Databricks Certified Data Analyst Associate Exam Dumps & Practice Test Questions

Question 1:

In the medallion architecture framework used to organize data processing layers, which specific layer do data analysts primarily rely on for conducting their reporting, analysis, and making business decisions?

A. None of these layers are used by data analysts
B. Gold
C. All of these layers are used equally by data analysts
D. Silver
E. Bronze

Answer: B

Explanation:

The medallion architecture is a layered data management framework that organizes data through successive refinement stages, typically labeled Bronze, Silver, and Gold. Each layer plays a distinct role in transforming raw data into high-quality, business-ready information.

The Bronze layer serves as the raw data ingestion zone. It contains unprocessed data directly sourced from systems, often messy, duplicated, and inconsistent. This layer is primarily used by data engineers and developers who focus on cleaning and initial processing, but it is generally not suitable for direct analysis due to its raw and unrefined state.

The Silver layer builds upon Bronze by applying data cleansing, normalization, and basic transformations. It results in a more consistent and enriched dataset but still might lack the final business-specific aggregations or metrics needed for strategic reporting. This layer supports intermediate analytics and some exploratory data work.

The Gold layer represents the pinnacle of the medallion architecture. It contains highly curated, aggregated, and refined datasets that align with business logic and key performance indicators (KPIs). This layer provides datasets specifically structured to meet the needs of data analysts and business users. It ensures data quality, governance, and accessibility for direct consumption in dashboards, reports, and decision-making tools.

Data analysts prefer the Gold layer because it reduces the time spent on data preparation and enables them to focus on extracting actionable insights. Unlike the raw or semi-processed data in Bronze and Silver, the Gold layer’s datasets are reliable, consistent, and optimized for query performance, making them ideal for efficient analysis.

Hence, within the medallion architecture, the Gold layer is the primary layer utilized by data analysts for their reporting and decision-making activities.

Question 2:

A new data analyst with strong SQL skills but no prior experience with Databricks needs to quickly find the right area in Databricks SQL where they can write, modify, and run SQL queries against their organization’s datasets. 

Which Databricks SQL page should they use to perform these tasks?

A. Data page
B. Dashboards page
C. Queries page
D. Alerts page
E. SQL Editor page

Answer: E

Explanation:

When working with Databricks SQL, the primary interface for writing and executing SQL commands is the SQL Editor page. This environment is specifically designed to allow users to compose, edit, and run SQL queries efficiently against available datasets.

The SQL Editor offers a robust workspace featuring syntax highlighting, auto-completion, error detection, and the ability to connect directly to various data sources. It enables analysts to interactively build queries, test them, and view immediate results without switching contexts. Additionally, users can save queries for future reuse, share them with teammates, and optimize query performance—all within this interface.

Other pages in Databricks SQL serve different purposes:

• The Data page allows browsing of databases, tables, and schema information but does not provide a dedicated space for writing SQL code.

• The Dashboards page is meant for visualization, presenting data through charts and graphs created from query results, not for query authoring.

• The Queries page acts as a repository for saved or historical queries, facilitating organization and management but not active editing.

• The Alerts page enables users to set up notifications based on query outcomes to monitor data health or thresholds, rather than writing or executing SQL.

Therefore, for someone new to Databricks SQL aiming to write and run queries effectively, the SQL Editor page is the correct and most direct choice. Mastering this page allows analysts to leverage Databricks’ full SQL capabilities, streamlining data exploration, report creation, and analytical workflows.

Question 3:

An organization has recently incorporated Databricks SQL to improve its data analytics capabilities. The data team wants to understand how Databricks SQL fits within their existing business intelligence ecosystem, which includes platforms like Tableau, Power BI, and Looker. 

How should Databricks SQL be positioned relative to these traditional BI tools?

A. As a direct replacement offering the same features
B. As a replacement with reduced capabilities
C. As a full substitute with enhanced features
D. As a complementary tool for polished, professional presentations
E. As a complementary tool for quick, in-platform BI tasks

Correct Answer: E

Explanation:

Databricks SQL is primarily built for efficient querying and exploration of structured data directly within the Databricks environment. Although it provides some native visualization and dashboarding capabilities, it is not designed to replace comprehensive BI platforms like Tableau, Power BI, or Looker. Those platforms excel in advanced data visualization, interactive dashboards, extensive report generation, and sharing capabilities suited for large organizational use.

Instead, Databricks SQL should be regarded as a complementary tool, enabling data analysts and engineers to perform quick, exploratory data analyses and generate lightweight visual insights without the overhead of exporting data. It’s particularly suited for ad-hoc queries, rapid iteration during data preparation, or initial data validation stages.

