{"id":13531,"date":"2026-09-16T09:26:50","date_gmt":"2026-09-16T09:26:50","guid":{"rendered":"https:\/\/www.examlabs.com\/certification\/?p=13531"},"modified":"2026-09-16T09:26:50","modified_gmt":"2026-09-16T09:26:50","slug":"amazon-aws-certified-data-engineer-associate-dea-c01-practice-test-questions-and-exam-dumps-part14-q261-280","status":"publish","type":"post","link":"https:\/\/www.examlabs.com\/certification\/amazon-aws-certified-data-engineer-associate-dea-c01-practice-test-questions-and-exam-dumps-part14-q261-280\/","title":{"rendered":"Amazon AWS Certified Data Engineer – Associate DEA-C01 Practice Test Questions and Exam Dumps Part14 Q261-280"},"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 261:<\/b><\/h3>\n

A data engineer needs to ingest data from an on-premises PostgreSQL database into Amazon S3 on a recurring basis. The source database must remain available during the process. Which AWS service is MOST appropriate?<\/b><\/p>\n

    \n
  1. Amazon Athena<\/span><\/li>\n
  2. AWS Database Migration Service (AWS DMS)<\/span><\/li>\n
  3. Amazon QuickSight<\/span><\/li>\n
  4. Amazon CloudWatch<\/span><\/li>\n<\/ol>\n

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

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

    AWS Database Migration Service is designed to migrate and replicate data from databases while keeping the source available. AWS DMS supports PostgreSQL and can use a full-load task followed by Change Data Capture (CDC) to continuously replicate changes. This makes it suitable when data must be moved from an operational database into Amazon S3 without taking the source offline. Athena is used to query data, QuickSight provides visualization, and CloudWatch provides monitoring. Therefore, AWS DMS is the most appropriate service for recurring database ingestion where the source system must remain operational.<\/span><\/p>\n

    Question 262:<\/b><\/h3>\n

    A company stores billions of objects in Amazon S3 and needs to identify objects that have not been accessed for a long period so they can reduce storage costs. Which feature should the data engineer consider?<\/b><\/p>\n

      \n
    1. S3 Access Points<\/span><\/li>\n
    2. S3 Object Lock<\/span><\/li>\n
    3. S3 Storage Class Analysis<\/span><\/li>\n
    4. S3 Transfer Acceleration<\/span><\/li>\n<\/ol>\n

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

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

      S3 Storage Class Analysis helps organizations understand access patterns for objects stored in Amazon S3. The analysis can provide information that helps determine whether data may benefit from moving to a more cost-effective storage class. This is especially useful for large datasets where access patterns are not well understood. S3 Access Points manage access to shared datasets, Object Lock protects objects from deletion or modification, and Transfer Acceleration improves transfer performance. Storage Class Analysis is therefore the most relevant feature when the goal is to analyze object access patterns and identify opportunities to reduce S3 storage costs.<\/span><\/p>\n

      Question 263:<\/b><\/h3>\n

      A data engineer is processing streaming records with Amazon Kinesis Data Streams. The application needs records for the same customer to be processed in order. Which approach should be used?<\/b><\/p>\n

        \n
      1. Use the customer ID as the partition key<\/span><\/li>\n
      2. Use a random partition key for every record<\/span><\/li>\n
      3. Disable Kinesis shard processing<\/span><\/li>\n
      4. Store all records in Amazon S3 first<\/span><\/li>\n<\/ol>\n

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

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

        Amazon Kinesis Data Streams uses partition keys to determine which shard receives a record. Records with the same partition key are routed to the same shard, where their order can be maintained. Using the customer ID as the partition key therefore ensures that records belonging to the same customer are directed to the same shard, helping preserve ordering for that customer’s events. Random partition keys can distribute records but do not guarantee that events for one customer stay together. S3 storage is not required for ordering. Therefore, using the customer ID as the partition key is the appropriate solution.<\/span><\/p>\n

        Question 264:<\/b><\/h3>\n

        A company wants to ensure that an AWS Glue job can access an Amazon S3 bucket securely without storing long-term AWS credentials in the job code. Which approach should be used?<\/b><\/p>\n

