{"id":12123,"date":"2026-09-15T06:15:58","date_gmt":"2026-09-15T06:15:58","guid":{"rendered":"https:\/\/www.examlabs.com\/certification\/?p=12123"},"modified":"2026-09-15T06:15:58","modified_gmt":"2026-09-15T06:15:58","slug":"databricks-certified-data-analyst-associate-practice-test-questions-and-exam-dumps-part-1-q1-20","status":"publish","type":"post","link":"https:\/\/www.examlabs.com\/certification\/databricks-certified-data-analyst-associate-practice-test-questions-and-exam-dumps-part-1-q1-20\/","title":{"rendered":"Databricks Certified Data Analyst Associate Practice Test Questions and Exam Dumps Part 1 Q1-20"},"content":{"rendered":"

View Full Databricks Certified Data Analyst Associate Exam Dumps<\/a> and Practice Test Dumps.<\/b><\/h2>\n

 <\/p>\n

Question 1<\/b><\/h3>\n

Which of the following layers of the medallion architecture is most commonly used by data analysts and business intelligence reporting?<\/b><\/p>\n

    \n
  1. Bronze Layer<\/span><\/li>\n
  2. Silver Layer<\/span><\/li>\n
  3. Gold Layer<\/span><\/li>\n
  4. Landing Layer<\/span><\/li>\n<\/ol>\n

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

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

    The medallion architecture is a data design pattern used to logically organize data in a lakehouse, consisting of three distinct tiers: Bronze, Silver, and Gold. The Bronze layer ingests raw, unprocessed data directly from source systems in its original format. The Silver layer cleans, normalizes, filters, and deduplicates this data to establish a reliable single source of truth. Finally, the Gold layer contains highly refined, business-level aggregates, dimensional models, and KPI-ready tables. Data analysts and business intelligence professionals primarily query the Gold layer because the data is pre-aggregated, validated, and structured specifically for reporting, dashboards, and analytical decision-making without requiring complex, low-level data wrangling or cleansing steps.<\/span><\/p>\n

    Question 2<\/b><\/h3>\n

    Which core component serves as the default storage and table format in Databricks, providing ACID transactions and time travel capabilities?<\/b><\/p>\n

      \n
    1. Apache Hive SerDe<\/span><\/li>\n
    2. Delta Lake<\/span><\/li>\n
    3. CSV Flat Files<\/span><\/li>\n
    4. Plain Parquet Storage<\/span><\/li>\n<\/ol>\n

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

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

      Delta Lake is an open-source storage layer that brings reliability, security, and performance to data lakes. In Databricks, every managed table is stored as a Delta table by default. Delta Lake stores data in efficient Apache Parquet columnar format while introducing an immutable transaction log (<\/span>_delta_log<\/span>) that records every atomic change made to the table. This transaction log is what enables ACID (Atomicity, Consistency, Isolation, Durability) transactions, ensuring that read and write operations never corrupt data even under high concurrency. Additionally, the log powers advanced features like time travel for querying past data versions, schema enforcement, and rapid rollbacks.<\/span><\/p>\n

      Question 3<\/b><\/h3>\n

      Where within Databricks SQL can a user write, edit, and execute SQL queries interactively?<\/b><\/p>\n

        \n
      1. Dashboards Page<\/span><\/li>\n
      2. Data Explorer<\/span><\/li>\n
      3. SQL Editor Page<\/span><\/li>\n
      4. Jobs Scheduler<\/span><\/li>\n<\/ol>\n

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

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

        The SQL Editor page in Databricks SQL is the dedicated workspace interface where data analysts can write, test, execute, save, and share ANSI SQL queries against Delta tables and views. It is designed specifically for interactive data exploration, providing features such as automatic syntax autocompletion, schema browsing, execution history tracking, and instant visualization creation. As the primary day-to-day workspace for analysts, it allows users to draft complex queries, inspect tabular outputs, and seamlessly convert query results into charts or dashboards without leaving the browser environment.<\/span><\/p>\n

        Question 4<\/b><\/h3>\n

        What is the primary compute resource used to execute queries and power dashboards within Databricks SQL?<\/b><\/p>\n

          \n
        1. Classic Apache Spark Driver Nodes<\/span><\/li>\n
        2. SQL Warehouses<\/span><\/li>\n
        3. Single-Node Development Clusters<\/span><\/li>\n
        4. Delta Live Tables Pipelines<\/span><\/li>\n<\/ol>\n