Dedicated BI tools offer richer functionality such as scheduled reports, version control, multi-user collaboration, role-based access, and integration into enterprise reporting workflows. These are essential for crafting polished, production-grade dashboards tailored to stakeholders across various levels, from executives to external clients.

Using Databricks SQL alongside traditional BI platforms allows teams to be agile—leveraging Databricks SQL for rapid, in-platform exploration while relying on BI tools for robust, interactive, and shareable visualizations. This complementary usage enhances overall analytics productivity and ensures each tool is utilized for its strengths.

In summary, Databricks SQL is best positioned as a fast, integrated BI companion designed for quick insights, not as a replacement for full-featured BI software.

Question 4:

When integrating Databricks with Fivetran to automate data ingestion, which method offers the most efficient and streamlined setup for connecting the two platforms?

A. Use Workflows to configure a SQL warehouse (previously called a SQL endpoint) for Fivetran interaction
B. Deploy Delta Live Tables to establish a cluster for Fivetran integration
C. Use Partner Connect’s automated process to set up a cluster for Fivetran
D. Use Partner Connect’s automated process to configure a SQL warehouse (formerly SQL endpoint) for Fivetran
E. Use Workflows to configure a cluster for Fivetran interaction

Correct Answer: D

Explanation:

The most efficient and automated way to integrate Databricks with Fivetran for seamless data ingestion is through Partner Connect’s automated workflow that provisions a SQL warehouse (previously known as a SQL endpoint). Partner Connect is designed to simplify and speed up integrations with trusted partners like Fivetran, minimizing manual setup and configuration errors.

This approach automatically creates a SQL warehouse, which serves as a scalable and optimized compute resource tailored for SQL queries and data ingestion tasks. A SQL warehouse is ideal for this use case because it can autoscale based on demand, supports concurrency, and provides a performant interface for Fivetran to push data into Databricks efficiently.

Other options, such as using Workflows or Delta Live Tables, do not offer the same level of automation or appropriateness for ingestion setups. Workflows orchestrate internal Databricks tasks but are not specifically meant for creating integration endpoints with external platforms. Delta Live Tables focus on building and managing data pipelines within Databricks and are not designed for direct ingestion service connections.

Moreover, configuring a cluster manually or via workflows is less optimal for this scenario because clusters can be costlier to manage, lack native autoscaling specific to SQL query workloads, and may require more maintenance. The SQL warehouse abstraction provides a simplified and cost-effective way to manage query compute resources optimized for data ingestion.

Therefore, Partner Connect’s automated setup of a SQL warehouse for Fivetran interaction ensures a reliable, scalable, and maintenance-light integration that follows best practices, making it the recommended solution for connecting Databricks and Fivetran.

Question 5:

Within the Databricks Lakehouse Platform, various data professionals utilize different services based on their main job functions. Databricks SQL is commonly used to query and analyze structured data, but some roles primarily depend on other specialized services such as Databricks Machine Learning or Databricks Data Science and Engineering, only accessing Databricks SQL occasionally.

Which of the following roles most likely treats Databricks SQL as a secondary tool, primarily using other specialized Databricks services for their core responsibilities?

A. Business Analyst
B. SQL Analyst
C. Data Engineer
D. Business Intelligence Analyst
E. Data Analyst

Answer: C

Explanation:

Data engineers focus mainly on creating, managing, and optimizing data pipelines, ensuring that clean, structured data is prepared and available for downstream analytical or machine learning tasks. Their primary environment within the Databricks Lakehouse Platform is Databricks Data Science and Engineering, where they use notebooks and code to develop ETL workflows, automate data ingestion, and optimize storage systems. This backend-oriented work requires strong programming and system management skills rather than SQL-based querying or reporting.

While data engineers can and sometimes do run SQL queries—using Databricks SQL to validate data quality, check transformations, or support other teams—they do not rely on Databricks SQL as their main tool. Instead, SQL querying is an occasional activity to complement their broader engineering duties.

In contrast, roles like business analysts, SQL analysts, business intelligence analysts, and data analysts engage extensively with Databricks SQL daily. These professionals rely on SQL queries for data retrieval, dashboard creation, report generation, and business insights. Their workflows depend heavily on the SQL service as their primary interface.

Thus, because data engineers primarily work with Databricks Data Science and Engineering but occasionally use Databricks SQL to support their tasks, they are the role most accurately described as using Databricks SQL as a secondary tool. Understanding these distinctions helps organizations configure appropriate access and optimize workflows to leverage each professional’s strengths on the Databricks platform.

Question 6:

A data analyst has scheduled a SQL query to run every four hours on a SQL endpoint, but the endpoint takes a long time to start up before executing the query. 

To reduce the startup delay while keeping costs under control, what change should the analyst make?

