{"id":13533,"date":"2026-09-16T09:26:23","date_gmt":"2026-09-16T09:26:23","guid":{"rendered":"https:\/\/www.examlabs.com\/certification\/?p=13533"},"modified":"2026-09-16T09:26:23","modified_gmt":"2026-09-16T09:26:23","slug":"amazon-aws-certified-data-engineer-associate-dea-c01-practice-test-questions-and-exam-dumps-part16-q301-320","status":"publish","type":"post","link":"https:\/\/www.examlabs.com\/certification\/amazon-aws-certified-data-engineer-associate-dea-c01-practice-test-questions-and-exam-dumps-part16-q301-320\/","title":{"rendered":"Amazon AWS Certified Data Engineer – Associate DEA-C01 Practice Test Questions and Exam Dumps Part16 Q301-320"},"content":{"rendered":"

<\/h1>\n

View Full <\/b>Amazon AWS Certified Data Engineer – Associate DEA-C01 Exam Dumps<\/b><\/a> and Practice Test Dumps.<\/b><\/h2>\n

 <\/p>\n

Question 301:<\/b><\/h3>\n

A data engineer needs to process large datasets using Apache Spark and wants to run distributed data processing jobs without managing individual servers. Which AWS service is most appropriate?<\/b><\/p>\n

    \n
  1. Amazon Athena<\/span><\/li>\n
  2. Amazon EMR<\/span><\/li>\n
  3. Amazon S3<\/span><\/li>\n
  4. Amazon EventBridge<\/span><\/li>\n<\/ol>\n

    Correct Answer: 2<\/b><\/p>\n

    Explanation:<\/b><\/p>\n

    Amazon EMR is designed to run big data frameworks such as Apache Spark, Hadoop, Hive, and Trino at scale. It provides managed clusters and infrastructure for distributed data processing. A data engineer can submit Spark jobs to EMR and process large datasets stored in Amazon S3 or other data stores. Athena is primarily a serverless SQL query service and is better suited for interactive queries. S3 provides object storage rather than distributed processing. EventBridge is used for event routing and scheduling. Therefore, EMR is the most appropriate choice when Spark-based distributed processing is required.<\/span><\/p>\n

    Question 302:<\/b><\/h3>\n

    A company wants to automatically track which objects in an Amazon S3 bucket are accessed and determine who accessed them. Which AWS service should the data engineer use?<\/b><\/p>\n

      \n
    1. AWS CloudTrail<\/span><\/li>\n
    2. Amazon Macie<\/span><\/li>\n
    3. Amazon Athena<\/span><\/li>\n
    4. AWS Glue<\/span><\/li>\n<\/ol>\n

      Correct Answer: 1<\/b><\/p>\n

      Explanation:<\/b><\/p>\n

      AWS CloudTrail records API activity and actions performed against AWS resources. For Amazon S3, CloudTrail can provide information about object-level API operations, including actions such as GetObject, PutObject, and DeleteObject when the appropriate data-event logging is enabled. This allows administrators and data engineers to investigate who performed an action and when it occurred. Amazon Macie focuses on sensitive-data discovery, Athena performs SQL queries, and Glue provides data integration and ETL capabilities. Therefore, CloudTrail is the appropriate service for auditing S3 access and identifying the API activity associated with objects.<\/span><\/p>\n

      Question 303:<\/b><\/h3>\n

      A data engineer stores large analytical datasets in Amazon S3. Queries frequently select only a few columns from the datasets. Which file format would provide the best query efficiency?<\/b><\/p>\n

        \n
      1. CSV<\/span><\/li>\n
      2. JSON<\/span><\/li>\n
      3. Apache Parquet<\/span><\/li>\n
      4. XML<\/span><\/li>\n<\/ol>\n

        Correct Answer: 3<\/b><\/p>\n

        Explanation:<\/b><\/p>\n

        Apache Parquet is a columnar storage format optimized for analytical workloads. When a query selects only a subset of columns, engines such as Amazon Athena can read only the required columns instead of scanning every field in each record. Parquet also supports efficient compression and encoding, which can significantly reduce storage consumption and query scanning costs. CSV and JSON are row-oriented text formats and generally require more data to be read during analytical queries. XML is also not optimized for large-scale analytical processing. Therefore, converting analytical datasets to Parquet is an effective way to improve query performance and reduce data-processing costs.<\/span><\/p>\n

