Databricks Databricks-Certified-Data-Engineer-Associate - Databricks Certified Data Engineer Associate Exam

Databricks Repos is a visual Git client and API in Databricks that supports common Git operations such as cloning, committing, pushing, pulling, and branch management. Databricks Notebooks versioning is a legacy feature that allows users to link notebooks to GitHub repositories and perform basic Git operations. However, Databricks Notebooks versioning does not support the use of multiple branches for development work, which is an advantage of using Databricks Repos. With Databricks Repos, users can create and manage branches for different features, experiments, or bug fixes, and merge, rebase, or resolve conflicts between them. Databricks recommends using a separate branch for each notebook and following data science and engineering code development best practices using Git for version control, collaboration, and CI/CD. References: Git integration with Databricks Repos - Azure Databricks | Microsoft Learn, Git version control for notebooks (legacy) | Databricks on AWS, Databricks Repos Is Now Generally Available - New ‘Files’ Feature in …, Databricks Repos - What it is and how we can use it | Adatis.

 The keyword that can be used to compact the small files associated with a Delta table is OPTIMIZE. The OPTIMIZE command performs file compaction on a Delta table by rewriting a set of small files into a set of larger files1. This can improve the performance of queries that scan the table by reducing the number of files that need to be read and the amount of metadata that needs to be processed1. The OPTIMIZE command can also optionally sort the data within each file by a given set of columns, which can further improve the query performance by enabling data skipping and predicate pushdown1. The OPTIMIZE command can be applied to the whole table or to a specific partition of the table1.

The other keywords are not suitable for compacting the small files associated with a Delta table. REDUCE is a keyword used in the SQL syntax for aggregating data using a user-defined function2. COMPACTION is not a valid keyword in SQL or Python. REPARTITION is a keyword used in the Python syntax for changing the number of partitions of a DataFrame or an RDD3. VACUUM is a keyword used to remove files that are no longer referenced by a Delta table and are older than a retention threshold4.

1: OPTIMIZE | Databricks on AWS

2: REDUCE | Databricks on AWS

3: repartition | Databricks on AWS

4: VACUUM | Databricks on AWS

https://community.databricks.com/t5/data-engineering/sequential-vs-concurrency-optimization-questions-from-query/td-p/36696

 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

In Databricks Asset Bundles , the databricks.yml file located at the repository root serves as the single authoritative configuration file for the entire project. This file defines key elements such as the bundle name, deployment targets (for example, dev, test, prod), workspace configurations, permissions, and resource definitions including jobs and pipelines. Databricks documentation specifies that there must be one primary databricks.yml file at the root , ensuring consistency and simplifying deployment processes. However, to support modularity and maintainability, this root configuration file can reference additional YAML files using the include mapping. This allows teams to organize configurations across directories while still maintaining a single source of truth. Option A is incorrect because multiple root-level configuration files are not supported as independent authorities. Option C is invalid since configuration files are not restricted to hidden directories. Option D introduces ambiguity and contradicts the requirement for a single authoritative configuration. Therefore, using one root-level configuration file with optional includes is the correct and recommended approach.

In a Databricks customer ' s cloud account, the primary elements stored include:

Data: This is the central type of content stored in the customer ' s cloud account. Data might include various datasets, tables, and files that are used and managed through Databricks platforms.

Notebooks: These are also stored within a customer ' s cloud account. Notebooks include scripts, notes, and other information necessary for data analysis and processing tasks.

Cluster management metadata is indeed managed through the cloud, but it ' s primarily handled by Databricks rather than stored directly in the customer ' s account. The Databricks web application itself is not stored within the customer ' s cloud account; rather, it ' s a service provided by Databricks.