          \n
        1. Store an access key in the Glue script<\/span><\/li>\n
        2. Use an IAM role for the Glue job<\/span><\/li>\n
        3. Put credentials in an S3 object<\/span><\/li>\n
        4. Store credentials in a CSV file<\/span><\/li>\n<\/ol>\n

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

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

          AWS Glue jobs should use IAM roles to obtain temporary AWS credentials when accessing AWS resources. An IAM role can be attached to the Glue job and granted only the permissions required to access specific S3 buckets, KMS keys, or other services. This follows the principle of least privilege and avoids embedding long-term access keys in code or configuration files. Storing credentials in scripts, S3 objects, or CSV files creates unnecessary security risks and makes credential management more difficult. Therefore, using an IAM role for the Glue job is the recommended and secure approach.<\/span><\/p>\n

          Question 265:<\/b><\/h3>\n

          A data engineer wants to improve Amazon Athena performance by reducing the amount of data read from a large Parquet dataset. The query frequently requests only three columns from a table containing 50 columns. Which capability helps most?<\/b><\/p>\n

            \n
          1. S3 Versioning<\/span><\/li>\n
          2. Column pruning<\/span><\/li>\n
          3. S3 Object Lock<\/span><\/li>\n
          4. Data replication<\/span><\/li>\n<\/ol>\n

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

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

            Column pruning allows a query engine such as Amazon Athena to read only the columns required by the query instead of processing every column in the dataset. This is especially effective with columnar formats such as Parquet and ORC. If a table contains 50 columns but a query requires only three, Athena can avoid scanning the other columns, reducing data scanned and potentially lowering query costs. S3 Versioning, Object Lock, and data replication do not reduce the columns read during a query. Therefore, column pruning is the most appropriate optimization for this workload.<\/span><\/p>\n

            Question 266:<\/b><\/h3>\n

            A company needs to run an AWS Glue job whenever a new file matching a specific pattern is added to Amazon S3. Which service can provide the event routing and filtering capability?<\/b><\/p>\n

              \n
            1. Amazon EventBridge<\/span><\/li>\n
            2. Amazon Macie<\/span><\/li>\n
            3. Amazon Athena<\/span><\/li>\n
            4. AWS Secrets Manager<\/span><\/li>\n<\/ol>\n

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

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

              Amazon EventBridge can receive events related to Amazon S3 object creation and apply event-pattern filtering before triggering a target service. A rule can be configured to respond only to events from a particular bucket or matching specific object characteristics. The matching event can then trigger an AWS Glue job or another processing workflow. Macie focuses on sensitive-data discovery, Athena provides SQL querying, and Secrets Manager manages credentials. EventBridge therefore provides the event-driven routing and filtering capability needed to start the Glue job only when relevant S3 objects are created.<\/span><\/p>\n

              Question 267:<\/b><\/h3>\n

              A data engineer needs to maintain a historical record of changes made to objects in an Amazon S3 bucket so previous versions can be recovered. Which feature should be enabled?<\/b><\/p>\n

                \n
              1. S3 Lifecycle<\/span><\/li>\n
              2. S3 Versioning<\/span><\/li>\n
              3. S3 Transfer Acceleration<\/span><\/li>\n
              4. S3 Select<\/span><\/li>\n<\/ol>\n

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

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

                Amazon S3 Versioning maintains multiple versions of an object in the same bucket. When an object is overwritten or deleted, previous versions can remain available, allowing organizations to recover earlier data. Versioning is useful for protecting against accidental overwrites or deletions and for maintaining historical object versions. S3 Lifecycle manages object transitions and expiration, Transfer Acceleration improves transfer speeds, and S3 Select allows applications to retrieve subsets of object data. Therefore, S3 Versioning is the appropriate feature when the requirement is to retain and recover previous versions of S3 objects.<\/span><\/p>\n

                Question 268:<\/b><\/h3>\n

                An Amazon Athena query uses a partitioned table but still scans excessive data. The query filters on a partition column using a function that prevents effective partition pruning. What should the engineer do?<\/b><\/p>\n

                  \n
                1. Remove all partitions<\/span><\/li>\n
                2. Convert the table to JSON<\/span><\/li>\n
                3. Rewrite the query to use direct partition predicates<\/span><\/li>\n
                4. Disable compression<\/span><\/li>\n<\/ol>\n

