Databricks-Certified-Data-Engineer-Associate Exam Dumps - Databricks Certification Questions and Answers

In Databricks, when creating a table, you can add a comment to columns or the entire table to provide more information about the data it contains. In this case, since it’s organization policy to indicate that the new table includes personally identifiable information (PII), option D is correct. The line of code would be added after defining the table structure and before closing with a semicolon. References: Data Engineer Associate Exam Guide, CREATE TABLE USING (Databricks SQL)

 Option C is the correct answer because Parquet files have a well-defined schema that is embedded within the data itself. This means that the data types and column names of the Parquet files are automatically detected and preserved when creating an external table from them. This also enables the use of SQL and other structured query languages to access and analyze the data. CSV files, on the other hand, do not have a schema embedded in them, and require specifying the schema explicitly or inferring it from the data when creating an external table from them. This can lead to errors or inconsistencies in the data types and column names, and also increase the processing time and complexity.

 CREATE TABLE AS SELECT, Parquet Files, CSV Files, Parquet vs. CSV

Databricks Asset Bundles are a Databricks CLI feature that lets teams define Databricks projects “as code” using YAML configuration files . In a bundle, metadata is expressed in YAML (for example, a required databricks.yml at the root), and the configuration can declare resources (such as Databricks Jobs/workflows and other supported assets), artifacts , and deployment targets . After defining these YAML manifests, teams use the Databricks CLI to validate, deploy, and run the bundle into a Databricks workspace, which matches the scenario described (YAML + CLI deployment). This approach standardizes how workflows and related resources are packaged and promoted across environments (dev/test/prod) while keeping definitions version-controlled and reproducible. GitHub is only a hosting/version-control platform (not the YAML deployment feature itself), Terraform is a separate IaC tool, and “DataOps” is a methodology rather than a specific Databricks feature. ( Databricks Documentation )

The SQL keyword that can be used to convert a table from a long format to a wide format is PIVOT. The PIVOT clause is used to rotate the rows of a table into columns of a new table1. The PIVOT clause can aggregate the values of a column based on the distinct values of another column, and use those values as the column names of the new table1. The PIVOT clause can be useful for transforming data from a long format, where each row represents an observation with multiple attributes, to a wide format, where each row represents an observation with a single attribute and multiple values2. For example, the PIVOT clause can be used to convert a table that contains the sales of different products by different regions into a table that contains the sales of each product by each region as separate columns1.

The other options are not suitable for converting a table from a long format to a wide format. CONVERT is a function that can be used to change the data type of an expression3. WHERE is a clause that can be used to filter the rows of a table based on a condition4. TRANSFORM is a keyword that can be used to apply a user-defined function to a group of rows in a table5. SUM is a function that can be used to calculate the total of a numeric column.

1: PIVOT | Databricks on AWS

2: Reshaping Data - Long vs Wide Format | Databricks on AWS

3: CONVERT | Databricks on AWS

4: WHERE | Databricks on AWS

5: TRANSFORM | Databricks on AWS

[SUM | Databricks on AWS]

Delta Lake provides schema enforcement on write, preventing incompatible data from being silently added to a table. When schema evolution is explicitly enabled through the appropriate write or ingestion configuration, new optional columns can be added without dropping and recreating the existing table. Delta Lake also maintains a transaction log containing committed table versions, enabling table-history inspection and time-travel queries for debugging or auditing, subject to retention settings. A plain Parquet table does not provide Delta’s transactional history or equivalent write enforcement. Recreating a Delta table whenever columns change is unnecessary and can disrupt its history. Auto Loader can detect evolving source schemas, but an external table with schema inference alone does not provide the complete set of required Delta capabilities. Therefore, option D is correct.

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The --target prod option explicitly selects the prod target from the bundle configuration. Databricks then resolves ${var.dataset_catalog} using the target-level value catalog_prod, which takes precedence over the top-level default of catalog_dev. This allows the same dashboard resource definition to be promoted across environments without editing the resource itself. The --var option can override an individual variable, but it does not automatically select the production target. The --profile option selects an authentication profile from .databrickscfg; it does not select a bundle target or automatically apply the target’s variables. bundle execute is not the deployment command. Consequently, option D correctly combines production-target selection with the environment-specific variable override already defined in the YAML configuration.

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One of the features of DLT is that it provides data quality metrics for each dataset in the pipeline, such as the number of records that pass or fail expectations, the number of records that are dropped, and the number of records that are written to the target. These metrics can be accessed from the DLT pipeline page, where the data engineer can click on each table and view the data quality statistics for the latest update or any previous update. This way, they can identify which table is dropping the records and why. References:

Monitor Delta Live Tables pipelines

Manage data quality with Delta Live Tables

Option D matches the heterogeneous sources and governance requirements. A relational database, object storage, and Kafka require source-appropriate ingestion configurations rather than a single connector attempting to handle every source. Database ingestion can use incremental or change-data-capture processing, Amazon S3 files can use file-oriented ingestion, and Kafka requires a streaming connector. Publishing the outputs into Unity Catalog schemas provides centralized permissions, discovery, and governance boundaries. Supported Databricks ingestion workloads also record lineage between source and destination objects in Unity Catalog. Delta table history and time travel provide access to earlier table versions when the required transaction-log and data-file retention settings are maintained. Disabling version history or repeatedly performing complete reloads would weaken auditability and increase processing overhead. Therefore, the separate, incrementally processed, Unity Catalog-governed design in option D is correct.

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 Create a table using SQL | Databricks on AWS, Create a table using SQL - Azure Databricks, Delta Lake Quickstart - Azure Databricks