        Question 304:<\/b><\/h3>\n

        A company uses AWS Glue jobs to process daily files from Amazon S3. The same files should not be processed repeatedly on every job run. Which Glue feature should be used?<\/b><\/p>\n

          \n
        1. Glue Data Catalog<\/span><\/li>\n
        2. Glue job bookmarks<\/span><\/li>\n
        3. Glue crawlers<\/span><\/li>\n
        4. Glue connections<\/span><\/li>\n<\/ol>\n

          Correct Answer: 2<\/b><\/p>\n

          Explanation:<\/b><\/p>\n

          AWS Glue job bookmarks help track previously processed data so that subsequent job runs can focus on new or changed records rather than processing the same input repeatedly. This is particularly useful for incremental ETL pipelines where new files or records arrive regularly in Amazon S3. The Glue Data Catalog stores metadata, crawlers discover schemas, and connections provide configuration for connecting to external data stores. Job bookmarks are specifically designed to maintain state between job runs and support incremental processing. Therefore, enabling Glue job bookmarks is the appropriate solution when previously processed files should not be processed again.<\/span><\/p>\n

          Question 305:<\/b><\/h3>\n

          A data engineer needs to query Amazon S3 data using standard SQL without provisioning or managing servers. Which service should be selected?<\/b><\/p>\n

            \n
          1. Amazon EMR<\/span><\/li>\n
          2. Amazon Redshift<\/span><\/li>\n
          3. Amazon Athena<\/span><\/li>\n
          4. Amazon RDS<\/span><\/li>\n<\/ol>\n

            Correct Answer: 3<\/b><\/p>\n

            Explanation:<\/b><\/p>\n

            Amazon Athena is a serverless interactive query service that allows users to analyze data stored in Amazon S3 using standard SQL. The data engineer does not need to provision database servers or manage infrastructure. Athena works particularly well with structured and semi-structured datasets stored in formats such as Parquet, ORC, JSON, and CSV. Amazon EMR is designed for managed big-data frameworks, Redshift is a data warehouse, and RDS provides managed relational databases. Therefore, Athena is the most appropriate choice when the requirement is to directly query S3 data with SQL while avoiding infrastructure management.<\/span><\/p>\n

            Question 306:<\/b><\/h3>\n

            A company wants to continuously capture changes from an Amazon RDS PostgreSQL database and replicate those changes to Amazon S3. Which AWS service should be used?<\/b><\/p>\n

              \n
            1. AWS Database Migration Service (AWS DMS)<\/span><\/li>\n
            2. Amazon EventBridge<\/span><\/li>\n
            3. AWS Glue Data Catalog<\/span><\/li>\n
            4. Amazon CloudWatch<\/span><\/li>\n<\/ol>\n

              Correct Answer: 1<\/b><\/p>\n

              Explanation:<\/b><\/p>\n

              AWS Database Migration Service supports ongoing replication using change data capture (CDC). A DMS task can initially load existing data from an RDS PostgreSQL database into Amazon S3 and then continuously capture subsequent changes. This approach is useful for building data lakes and keeping S3 datasets synchronized with operational databases. EventBridge is primarily an event-routing service, Glue Data Catalog manages metadata, and CloudWatch provides monitoring and observability. Therefore, AWS DMS is the appropriate service for continuously replicating database changes from PostgreSQL to Amazon S3 using CDC.<\/span><\/p>\n

              Question 307:<\/b><\/h3>\n

              A streaming application requires multiple independent consumers to read records from the same Amazon Kinesis Data Streams stream with dedicated read throughput. Which feature should be used?<\/b><\/p>\n

                \n
              1. Kinesis Data Firehose<\/span><\/li>\n
              2. Enhanced fan-out<\/span><\/li>\n
              3. S3 Transfer Acceleration<\/span><\/li>\n
              4. Athena workgroups<\/span><\/li>\n<\/ol>\n