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

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

                  Partition pruning works most effectively when Athena can directly determine which partition values are required from the query predicate. Applying functions or transformations to partition columns can sometimes prevent the query engine from efficiently identifying the relevant partitions. Rewriting the query to use direct predicates on partition columns can allow Athena to prune unnecessary partitions and reduce scanned data. Removing partitions or disabling compression would generally increase costs, while converting the dataset to JSON would usually reduce analytical efficiency. Therefore, rewriting the query so it directly filters the partition columns is the best approach.<\/span><\/p>\n

                  Question 269:<\/b><\/h3>\n

                  A data pipeline should retry a failed API call several times, waiting longer between each attempt. Which retry strategy is MOST appropriate?<\/b><\/p>\n

                    \n
                  1. Immediate infinite retries<\/span><\/li>\n
                  2. Fixed retries with no delay<\/span><\/li>\n
                  3. Exponential backoff<\/span><\/li>\n
                  4. Disable error handling<\/span><\/li>\n<\/ol>\n

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

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

                    Exponential backoff increases the waiting time between successive retry attempts. For example, a system might wait one second before the first retry, two seconds before the second, four seconds before the third, and so on. This reduces pressure on an overloaded or temporarily unavailable service and provides time for transient failures to recover. Exponential backoff is commonly used in distributed systems and AWS workflows. Immediate retries can increase load during an outage, while infinite retries can prevent workflows from completing. Disabling error handling provides no recovery mechanism. Therefore, exponential backoff is the recommended retry strategy.<\/span><\/p>\n

                    Question 270:<\/b><\/h3>\n

                    A data engineer wants to ensure that only authorized analysts can query specific datasets in an AWS data lake. Which AWS service provides centralized data lake permissions and governance?<\/b><\/p>\n

                      \n
                    1. Amazon CloudWatch<\/span><\/li>\n
                    2. AWS Lake Formation<\/span><\/li>\n
                    3. Amazon Kinesis Data Firehose<\/span><\/li>\n
                    4. Amazon Route 53<\/span><\/li>\n<\/ol>\n

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

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

                      AWS Lake Formation provides centralized governance and fine-grained access control for data lakes built on AWS. It can help administrators manage permissions for databases, tables, columns, and other data resources while integrating with services such as Amazon Athena. This allows organizations to provide analysts with access to only the datasets or fields they are authorized to use. CloudWatch focuses on monitoring, Kinesis Data Firehose handles streaming data delivery, and Route 53 provides DNS services. Therefore, Lake Formation is the most appropriate service for centralized data lake permissions and governance.<\/span><\/p>\n

                      Question 271:<\/b><\/h3>\n

                      A company wants to convert raw JSON files stored in Amazon S3 into compressed Parquet files for analytical workloads. Which AWS service can perform this transformation as part of a managed ETL pipeline?<\/b><\/p>\n

                        \n
                      1. AWS Glue<\/span><\/li>\n
                      2. Amazon Route 53<\/span><\/li>\n
                      3. AWS Secrets Manager<\/span><\/li>\n
                      4. Amazon CloudWatch<\/span><\/li>\n<\/ol>\n

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

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

                        AWS Glue provides managed ETL capabilities that can read raw data from Amazon S3, transform it, and write the results in optimized formats such as Parquet. A Glue job can parse JSON records, apply transformations, convert data types, and write compressed Parquet files to an S3 destination. Parquet is well suited for analytical workloads because it is columnar and supports efficient column pruning. Route 53 provides DNS functionality, Secrets Manager manages credentials, and CloudWatch handles monitoring. Therefore, AWS Glue is the appropriate managed service for converting raw JSON into compressed Parquet as part of an ETL pipeline.<\/span><\/p>\n

                        Question 272:<\/b><\/h3>\n

                        A data engineer wants to monitor an AWS Glue job and receive an alert when the job fails. Which AWS service should be used to create the alerting mechanism?<\/b><\/p>\n

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

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

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

                          Amazon CloudWatch provides monitoring and alerting capabilities for AWS resources and applications. A data engineer can use CloudWatch metrics, logs, and events to monitor AWS Glue jobs and configure alarms or event-driven notifications when failures occur. This allows the team to respond quickly to pipeline problems. Athena is designed for SQL-based analytics, S3 provides object storage, and Macie focuses on sensitive-data discovery. CloudWatch can also integrate with Amazon SNS or EventBridge to deliver notifications or trigger automated responses. Therefore, CloudWatch is the most appropriate service for monitoring Glue jobs and generating failure alerts.<\/span><\/p>\n