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

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

          SQL Warehouses (formerly referred to as SQL endpoints) are specialized, dedicated compute resources optimized specifically to handle Databricks SQL workloads, ad-hoc queries, and BI dashboard reporting. Unlike general-purpose Apache Spark clusters used for data engineering or machine learning, SQL Warehouses manage infrastructure scaling automatically based on concurrency demands. They support Photon\u2014Databricks’ native vectorized query engine\u2014to accelerate relational query performance. Furthermore, SQL Warehouses handle user session management, query result caching, and high-concurrency loads efficiently, ensuring business analysts experience low latency when querying large datasets.<\/span><\/p>\n

          Question 5<\/b><\/h3>\n

          A data analyst writes a query to group data by region, but the output fails because a non-aggregated column is included in the <\/b>SELECT<\/b> clause without being grouped. Which clause is required to fix this query?<\/b><\/p>\n

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

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

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

            When performing aggregate functions (such as <\/span>COUNT()<\/span>, <\/span>SUM()<\/span>, or <\/span>AVG()<\/span>) in SQL, any column in the <\/span>SELECT<\/span> list that is not part of an aggregate function must be included in a <\/span>GROUP BY<\/span> clause. Without a <\/span>GROUP BY<\/span> clause specifying those non-aggregated columns, the database engine cannot determine how to partition or group the individual rows for the aggregation calculation, resulting in a syntax or analysis exception. Adding the appropriate <\/span>GROUP BY<\/span> clause organizes the rows into distinct groups based on the specified column values, allowing the aggregate functions to execute correctly across each group.<\/span><\/p>\n

            Question 6<\/b><\/h3>\n

            How should Databricks SQL be positioned in relation to other business intelligence (BI) tools like Tableau, Power BI, or Looker?<\/b><\/p>\n

              \n
            1. As an exact functional substitute with identical visualization capabilities<\/span><\/li>\n
            2. As a complete, mandatory replacement for all enterprise reporting requirements<\/span><\/li>\n
            3. As a complementary tool for quick in-platform BI work, querying, and native dashboarding<\/span><\/li>\n
            4. As a backend database storage system that cannot connect to external tools<\/span><\/li>\n<\/ol>\n

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

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

              Databricks SQL provides robust native querying, dashboarding, and visual analytics capabilities directly within the lakehouse platform, making it ideal for rapid in-platform data exploration and team reporting. However, it is designed to work both standalone and complementarily alongside specialized enterprise BI tools like Tableau, Power BI, and Looker. External BI tools connect directly to Databricks SQL Warehouses using native drivers (such as JDBC\/ODBC or partner connectors) to leverage the high performance and governance of the lakehouse for organization-wide executive dashboards and deep visual analytics.<\/span><\/p>\n

              Question 7<\/b><\/h3>\n

              What is the primary difference between a managed table and an external table in Databricks Unity Catalog?<\/b><\/p>\n

                \n
              1. Managed tables store both metadata and underlying data files within Unity Catalog storage, whereas external tables point to data files located in custom cloud object storage locations.<\/span><\/li>\n
              2. Managed tables are read-only, whereas external tables allow write operations.<\/span><\/li>\n
              3. External tables cannot be queried using Databricks SQL.<\/span><\/li>\n
              4. Managed tables do not support Delta Lake formatting.<\/span><\/li>\n<\/ol>\n

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

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

                In Databricks Unity Catalog, the fundamental difference between managed and external tables lies in who controls the physical data files. For managed tables, Databricks fully governs both the table metadata and the underlying data files stored in the catalog’s designated storage location; executing a <\/span>DROP TABLE<\/span> command deletes both the metadata and the physical data files from storage. Conversely, external tables point to data files stored in a user-managed cloud storage location (such as an S3 bucket or Azure ADLS container). Dropping an external table deletes only the catalog metadata, leaving the actual underlying data files completely untouched and safe in cloud storage.<\/span><\/p>\n

                Question 8<\/b><\/h3>\n

                Which of the following describes a key advantage of using a Delta Lake-based data lakehouse over a traditional enterprise data warehouse (EDW)?<\/b><\/p>\n

                  \n
                1. It forces data to be transformed into proprietary, closed binary formats.<\/span><\/li>\n
                2. It eliminates the need for any storage costs.<\/span><\/li>\n
                3. It combines the reliability and ACID transactions of data warehouses with the flexible, open-source file formats of data lakes.<\/span><\/li>\n
                4. It restricts users from writing standard SQL queries.<\/span><\/li>\n<\/ol>\n