                Correct Answer: 2<\/b><\/p>\n

                Explanation:<\/b><\/p>\n

                Amazon Kinesis Data Streams enhanced fan-out provides dedicated read throughput for registered consumer applications. This allows multiple consumers to read from the same stream without competing for the standard shared read throughput. It is especially useful when several applications need to process the same real-time stream independently and require predictable performance. Kinesis Data Firehose is designed primarily for delivering streaming data to destinations such as S3, Redshift, and OpenSearch. S3 Transfer Acceleration concerns object uploads, while Athena workgroups manage query environments. Therefore, enhanced fan-out is the correct feature for dedicated consumer throughput.<\/span><\/p>\n

                Question 308:<\/b><\/h3>\n

                A data engineer wants to automatically divide incoming Firehose records into different Amazon S3 prefixes based on record attributes. Which feature should be configured?<\/b><\/p>\n

                  \n
                1. S3 Versioning<\/span><\/li>\n
                2. Firehose dynamic partitioning<\/span><\/li>\n
                3. S3 Object Lock<\/span><\/li>\n
                4. Athena partition projection<\/span><\/li>\n<\/ol>\n

                  Correct Answer: 2<\/b><\/p>\n

                  Explanation:<\/b><\/p>\n

                  Amazon Data Firehose dynamic partitioning allows streaming records to be automatically delivered to different Amazon S3 prefixes based on values within the records. For example, records can be organized by customer, region, or event date without requiring a separate application to manually determine the destination prefix. This can improve the organization and query efficiency of data stored in S3. S3 Versioning maintains multiple object versions, Object Lock protects objects from deletion or modification, and Athena partition projection helps Athena infer partition values during queries. Therefore, Firehose dynamic partitioning is the correct feature for dynamically organizing delivered streaming records.<\/span><\/p>\n

                  Question 309:<\/b><\/h3>\n

                  A data warehouse contains a very large Amazon Redshift table. Queries frequently join the table with another large table using the same customer_id column. Which design consideration can reduce data movement during joins?<\/b><\/p>\n

                    \n
                  1. Use a suitable distribution key<\/span><\/li>\n
                  2. Disable compression<\/span><\/li>\n
                  3. Store the table as CSV<\/span><\/li>\n
                  4. Remove the sort key<\/span><\/li>\n<\/ol>\n

                    Correct Answer: 1<\/b><\/p>\n

                    Explanation:<\/b><\/p>\n

                    Amazon Redshift distribution determines how table rows are distributed across compute nodes. Selecting a suitable distribution key can place related rows on the same nodes, reducing the amount of data that must move across nodes during joins. When large tables are frequently joined using the same column, such as customer_id, an appropriate distribution strategy can improve query performance. Compression and file format considerations do not directly solve inter-node data movement in Redshift. Removing sort keys can also negatively affect query performance. Therefore, choosing an appropriate distribution key is an important Redshift optimization technique for large join operations.<\/span><\/p>\n

                    Question 310:<\/b><\/h3>\n

                    A data engineer needs to store frequently accessed application data in memory to reduce latency for repeated reads. Which AWS service is most appropriate?<\/b><\/p>\n

                      \n
                    1. Amazon RDS<\/span><\/li>\n
                    2. Amazon ElastiCache<\/span><\/li>\n
                    3. Amazon S3 Glacier<\/span><\/li>\n
                    4. AWS Glue<\/span><\/li>\n<\/ol>\n

                      Correct Answer: 2<\/b><\/p>\n

                      Explanation:<\/b><\/p>\n

                      Amazon ElastiCache provides managed in-memory caching using technologies such as Redis and Memcached. It can store frequently accessed data in memory, allowing applications to retrieve values much faster than repeatedly querying a persistent database. This can reduce database load and improve application response times. Amazon RDS is a managed relational database service, S3 Glacier is intended for archival storage, and AWS Glue is used for data integration and ETL workloads. Therefore, ElastiCache is the most appropriate service when the requirement is to provide low-latency access to frequently requested data through an in-memory cache.<\/span><\/p>\n