                          Question 273:<\/b><\/h3>\n

                          A company needs to securely transfer data from an on-premises network to AWS over a private network connection. Which AWS service can provide a dedicated private connection?<\/b><\/p>\n

                            \n
                          1. AWS Direct Connect<\/span><\/li>\n
                          2. Amazon Athena<\/span><\/li>\n
                          3. Amazon Macie<\/span><\/li>\n
                          4. Amazon Kinesis Data Firehose<\/span><\/li>\n<\/ol>\n

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

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

                            AWS Direct Connect provides a dedicated network connection between an organization’s on-premises environment and AWS. It can provide more consistent network performance and private connectivity compared with using the public internet. Direct Connect is commonly used by organizations that need reliable connectivity for large data transfers, hybrid architectures, or enterprise workloads. Athena is a query service, Macie focuses on sensitive-data discovery, and Kinesis Data Firehose delivers streaming data. Therefore, AWS Direct Connect is the appropriate service when the requirement is to establish a dedicated private connection between an on-premises environment and AWS.<\/span><\/p>\n

                            Question 274:<\/b><\/h3>\n

                            A data engineer needs to store semi-structured documents with flexible schemas and retrieve them using a unique key with very low latency. Which AWS service is MOST appropriate?<\/b><\/p>\n

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

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

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

                              Amazon DynamoDB is a fully managed NoSQL database designed for low-latency access at scale. It supports flexible schemas and is well suited for storing semi-structured application data such as JSON-like documents. Items can be efficiently retrieved using primary keys, and DynamoDB automatically manages much of the underlying infrastructure. Amazon Redshift is designed for analytical data warehousing, Athena is used for interactive querying of data sources such as S3, and CloudWatch provides monitoring and logging. Therefore, DynamoDB is the best fit for semi-structured documents that require predictable, low-latency key-based access.<\/span><\/p>\n

                              Question 275:<\/b><\/h3>\n

                              A company wants to detect unauthorized API activity and maintain an audit history of AWS service calls made in its account. Which AWS service should be enabled?<\/b><\/p>\n

                                \n
                              1. AWS CloudTrail<\/span><\/li>\n
                              2. Amazon Athena<\/span><\/li>\n
                              3. Amazon Kinesis Data Firehose<\/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 in an AWS account. It can provide information about who performed an action, which AWS service was accessed, when the action occurred, and other event details. This makes CloudTrail useful for security investigations, compliance auditing, and detecting unauthorized or unexpected activity. Athena can query data, Kinesis Data Firehose delivers streaming records, and Glue performs data integration and transformation. CloudTrail logs can also be delivered to Amazon S3 for long-term storage and analysis. Therefore, AWS CloudTrail is the appropriate service for maintaining an audit history of AWS API activity.<\/span><\/p>\n

                                Question 276:<\/b><\/h3>\n

                                A data engineer wants to reduce the cost of storing infrequently accessed Amazon S3 objects while keeping them available for future retrieval. Which storage class is designed for automatically optimizing storage costs based on changing access patterns?<\/b><\/p>\n

                                  \n
                                1. S3 Standard<\/span><\/li>\n
                                2. S3 Intelligent-Tiering<\/span><\/li>\n
                                3. S3 One Zone-IA<\/span><\/li>\n
                                4. S3 Glacier Deep Archive<\/span><\/li>\n<\/ol>\n

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

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

                                  Amazon S3 Intelligent-Tiering is designed to automatically optimize storage costs as object access patterns change. It can move objects between access tiers based on how frequently they are accessed while keeping the objects available without requiring the data engineer to manually determine when each object should transition. S3 Standard is intended for frequently accessed data, One Zone-IA is designed for infrequently accessed data stored in a single Availability Zone, and Glacier Deep Archive is optimized for very long-term archival. Therefore, S3 Intelligent-Tiering is the best choice when access patterns are unpredictable or changing.<\/span><\/p>\n

                                  Question 277:<\/b><\/h3>\n

                                  A data pipeline processes records from an external source. Some records fail validation because required fields are missing. The company wants to retain these records separately for later investigation. What should the data engineer implement?<\/b><\/p>\n

