By implementing redundant components to handle failovers automatically.
D) By using a central monitoring system to detect and address failures.

Answer & Explanation:

A) Correct. The Bulkhead pattern helps in isolating failures to prevent them from affecting other parts of the system, improving resilience.
B) Incorrect. Replicating services across data centers is a high-availability strategy, not specific to the Bulkhead pattern.
C) Incorrect. Redundant components handle failovers, which is different from the isolation strategy of the Bulkhead pattern.
D) Incorrect. Central monitoring is not related to the Bulkhead pattern; it focuses on failure isolation.

Q#14: (Code-Based Question, Single-Select)

How do you define a Dockerfile to build a container image for a Java microservice?

dockerfile:

       FROM openjdk:11-jre
       COPY target/my-service.jar /app/my-service.jar
       ENTRYPOINT ["java", "-jar", "/app/my-service.jar"]

dockerfile:

       FROM ubuntu:20.04
       COPY my-service.jar /app/my-service.jar
       ENTRYPOINT ["java", "-jar", "/app/my-service.jar"]

dockerfile:

       FROM openjdk:11
       WORKDIR /app
       COPY target/my-service.jar .
       ENTRYPOINT ["java", "-jar", "my-service.jar"]

dockerfile:

       FROM openjdk:8
       COPY target/my-service.jar /opt/my-service.jar
       ENTRYPOINT ["java", "-jar", "/opt/my-service.jar"]

Answer & Explanation:

A) Correct. This Dockerfile uses an appropriate base image (openjdk:11-jre), copies the JAR file to the container, and sets the entry point to run the JAR file.
B) Incorrect. Using ubuntu:20.04 is less specific and may require additional setup for Java.
C) Incorrect. openjdk:11 is a complete JDK image and may include unnecessary components for a runtime-only container.
D) Incorrect. openjdk:8 is outdated compared to openjdk:11-jre, which is preferable for current Java versions.

Q#15: (Concept-Based Question, Multi-Select)

Which of the following statements are true about service discovery using tools like Eureka and Consul?

A) Eureka is a REST-based service registry for Java applications developed by Netflix.
B) Consul provides both service discovery and key-value storage capabilities.
C) Eureka and Consul both support health checks to monitor service availability.
D) Consul requires a central configuration server to manage service discovery.

Answer & Explanation:

A) Correct. Eureka is a service registry developed by Netflix, primarily used for service discovery in Java applications.
B) Correct. Consul offers service discovery along with key-value storage and supports various use cases in distributed systems.
C) Correct. Both Eureka and Consul support health checks to ensure that only healthy services are included in the registry.
D) Incorrect. Consul does not require a central configuration server; it operates as a distributed service discovery and key-value store system.

Q#16: (Concept-Based Question, Single-Select)

How do Prometheus and Grafana work together for monitoring microservices?

A) Prometheus provides alerts based on predefined thresholds, while Grafana visualizes these alerts.
B) Grafana collects and stores metrics, while Prometheus visualizes these metrics.
C) Prometheus and Grafana both perform data collection and storage independently.
D) Prometheus collects and stores metrics from microservices, while Grafana visualizes these metrics.

Answer & Explanation:

A) Incorrect. Prometheus provides alerting capabilities, but Grafana is used for visualization and does not handle alerts directly.
B) Incorrect. Grafana does not collect metrics; it relies on Prometheus or other sources for this data.
C) Incorrect. Prometheus and Grafana are not independent in terms of data collection; Prometheus handles this, and Grafana focuses on visualization.
D) Correct. Prometheus is responsible for collecting and storing metrics from various sources, while Grafana is used to create dashboards and visualize these metrics.

Q#17: (Concept-Based Question, Multi-Select)

What are the differences between Zipkin and Jaeger in Microservices distributed tracing?

A) Zipkin is a distributed tracing system with support for tracing, while Jaeger is a distributed tracing system optimized for high-throughput environments.
B) Jaeger provides better integration with cloud-native technologies compared to Zipkin.
C) Zipkin is more suitable for tracing microservices in non-cloud environments, while Jaeger is optimized for cloud-native architectures.
D) Jaeger offers built-in support for data storage, while Zipkin requires external storage solutions.

Answer & Explanation:

A) Correct. Jaeger is designed to handle higher throughput and larger volumes of trace data compared to Zipkin.
B) Correct. Jaeger is optimized for cloud-native environments and integrates well with modern cloud infrastructure.
C) Incorrect. Both Zipkin and Jaeger are suitable for various environments, but Jaeger has specific optimizations for cloud-native setups.
D) Incorrect. Both systems can use external storage solutions; Jaeger does not inherently offer built-in storage support.

Q#18: (Concept-Based Question, Multi-Select)

Which techniques are commonly used to handle distributed transactions in a microservices application?