                      Question 311:<\/b><\/h3>\n

                      A company wants to catalog tables stored in Amazon S3 and make their metadata available to services such as Amazon Athena. Which component should be used?<\/b><\/p>\n

                        \n
                      1. AWS Glue Data Catalog<\/span><\/li>\n
                      2. Amazon CloudWatch<\/span><\/li>\n
                      3. AWS Secrets Manager<\/span><\/li>\n
                      4. Amazon EventBridge<\/span><\/li>\n<\/ol>\n

                        Correct Answer: 1<\/b><\/p>\n

                        Explanation:<\/b><\/p>\n

                        The AWS Glue Data Catalog is a centralized metadata repository that stores information about datasets, tables, schemas, and partitions. Services such as Amazon Athena can use this catalog to understand the structure and location of data stored in Amazon S3. Glue crawlers can automatically discover schemas and populate catalog tables. CloudWatch focuses on monitoring and observability, Secrets Manager securely stores credentials and secrets, and EventBridge handles event routing and scheduling. Therefore, the Glue Data Catalog is the correct component for maintaining metadata that can be consumed by Athena and other AWS analytics services.<\/span><\/p>\n

                        Question 312:<\/b><\/h3>\n

                        A data engineer wants to query an Amazon Redshift table without copying the data into another system and needs to return only rows that match a specified condition. Which SQL clause should be used?<\/b><\/p>\n

                          \n
                        1. ORDER BY<\/span><\/li>\n
                        2. GROUP BY<\/span><\/li>\n
                        3. WHERE<\/span><\/li>\n
                        4. UNION<\/span><\/li>\n<\/ol>\n

                          Correct Answer: 3<\/b><\/p>\n

                          Explanation:<\/b><\/p>\n

                          The SQL WHERE clause filters rows based on a specified condition. For example, a query can use <\/span>WHERE region = ‘US’<\/span> to return only records where the region column matches the required value. ORDER BY sorts query results, GROUP BY aggregates rows into groups, and UNION combines the results of multiple queries. Filtering data early can also reduce the amount of data that needs to be processed by the query engine. Therefore, when a data engineer needs to return only records satisfying a particular condition, the WHERE clause is the appropriate SQL construct.<\/span><\/p>\n

                          Question 313:<\/b><\/h3>\n

                          A company wants to enforce permissions across a data lake based on business classifications such as \u201cConfidential\u201d and \u201cPublic.\u201d Which AWS Lake Formation capability is best suited for this requirement?<\/b><\/p>\n

                            \n
                          1. S3 Lifecycle policies<\/span><\/li>\n
                          2. LF-tags<\/span><\/li>\n
                          3. S3 Transfer Acceleration<\/span><\/li>\n
                          4. CloudWatch alarms<\/span><\/li>\n<\/ol>\n

                            Correct Answer: 2<\/b><\/p>\n

                            Explanation:<\/b><\/p>\n

                            AWS Lake Formation LF-tags provide a tag-based approach for managing permissions across data lake resources. Organizations can assign business classifications or categories to databases, tables, or columns and then create permissions based on those tags. This can simplify governance when many datasets share similar security classifications. S3 Lifecycle policies manage object transitions and expiration, Transfer Acceleration improves data transfer performance, and CloudWatch alarms monitor metrics or events. Therefore, LF-tags are the appropriate Lake Formation capability for implementing scalable, classification-based access control across data lake resources.<\/span><\/p>\n

                            Question 314:<\/b><\/h3>\n

                            A data engineer needs to protect sensitive credentials used by an ETL application and wants automatic secret rotation capabilities. Which AWS service should be used?<\/b><\/p>\n

                              \n
                            1. Amazon S3<\/span><\/li>\n
                            2. AWS Secrets Manager<\/span><\/li>\n
                            3. Amazon Athena<\/span><\/li>\n
                            4. Amazon Kinesis Data Streams<\/span><\/li>\n<\/ol>\n