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

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

                  A data lakehouse architecture bridges the traditional gap between low-cost data lakes and rigid enterprise data warehouses. By storing open-source, standard file formats (like Apache Parquet) combined with a transactional metadata layer (Delta Lake), it delivers reliable ACID transactions, schema enforcement, high performance, and governance directly on top of cheap cloud object storage. This prevents data silos, eliminates the need to maintain separate storage and warehouse systems, and allows organizations to run both heavy machine learning workloads and standard SQL reporting on the exact same copy of data.<\/span><\/p>\n

                  Question 9<\/b><\/h3>\n

                  When a data analyst creates a query result set containing one <\/b>STRING<\/b> column and one <\/b>INT<\/b> column, what default visualization type is automatically selected when clicking “Add visualization”?<\/b><\/p>\n

                    \n
                  1. Line Chart<\/span><\/li>\n
                  2. Bar Chart<\/span><\/li>\n
                  3. Scatter Plot<\/span><\/li>\n
                  4. Cohort Grid<\/span><\/li>\n<\/ol>\n

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

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

                    Databricks SQL includes a built-in visualization builder that automatically analyzes the schema and data types of a query result set to infer the most sensible default visualization type. When a query returns exactly one categorical string column (representing categories or labels) and one numeric integer column (representing metrics or counts), a Bar Chart is selected by default. The tool automatically maps the <\/span>STRING<\/span> column to the X-axis and the <\/span>INT<\/span> column to the Y-axis, allowing analysts to visualize categorical distributions instantly without manual configuration.<\/span><\/p>\n

                    Question 10<\/b><\/h3>\n

                    Which feature in Databricks allows users to query historical snapshots of table data as of a specific point in time or version number?<\/b><\/p>\n

                      \n
                    1. Delta Time Travel<\/span><\/li>\n
                    2. Table Cloning<\/span><\/li>\n
                    3. Database Indexing<\/span><\/li>\n
                    4. Schema Evolution<\/span><\/li>\n<\/ol>\n

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

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

                      Delta Time Travel is a powerful feature enabled directly by the Delta Lake transaction log (<\/span>_delta_log<\/span>). Because every transaction creates an immutable version of the table, analysts and engineers can query, audit, or roll back table data as of a specific timestamp or transaction version number using syntax like <\/span>SELECT * FROM table TIMESTAMP AS OF ‘2026-01-01’<\/span>. This capability is invaluable for reproducing past business reports, tracking data lineage across time, debugging data pipeline errors, and recovering quickly from accidental data modifications or corruptions without needing complex backups.<\/span><\/p>\n

                      Question 11<\/b><\/h3>\n

                      Which Databricks governance solution provides centralized access control, auditing, and lineage across workspaces for tables, views, and volumes?<\/b><\/p>\n

                        \n
                      1. AWS Identity and Access Management<\/span><\/li>\n
                      2. Databricks Unity Catalog<\/span><\/li>\n
                      3. Legacy Hive Metastore<\/span><\/li>\n
                      4. Azure Active Directory Groups<\/span><\/li>\n<\/ol>\n

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

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

                        Databricks Unity Catalog is a fine-grained governance solution built specifically for data and AI workloads across the Databricks Lakehouse Platform. It provides a centralized place to manage access permissions, track automatic data lineage, share data securely across organizations, and audit user queries across all workspaces in an account. Unity Catalog establishes a three-level namespace (<\/span>catalog.schema.table<\/span>), allowing administrators to grant permissions down to the row and column level while giving security and compliance teams complete visibility into how data assets are accessed and utilized across the enterprise.<\/span><\/p>\n

                        Question 12<\/b><\/h3>\n

                        A data analyst wants to share a recurring report with executive stakeholders who do not have direct access to the Databricks workspace. Which feature can be used to deliver these updates automatically?<\/b><\/p>\n

                          \n
                        1. Manual CSV exports via email attachment or scheduled dashboard subscriptions\/alerts<\/span><\/li>\n
                        2. Writing a custom Python script using local sockets<\/span><\/li>\n
                        3. Converting the workspace into a static PDF notebook manually every hour<\/span><\/li>\n
                        4. Direct SSH tunneling into the driver node<\/span><\/li>\n<\/ol>\n

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

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

                          Databricks SQL supports scheduling regular refreshes and automated dashboard subscriptions or alerts. Analysts can configure scheduled reports to execute queries at designated times and send snapshot results or alert notifications directly to stakeholders via email or integrated webhooks (such as Slack or Microsoft Teams). This ensures that business consumers and executive stakeholders stay informed with up-to-date metric summaries without needing direct, active login access to the underlying Databricks workspace, streamlining organizational communication and reporting workflows.<\/span><\/p>\n

                          Question 13<\/b><\/h3>\n

                          Which SQL keyword or function is used to transform a column containing arrays or maps into multiple rows, duplicating the values of other columns in the record?<\/b><\/p>\n