A) Two-Phase Commit (2PC) ensures atomicity across multiple services by coordinating transactions.
B) Sagas manage distributed transactions by breaking them into a series of compensatable steps.
C) Two-Phase Commit (2PC) requires all services to be available for a transaction to complete.
D) Sagas rely on a single coordinator service to manage all transaction steps.

Answer & Explanation:

A) Correct. Two-Phase Commit (2PC) coordinates distributed transactions to ensure atomicity, though it may be less fault-tolerant.
B) Correct. Sagas decompose transactions into smaller, compensatable steps, making them more resilient to failures.
C) Incorrect. 2PC requires all services to be available and can be less fault-tolerant compared to other methods.
D) Incorrect. Sagas do not rely on a single coordinator; they use a series of compensating actions managed by each service.

Q#19: (Code-Based Question, Single-Select)

In a Spring Boot application using Resilience4j, how do you configure a retry mechanism for a REST call?

        @Configuration
        public class RetryConfig {
           
           @Bean
           public RetryRegistry retryRegistry() {
              return RetryRegistry.of(RetryConfig.custom().maxAttempts(3).build());
           }
        }

        @Bean
        public Retry retry() {
           return Retry.ofDefaults("myRetry");
        }

        @Retry(name = "myRetry", fallbackMethod = "fallback")
        public String callExternalService() {
            
          // Code to call the external service
        }

        @Retryable
        public String callExternalService() {

           // Code to call the external service
        }

Answer & Explanation:

A) Incorrect. This code configures a Retry Registry, which is not directly related to method-level retry functionality in the context shown.
B) Incorrect. This option shows a way to create a Retry instance but does not integrate it with method-level retry functionality.
C) Correct. This option demonstrates how to use the @Retry annotation from Resilience4j to configure a retry mechanism for a method with a fallback.
D) Incorrect. The @Retryable annotation is not part of Resilience4j; the correct annotation is @Retry.

Q#20: (Scenario-Based Question, Multi-Select)

You are deploying a microservices application on Kubernetes and need to configure autoscaling. Which of the following are correct regarding Kubernetes autoscaling?

A) Horizontal Pod Autoscaler (HPA) adjusts the number of pod replicas based on CPU usage.
B) Vertical Pod Autoscaler (VPA) adjusts the CPU and memory requests of individual pods.
C) Kubernetes does not support autoscaling of pods based on custom metrics.
D) Autoscaling in Kubernetes requires manual intervention for adjusting the number of nodes in a cluster.

Answer & Explanation:

A) Correct. HPA automatically scales the number of pod replicas based on metrics like CPU usage or memory usage.
B) Correct. VPA is used to automatically adjust the CPU and memory requests of pods based on historical resource usage.
C) Incorrect. Kubernetes supports autoscaling based on custom metrics using the Custom Metrics Adapter.
D) Incorrect. Kubernetes can autoscale the number of nodes in a cluster using the Cluster Autoscaler.

Topics Covered in the Practice Tests

Below are the topics which are covered in the Java Microservices Practice Tests & Interview Questions on Udemy.

Key principles of Microservices
Monolithic vs Microservices
gRPC for efficient binary communication
API Gateway pattern
Asynchronous communication (Message Queues like Kafka, RabbitMQ)
OpenFeign for REST clients
Load balancing strategies (Ribbon, OpenFeign Load Balancer)
Database per service pattern
Event sourcing and CQRS patterns
Handling distributed transactions (Sagas, Two-Phase Commit)
Data consistency (eventual consistency, strong consistency)
Caching mechanisms (Redis, Memcached)
Circuit Breaker Pattern (e.g., using Resilience4j)
Retry mechanisms
Bulkhead pattern
Timeout handling
Distributed tracing (Zipkin, Sleuth)
Docker for containerization of microservices
Service discovery with Eureka or Consul
Load balancing strategies (Client-side, Server-side)
API Gateway patterns (Netflix Zuul, Spring Cloud Gateway)
JWT (JSON Web Token)
Role-Based Access Control (RBAC)
Security practices for inter-service communication
Distributed logging (ELK Stack: Elasticsearch, Logstash, Kibana)
Monitoring (Prometheus, Grafana)
Distributed tracing (Zipkin, Jaeger)
Health checks (Spring Boot Actuator)
Jenkins or GitLab for Continuous Integration
Horizontal vs vertical scaling
Stateless vs stateful services
Autoscaling in Kubernetes
Load testing and tuning (Apache JMeter)
Event streaming with Kafka
Event Sourcing and Command Query Responsibility Segregation (CQRS)
Async messaging patterns

Additionally, you can expect update on topics and practice sets accordingly when required.

For conceptual tutorials on Java Microservices, kindly visit Java Microservices Tutorial.

Java Microservices MCQs Practice Test

Java Microservices MCQs Practice Test

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Topics Covered in the Practice Tests

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