                              Correct Answer: 2<\/b><\/p>\n

                              Explanation:<\/b><\/p>\n

                              AWS Secrets Manager is designed to securely store and manage sensitive information such as database credentials, API keys, and passwords. It supports secret rotation, allowing credentials to be changed regularly without requiring sensitive values to be embedded directly in application code or configuration files. Amazon S3 is object storage, Athena provides SQL analytics, and Kinesis Data Streams handles real-time streaming data. Using Secrets Manager improves security by centralizing credential management and reducing the risk of exposing credentials in source code. Therefore, Secrets Manager is the most appropriate solution for securely storing and rotating ETL application credentials.<\/span><\/p>\n

                              Question 315:<\/b><\/h3>\n

                              A company needs to automatically detect personally identifiable information (PII) in Amazon S3 data. Which AWS service should the data engineer use?<\/b><\/p>\n

                                \n
                              1. Amazon Macie<\/span><\/li>\n
                              2. Amazon Redshift<\/span><\/li>\n
                              3. AWS DMS<\/span><\/li>\n
                              4. Amazon EventBridge<\/span><\/li>\n<\/ol>\n

                                Correct Answer: 1<\/b><\/p>\n

                                Explanation:<\/b><\/p>\n

                                Amazon Macie is designed to discover and help protect sensitive data stored in Amazon S3. It can use machine learning and pattern matching to identify sensitive information, including various types of personally identifiable information. Macie can help organizations understand where sensitive data exists and identify potentially risky S3 configurations. Redshift is a data warehouse, DMS handles database migration and replication, and EventBridge routes events. Therefore, Amazon Macie is the correct service when the primary requirement is discovering sensitive or PII data in S3.<\/span><\/p>\n

                                Question 316:<\/b><\/h3>\n

                                A data pipeline should execute several AWS services in a specific sequence and automatically handle failures and retries. Which service is most suitable?<\/b><\/p>\n

                                  \n
                                1. Amazon S3<\/span><\/li>\n
                                2. AWS Step Functions<\/span><\/li>\n
                                3. Amazon Macie<\/span><\/li>\n
                                4. Amazon CloudFront<\/span><\/li>\n<\/ol>\n

                                  Correct Answer: 2<\/b><\/p>\n

                                  Explanation:<\/b><\/p>\n

                                  AWS Step Functions provides serverless workflow orchestration and allows a data engineer to define a sequence of tasks using state machines. Individual steps can invoke services such as Lambda, Glue, ECS, and other AWS services. Step Functions also supports retry and catch behavior, allowing workflows to recover from transient failures or route failed executions to appropriate error-handling paths. S3 is storage, Macie discovers sensitive data, and CloudFront is a content delivery service. Therefore, Step Functions is the most suitable choice when a data pipeline requires ordered execution, failure handling, and workflow orchestration.<\/span><\/p>\n

                                  Question 317:<\/b><\/h3>\n

                                  A data engineer needs to monitor AWS Glue job failures and automatically notify an operations team. Which combination is most appropriate?<\/b><\/p>\n

                                    \n
                                  1. CloudWatch and Amazon SNS<\/span><\/li>\n
                                  2. S3 and Athena<\/span><\/li>\n
                                  3. Macie and DMS<\/span><\/li>\n
                                  4. Redshift and ElastiCache<\/span><\/li>\n<\/ol>\n

                                    Correct Answer: 1<\/b><\/p>\n

                                    Explanation:<\/b><\/p>\n

                                    Amazon CloudWatch can monitor AWS Glue job metrics and events, while Amazon SNS can distribute notifications to subscribed users or systems. A CloudWatch alarm or event-driven configuration can detect relevant Glue job failures and send a notification through SNS. This provides an automated monitoring and alerting mechanism for data pipeline operations. S3 and Athena are primarily storage and analytics services, while Macie and DMS address data security and migration requirements. Redshift and ElastiCache serve analytical and caching workloads. Therefore, CloudWatch combined with SNS is the appropriate solution for notifying the operations team about Glue job failures.<\/span><\/p>\n

                                    Question 318:<\/b><\/h3>\n

                                    A company wants to ensure that an S3 object cannot be deleted or overwritten for a specified retention period. Which S3 capability should be configured?<\/b><\/p>\n

