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Free Databricks Databricks-Certified-Data-Analyst-Associate Exam Actual Questions

The questions for Databricks-Certified-Data-Analyst-Associate were last updated On Apr 27, 2025

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Question No. 1

Data professionals with varying titles use the Databricks SQL service as the primary touchpoint with the Databricks Lakehouse Platform. However, some users will use other services like Databricks Machine Learning or Databricks Data Science and Engineering.

Which of the following roles uses Databricks SQL as a secondary service while primarily using one of the other services?

ABusiness analyst

BSQL analyst

CData engineer

DBusiness intelligence analyst

EData analyst
Data engineers are primarily responsible for building, managing, and optimizing data pipelines and architectures. They use Databricks Data Science and Engineering service to perform tasks such as data ingestion, transformation, quality, and governance. Data engineers may use Databricks SQL as a secondary service to query, analyze, and visualize data from the lakehouse, but this is not their main focus.Reference:Databricks SQL overview,Databricks Data Science and Engineering overview,Data engineering with Databricks

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Correct Answer: C

Question No. 2

A data engineering team has created a Structured Streaming pipeline that processes data in micro-batches and populates gold-level tables. The microbatches are triggered every minute.

A data analyst has created a dashboard based on this gold-level dat

a. The project stakeholders want to see the results in the dashboard updated within one minute or less of new data becoming available within the gold-level tables.

Which of the following cautions should the data analyst share prior to setting up the dashboard to complete this task?

AThe required compute resources could be costly

BThe gold-level tables are not appropriately clean for business reporting

CThe streaming data is not an appropriate data source for a dashboard

DThe streaming cluster is not fault tolerant

EThe dashboard cannot be refreshed that quickly
A Structured Streaming pipeline that processes data in micro-batches and populates gold-level tables every minute requires a high level of compute resources to handle the frequent data ingestion, processing, and writing. This could result in a significant cost for the organization, especially if the data volume and velocity are large. Therefore, the data analyst should share this caution with the project stakeholders before setting up the dashboard and evaluate the trade-offs between the desired refresh rate and the available budget. The other options are not valid cautions because:
B . The gold-level tables are assumed to be appropriately clean for business reporting, as they are the final output of the data engineering pipeline. If the data quality is not satisfactory, the issue should be addressed at the source or silver level, not at the gold level.
C . The streaming data is an appropriate data source for a dashboard, as it can provide near real-time insights and analytics for the business users. Structured Streaming supports various sources and sinks for streaming data, including Delta Lake, which can enable both batch and streaming queries on the same data.
D . The streaming cluster is fault tolerant, as Structured Streaming provides end-to-end exactly-once fault-tolerance guarantees through checkpointing and write-ahead logs. If a query fails, it can be restarted from the last checkpoint and resume processing.
E . The dashboard can be refreshed within one minute or less of new data becoming available in the gold-level tables, as Structured Streaming can trigger micro-batches as fast as possible (every few seconds) and update the results incrementally. However, this may not be necessary or optimal for the business use case, as it could cause frequent changes in the dashboard and consume more resources.Reference:Streaming on Databricks,Monitoring Structured Streaming queries on Databricks,A look at the new Structured Streaming UI in Apache Spark 3.0,Run your first Structured Streaming workload

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Correct Answer: A

Question No. 3

Which of the following approaches can be used to connect Databricks to Fivetran for data ingestion?

AUse Workflows to establish a SQL warehouse (formerly known as a SQL endpoint) for Fivetran to interact with

BUse Delta Live Tables to establish a cluster for Fivetran to interact with

CUse Partner Connect's automated workflow to establish a cluster for Fivetran to interact with

DUse Partner Connect's automated workflow to establish a SQL warehouse (formerly known as a SQL endpoint) for Fivetran to interact with

EUse Workflows to establish a cluster for Fivetran to interact with
Partner Connect is a feature that allows you to easily connect your Databricks workspace to Fivetran and other ingestion partners using an automated workflow. You can select a SQL warehouse or a cluster as the destination for your data replication, and the connection details are sent to Fivetran. You can then choose from over 200 data sources that Fivetran supports and start ingesting data into Delta Lake.Reference:Connect to Fivetran using Partner Connect,Use Databricks with Fivetran

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Correct Answer: C

Question No. 4

Delta Lake stores table data as a series of data files, but it also stores a lot of other information.

Which of the following is stored alongside data files when using Delta Lake?

ANone of these

BTable metadata, data summary visualizations, and owner account information

CTable metadata

DData summary visualizations

EOwner account information
Delta Lake is a storage layer that enhances data lakes with features like ACID transactions, schema enforcement, and time travel. While it stores table data as Parquet files, Delta Lake also keeps a transaction log (stored in the _delta_log directory) that contains detailed table metadata.
This metadata includes:
Table schema
Partitioning information
Data file paths
Transactional operations like inserts, updates, and deletes
Commit history and version control
This metadata is critical for supporting Delta Lake's advanced capabilities such as time travel and efficient query execution. Delta Lake does not store data summary visualizations or owner account information directly alongside the data files.

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Correct Answer: C

Question No. 5

A data analyst has a managed table table_name in database database_name. They would now like to remove the table from the database and all of the data files associated with the table. The rest of the tables in the database must continue to exist.

Which of the following commands can the analyst use to complete the task without producing an error?

ADROP DATABASE database_name;

BDROP TABLE database_name.table_name;

CDELETE TABLE database_name.table_name;

DDELETE TABLE table_name FROM database_name;

EDROP TABLE table_name FROM database_name;
The DROP TABLE command removes a table from the metastore and deletes the associated data files. The syntax for this command is DROP TABLE [IF EXISTS] [database_name.]table_name;. The optional IF EXISTS clause prevents an error if the table does not exist. The optional database_name. prefix specifies the database where the table resides. If not specified, the current database is used. Therefore, the correct command to remove the table table_name from the database database_name and all of the data files associated with it is DROP TABLE database_name.table_name;. The other commands are either invalid syntax or would produce undesired results.Reference:Databricks - DROP TABLE

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Correct Answer: B