Unlock Databricks Mastery: Databricks Certified Data Engineer Associate Exam Prep Revolution

Correct : D

The only option that is stored in the Databricks customer's cloud account is data. Data is stored in the customer's cloud storage service, such as AWS S3 or Azure Data Lake Storage. The customer has full control and ownership of their data and can access it directly from their cloud account.

Option A is not correct, as the Databricks web application is hosted and managed by Databricks on their own cloud infrastructure. The customer does not need to install or maintain the web application, but only needs to access it through a web browser.

Option B is not correct, as the cluster management metadata is stored and managed by Databricks on their own cloud infrastructure. The cluster management metadata includes information such as cluster configuration, status, logs, and metrics. The customer can view and manage their clusters through the Databricks web application, but does not have direct access to the cluster management metadata.

Option C is not correct, as the repos are stored and managed by Databricks on their own cloud infrastructure. Repos are version-controlled repositories that store code and data files for Databricks projects. The customer can create and manage their repos through the Databricks web application, but does not have direct access to the repos.

Option E is not correct, as the notebooks are stored and managed by Databricks on their own cloud infrastructure. Notebooks are interactive documents that contain code, text, and visualizations for Databricks workflows. The customer can create and manage their notebooks through the Databricks web application, but does not have direct access to the notebooks.

Databricks Architecture

Databricks Data Sources

Databricks Repos

[Databricks Notebooks]

[Databricks Data Engineer Professional Exam Guide]

Correct : A

According to the medallion lakehouse architecture, gold tables are the final layer of data that powers analytics, machine learning, and production applications. They are often highly refined and aggregated, containing data that has been transformed into knowledge, rather than just information. Silver tables, on the other hand, are the intermediate layer of data that represents a validated, enriched version of the raw data from the bronze layer. They provide an enterprise view of all its key business entities, concepts and transactions, but they may not have all the aggregations and calculations that are required for specific use cases. Therefore, gold tables are more likely to contain aggregations than silver tables.Reference:

What is the medallion lakehouse architecture?

What is a Medallion Architecture?

Correct : C

Delta Lake is the optimized storage layer that provides the foundation for storing data and tables in the Databricks lakehouse.Delta Lake is open source software that extends Parquet data files with a file-based transaction log for ACID transactions and scalable metadata handling1.Delta Lake stores its data and metadata in a collection of files in a directory on a cloud storage system, such as AWS S3 or Azure Data Lake Storage2. Each Delta table has a transaction log that records the history of operations performed on the table, such as insert, update, delete, merge, etc.The transaction log also stores the schema and partitioning information of the table2.The transaction log enables Delta Lake to provide ACID guarantees, time travel, schema enforcement, and other features1.Reference:

What is Delta Lake? | Databricks on AWS

Quickstart --- Delta Lake Documentation

Correct : E

The best practice is to use 'Complete' as output mode instead of 'append' when working with aggregated tables. Since gold layer is work final aggregated tables, the only option with output mode as complete is option E.

Correct : C

The MERGE command can be used to upsert data from a source table, view, or DataFrame into a target Delta table. It allows you to specify conditions for matching and updating existing records, and inserting new records when no match is found.This way, you can avoid writing duplicate records into a Delta table1.The other commands (DROP, IGNORE, APPEND, INSERT) do not have this functionality and may result in duplicate records or data loss234.Reference:1: Upsert into a Delta Lake table using merge | Databricks on AWS2: SQL DELETE | Databricks on AWS3: SQL INSERT INTO | Databricks on AWS4: SQL UPDATE | Databricks on AWS