                                      \n
                                    1. S3 Transfer Acceleration<\/span><\/li>\n
                                    2. S3 Object Lock<\/span><\/li>\n
                                    3. S3 Select<\/span><\/li>\n
                                    4. S3 Inventory<\/span><\/li>\n<\/ol>\n

                                      Correct Answer: 2<\/b><\/p>\n

                                      Explanation:<\/b><\/p>\n

                                      Amazon S3 Object Lock helps protect objects from deletion or overwrite for a defined retention period. It is commonly used for compliance, regulatory, and records-retention requirements. Object Lock supports retention modes that control how objects can be modified or deleted during the retention period. S3 Transfer Acceleration improves transfer performance, S3 Select allows applications to retrieve subsets of object data, and S3 Inventory provides reports about objects and their metadata. Therefore, S3 Object Lock is the appropriate capability when objects must remain protected from deletion or overwrite for a specified period.<\/span><\/p>\n

                                      Question 319:<\/b><\/h3>\n

                                      A data engineer wants to improve the performance of an Amazon Athena query by avoiding unnecessary scanning of partitions. Which approach should be used?<\/b><\/p>\n

                                        \n
                                      1. Remove partition columns from the query<\/span><\/li>\n
                                      2. Use predicates on partition columns<\/span><\/li>\n
                                      3. Convert Parquet files to CSV<\/span><\/li>\n
                                      4. Disable compression<\/span><\/li>\n<\/ol>\n

                                        Correct Answer: 2<\/b><\/p>\n

                                        Explanation:<\/b><\/p>\n

                                        Athena can reduce the amount of data scanned when queries include predicates that restrict partition columns. For example, if data is partitioned by year and month, filtering with conditions such as <\/span>year = 2026 AND month = 9<\/span> allows Athena to read only the relevant partitions instead of scanning the entire dataset. This can significantly improve query performance and reduce Athena query costs because charges are based largely on data scanned. Removing partition predicates defeats this optimization, while converting Parquet to CSV and disabling compression generally increase the amount of data that must be scanned.<\/span><\/p>\n

                                        Question 320:<\/b><\/h3>\n

                                        A company receives malformed records from a real-time streaming source. The valid records should continue through the pipeline while invalid records must be isolated for later investigation. What is the best design?<\/b><\/p>\n

                                          \n
                                        1. Discard all records when one record fails<\/span><\/li>\n
                                        2. Store invalid records in a quarantine or dead-letter destination<\/span><\/li>\n
                                        3. Stop the entire streaming application permanently<\/span><\/li>\n
                                        4. Delete the source stream<\/span><\/li>\n<\/ol>\n

                                          Correct Answer: 4<\/b><\/p>\n

                                          Explanation:<\/b><\/p>\n

                                          A robust streaming pipeline should isolate malformed or invalid records rather than allowing them to stop processing for valid data. A quarantine or dead-letter destination can store failed records along with useful metadata so they can be inspected, corrected, and potentially reprocessed later. This approach improves pipeline resilience and prevents a small number of bad records from affecting the entire workload. Discarding records can result in data loss, permanently stopping the pipeline reduces availability, and deleting the source stream is not an appropriate error-handling strategy. Therefore, isolating invalid records in a quarantine or dead-letter destination is the recommended design.<\/span><\/p>\n

                                           <\/p>\n","protected":false},"excerpt":{"rendered":"

                                          View Full Amazon AWS Certified Data Engineer – Associate DEA-C01 Exam Dumps and Practice Test Dumps.   Question 301: A data engineer needs to process large datasets using Apache Spark and wants to run distributed data processing jobs without managing individual servers. Which AWS service is most appropriate? Amazon Athena Amazon EMR Amazon S3 Amazon […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[1648,1647],"tags":[],"_links":{"self":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/13533"}],"collection":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/comments?post=13533"}],"version-history":[{"count":1,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/13533\/revisions"}],"predecessor-version":[{"id":13543,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/13533\/revisions\/13543"}],"wp:attachment":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/media?parent=13533"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/categories?post=13533"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/tags?post=13533"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}