                                    \n
                                  1. Delete all invalid records permanently<\/span><\/li>\n
                                  2. Send invalid records to a quarantine or dead-letter location<\/span><\/li>\n
                                  3. Stop the entire pipeline permanently<\/span><\/li>\n
                                  4. Ignore validation failures<\/span><\/li>\n<\/ol>\n

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

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

                                    A quarantine or dead-letter location allows invalid records to be separated from successfully processed data while preserving them for investigation and possible reprocessing. This approach prevents bad records from contaminating downstream datasets while ensuring that potentially recoverable data is not permanently lost. The data engineering team can inspect the failed records, identify the reason for validation failure, correct the source or transformation logic, and replay the records when appropriate. Permanently deleting invalid records can cause data loss, while ignoring validation failures can compromise data quality. Therefore, using a quarantine or dead-letter location is the best approach.<\/span><\/p>\n

                                    Question 278:<\/b><\/h3>\n

                                    A company needs to query data in Amazon S3 but wants Athena to automatically infer partitions from the S3 path without maintaining a large partition metadata list manually. Which Athena capability can help?<\/b><\/p>\n

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

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

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

                                      Athena partition projection allows partition information to be calculated based on configured rules rather than requiring every partition to be individually stored in the AWS Glue Data Catalog. This can be useful for datasets with a large number of predictable partitions, such as date-based S3 paths. Athena can determine the relevant partition locations when a query includes appropriate predicates. S3 Versioning maintains object versions, Object Lock provides retention protection, and CloudTrail records AWS API activity. Therefore, Athena partition projection is the appropriate capability when the goal is to reduce manual partition metadata management for predictable datasets.<\/span><\/p>\n

                                      Question 279:<\/b><\/h3>\n

                                      A data engineer needs to send transformed streaming data to Amazon S3 and wants the service to buffer records before writing objects to reduce the number of small files. Which service provides this capability?<\/b><\/p>\n

                                        \n
                                      1. Amazon RDS<\/span><\/li>\n
                                      2. Amazon Kinesis Data Firehose<\/span><\/li>\n
                                      3. Amazon Redshift<\/span><\/li>\n
                                      4. AWS Lake Formation<\/span><\/li>\n<\/ol>\n

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

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

                                        Amazon Kinesis Data Firehose automatically buffers incoming streaming records based on configured buffering conditions before delivering them to destinations such as Amazon S3. Buffering allows Firehose to combine records into larger objects rather than creating an individual S3 object for every incoming event. This can help reduce small-file problems in downstream analytics workloads. Firehose can also transform records using AWS Lambda before delivery. RDS is a relational database service, Redshift is an analytical data warehouse, and Lake Formation provides governance. Therefore, Kinesis Data Firehose is the best service for managed streaming delivery with buffering to S3.<\/span><\/p>\n

                                        Question 280:<\/b><\/h3>\n

                                        A company wants to ensure that an ETL application has access only to the specific Amazon S3 bucket and KMS key it needs. Which security principle should the data engineer follow when creating the IAM policy?<\/b><\/p>\n

                                          \n
                                        1. Grant administrator access<\/span><\/li>\n
                                        2. Allow all AWS services<\/span><\/li>\n
                                        3. Use the principle of least privilege<\/span><\/li>\n
                                        4. Use anonymous access<\/span><\/li>\n<\/ol>\n

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

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

                                          The principle of least privilege means granting an application or user only the permissions required to perform its intended tasks. For an ETL application, the IAM policy should restrict S3 access to the necessary bucket and objects and allow only the required KMS operations on the specific encryption key. This reduces the potential impact if credentials or the application are compromised. Granting administrator access or broad permissions unnecessarily increases security risk, while anonymous access is inappropriate for protected data. Therefore, following the principle of least privilege provides the most secure and appropriate IAM design for the ETL application.<\/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 261: A data engineer needs to ingest data from an on-premises PostgreSQL database into Amazon S3 on a recurring basis. The source database must remain available during the process. Which AWS service is MOST appropriate? Amazon Athena […]<\/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\/13531"}],"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=13531"}],"version-history":[{"count":1,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/13531\/revisions"}],"predecessor-version":[{"id":13545,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/13531\/revisions\/13545"}],"wp:attachment":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/media?parent=13531"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/categories?post=13531"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/tags?post=13531"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}