                            \n
                          1. flatten()<\/span><\/li>\n
                          2. explode()<\/span><\/li>\n
                          3. collect_list()<\/span><\/li>\n
                          4. pivot()<\/span><\/li>\n<\/ol>\n

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

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

                            The <\/span>explode()<\/span> function in Spark SQL and Databricks SQL is used to unpack elements from a nested array or map column into separate individual rows. When applied, each element in the collection creates a new row, while duplicating the scalar values of the remaining columns in the original dataset. This is a critical data transformation step for data analysts working with semi-structured or nested data (such as JSON arrays of line items within an order), allowing them to flatten complex structures into standard tabular formats for relational querying and aggregation.<\/span><\/p>\n

                            Question 14<\/b><\/h3>\n

                            When executing queries in Databricks SQL, how does the system optimize performance for repetitive identical queries without recomputing results?<\/b><\/p>\n

                              \n
                            1. By caching query results automatically in the query result cache layer<\/span><\/li>\n
                            2. By permanently deleting underlying source tables<\/span><\/li>\n
                            3. By converting all tables into static text files<\/span><\/li>\n
                            4. By doubling the size of the cluster automatically<\/span><\/li>\n<\/ol>\n

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

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

                              Databricks SQL utilizes a built-in query results cache. When a user executes a query, Databricks checks whether an identical query has been run recently and whether the underlying table data has changed. If the query text and source data match an existing cache entry, Databricks returns the cached results instantly without spinning up compute resources or re-running execution plans. This caching layer optimizes both response latency and operational compute costs, providing lightning-fast response times for repetitive dashboard loads and frequent analytical queries.<\/span><\/p>\n

                              Question 15<\/b><\/h3>\n

                              Which Databricks feature should a data analyst use to combine multiple disparate Delta tables based on a common matching key column?<\/b><\/p>\n

                                \n
                              1. SQL <\/span>JOIN<\/span> operations (e.g., <\/span>INNER JOIN<\/span>, <\/span>LEFT JOIN<\/span>)<\/span><\/li>\n
                              2. UNION ALL<\/span> stacking only<\/span><\/li>\n
                              3. GROUP BY<\/span> rollups<\/span><\/li>\n
                              4. Partition dropping<\/span><\/li>\n<\/ol>\n

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

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

                                Standard SQL <\/span>JOIN<\/span> clauses (such as <\/span>INNER JOIN<\/span>, <\/span>LEFT JOIN<\/span>, <\/span>RIGHT JOIN<\/span>, or <\/span>FULL OUTER JOIN<\/span>) are used to relate and combine rows from two or more tables based on a shared matching key column condition (such as <\/span>customer_id<\/span> or <\/span>product_id<\/span>). Relational joins are fundamental for data modeling in Databricks SQL, allowing analysts to connect dimension tables with fact tables to enrich datasets and construct comprehensive analytical models for reporting and business intelligence dashboards.<\/span><\/p>\n

                                Question 16<\/b><\/h3>\n

                                What is the primary benefit of using Photon acceleration in Databricks SQL warehouses?<\/b><\/p>\n

                                  \n
                                1. It converts SQL queries into Python code automatically.<\/span><\/li>\n
                                2. It provides vectorised query execution engines written in C++ to process relational queries significantly faster.<\/span><\/li>\n
                                3. It eliminates the need for Delta Lake transaction logs.<\/span><\/li>\n
                                4. It restricts user access to read-only views.<\/span><\/li>\n<\/ol>\n

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

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

                                  Photon is Databricks’ native vectorized execution engine built entirely in C++. It is engineered to process data much faster by taking advantage of modern CPU hardware capabilities, utilizing SIMD (Single Instruction, Multiple Data) vectorization and efficient memory management. Photon drastically accelerates SQL query performance, speeds up scans, joins, and aggregations, and reduces execution times for heavy data analysis workloads, resulting in faster dashboard rendering and higher query concurrency on SQL Warehouses.<\/span><\/p>\n

                                  Question 17<\/b><\/h3>\n

                                  How can a data analyst search for tables tagged with specific sensitive keys (such as <\/b>confidential<\/b>) across a Unity Catalog metastore workspace?<\/b><\/p>\n

                                    \n
                                  1. By using the search filter syntax <\/span>tag:confidential<\/span> in the workspace search bar<\/span><\/li>\n
                                  2. By manually scanning every single database schema file line by line<\/span><\/li>\n
                                  3. By running an operating system terminal command <\/span>grep<\/span><\/li>\n
                                  4. By deleting all untagged tables<\/span><\/li>\n<\/ol>\n

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

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

                                    Databricks Unity Catalog supports robust object tagging for metadata management and data governance. Analysts and data stewards can assign custom tags (such as <\/span>pii:true<\/span> or <\/span>classification:confidential<\/span>) to catalogs, schemas, tables, and columns. To locate these assets quickly across workspaces, users can type specific search filter syntax, such as <\/span>tag:confidential<\/span>, directly into the top Databricks workspace search bar. This enables fast auditing of sensitive data assets, helps verify compliance with corporate governance policies, and simplifies data discovery.<\/span><\/p>\n

                                    Question 18<\/b><\/h3>\n

                                    Which of the following best describes the contents and purpose of a Silver layer table in the medallion architecture?<\/b><\/p>\n

                                      \n
                                    1. Unprocessed raw JSON files dumped straight from an external API source<\/span><\/li>\n
                                    2. Final executive presentation dashboard layouts<\/span><\/li>\n
                                    3. Cleaned, filtered, deduplicated, and conformed data ready for joining and advanced analysis<\/span><\/li>\n
                                    4. Local backup archives stored on a user’s personal laptop<\/span><\/li>\n<\/ol>\n

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

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

                                      The Silver layer sits in the middle of the medallion architecture, bridging raw ingestion and business reporting. It takes the unvalidated, raw data from the Bronze layer and cleans it\u2014handling missing values, resolving data type inconsistencies, removing duplicate records, and standardizing formats. The resulting Silver tables form a trusted, conformed single source of truth that is fully cleansed and structured, making it ideal for complex relational joins, exploratory data analysis, feature engineering, and powering downstream Gold layer aggregations.<\/span><\/p>\n

                                      Question 19<\/b><\/h3>\n

                                      If a data analyst needs to calculate running totals or moving averages across an ordered partition of windowed data, which SQL clause should they utilize?<\/b><\/p>\n

                                        \n
                                      1. OVER (PARTITION BY … ORDER BY …)<\/span><\/li>\n
                                      2. GROUP BY ROLLUP<\/span><\/li>\n
                                      3. DISTINCT<\/span><\/li>\n
                                      4. EXISTS<\/span><\/li>\n<\/ol>\n

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

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

                                        Window functions in Databricks SQL perform calculations across sets of table rows that are somehow related to the current row. They utilize the <\/span>OVER<\/span> clause combined with <\/span>PARTITION BY<\/span> (to divide rows into logical groups or partitions) and <\/span>ORDER BY<\/span> (to sort rows sequentially within each partition) alongside analytical functions like <\/span>SUM()<\/span>, <\/span>AVG()<\/span>, or <\/span>ROW_NUMBER()<\/span>. This enables sophisticated analytical calculations\u2014such as running totals, moving averages, and cumulative distributions\u2014without collapsing row counts or requiring complex self-joins.<\/span><\/p>\n

                                        Question 20<\/b><\/h3>\n

                                        What happens to data files in cloud storage when a managed Delta table is dropped using the <\/b>DROP TABLE<\/b> command in Databricks?<\/b><\/p>\n

                                          \n
                                        1. Only the metadata in the metastore is deleted, while data files remain untouched.<\/span><\/li>\n
                                        2. Both the table metadata and the underlying data files stored in the managed location are permanently deleted.<\/span><\/li>\n
                                        3. The data files are automatically moved to an encrypted trash bin with no recovery option.<\/span><\/li>\n
                                        4. The table is converted into a CSV file.<\/span><\/li>\n<\/ol>\n

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

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

                                          When a managed table is dropped in Databricks Unity Catalog, Databricks holds governance over both the table definition (metadata) and the physical data files stored in the managed storage location. Consequently, executing the <\/span>DROP TABLE<\/span> command on a managed table deletes both the catalog metadata and the underlying physical data files from cloud storage. This is a crucial distinction from external tables, where dropping the table removes only the catalog metadata while leaving the physical data files completely untouched in the user’s custom cloud storage bucket.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

                                          View Full Databricks Certified Data Analyst Associate Exam Dumps and Practice Test Dumps.   Question 1 Which of the following layers of the medallion architecture is most commonly used by data analysts and business intelligence reporting? Bronze Layer Silver Layer Gold Layer Landing Layer Correct Answer: 3 Explanation The medallion architecture is a data design […]<\/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\/12123"}],"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=12123"}],"version-history":[{"count":1,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/12123\/revisions"}],"predecessor-version":[{"id":12144,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/12123\/revisions\/12144"}],"wp:attachment":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/media?parent=12123"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/categories?post=12123"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/tags?post=12123"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}