A. Reduce the SQL endpoint cluster size
B. Increase the SQL endpoint cluster size
C. Disable the Auto Stop feature
D. Increase the minimum scaling value
E. Switch to a Serverless SQL endpoint

Answer: E

Explanation:

When a SQL endpoint takes a long time to start before executing scheduled queries, this delay is usually caused by the cold-start problem inherent in traditional cluster-based endpoints. These endpoints allocate compute resources ahead of time, but when configured to auto-stop to save costs during idle periods, they must fully restart or “spin up” before processing new queries. This startup process creates significant latency, especially problematic for queries running intermittently, such as every few hours.

To address this issue effectively while balancing cost efficiency, switching to a Serverless SQL endpoint is the best solution. Serverless endpoints eliminate the need to manually start or maintain a cluster. Instead, compute resources are dynamically and instantaneously allocated when a query is received. This means the endpoint is ready to run queries immediately, without startup lag, which greatly improves response time for scheduled or ad-hoc queries.

Serverless SQL endpoints also charge only for active usage time, which keeps costs controlled, especially for workloads with sporadic query patterns. Unlike simply increasing cluster size or changing scaling settings, which might improve query throughput but cannot remove startup latency, serverless endpoints fundamentally solve the cold-start delay.

Disabling Auto Stop might keep the cluster warm and reduce startup delays but would increase costs by running compute resources unnecessarily during idle times.

Therefore, using a Serverless SQL endpoint provides the optimal balance of reduced latency and cost management, making it the ideal adjustment for the analyst’s scenario.

Question 7:

A data engineering team has built a Structured Streaming pipeline in their Databricks environment that processes data in micro-batches every minute and writes to gold-level tables designed for high-quality business reporting. A data analyst has created a dashboard that queries these gold tables. Stakeholders now want the dashboard to refresh and show newly arrived data within one minute of it landing in the gold tables.

What key caution should the analyst share with stakeholders before configuring the dashboard to refresh this frequently?

A. The required compute resources could be costly
B. The gold-level tables are not clean enough for reporting
C. Streaming data is not suitable for dashboards
D. The streaming cluster lacks fault tolerance
E. The dashboard cannot refresh that quickly

Correct Answer: A

Explanation:

While it is technically feasible to configure a dashboard to refresh every minute, enabling near real-time visibility into the gold-level tables, this requirement comes with important trade-offs. The primary concern is the cost and compute resource implications of querying the tables so frequently. Each dashboard refresh triggers queries that require compute clusters to be actively running and responsive at all times.

In cloud-based environments like Databricks, compute resources are billed based on usage, so running clusters continuously to support minute-level refreshes can significantly increase operational costs. This continuous resource allocation can also impact other workloads if compute capacity is shared, leading to potential performance bottlenecks.

Options such as data cleanliness (B) or cluster fault tolerance (D) are valid concerns for data quality and reliability, but they don’t directly impact the frequency of dashboard refreshes. Similarly, dashboards can be configured to refresh rapidly (E) depending on the BI tool used, and streaming data can feed dashboards if architected properly (C).

Therefore, the most important caution to communicate is that frequent dashboard refreshes come with a potentially high cost due to increased compute resource demands. Stakeholders should carefully evaluate if the need for data freshness outweighs these additional expenses and infrastructure considerations before proceeding.

Question 8:

A data engineering team needs to ingest large datasets stored in cloud object storage services like AWS S3, Azure Data Lake, or Google Cloud Storage directly into their analytics system. They want to avoid copying data and instead create external tables that directly reference the data in the cloud storage.

Which is the correct approach to create such external tables pointing to cloud storage?

A. Create an external table specifying the DBFS path with FROM
B. Create an external table specifying the DBFS path with PATH
C. Direct ingestion from cloud storage is not possible
D. Create an external table specifying the object storage path with FROM
E. Create an external table specifying the object storage path with LOCATION

Correct Answer: E

Explanation:

When working with large datasets stored in cloud object storage, modern data platforms like Databricks support creating external tables that directly reference files without physically copying them into internal storage. The key is to use the LOCATION clause in the table definition, which points the table to the exact cloud storage path where the data files reside (e.g., an S3 bucket path or Azure Blob Storage URL).

This approach allows the analytics engine to read the data on-demand from its original location, saving storage costs and avoiding the overhead of data duplication. It also supports dynamic access to updated data without requiring a repeated ingestion process.

In contrast, DBFS (Databricks File System) is an abstraction for internal storage in Databricks, not the external cloud object storage itself. Therefore, options A and B are incorrect because they refer to DBFS paths when the goal is to link directly to external cloud storage.

Option C is incorrect because direct external table creation is fully supported by current cloud data platforms, making it a best practice for scalability and performance.

Option D is syntactically wrong because the FROM clause specifies table names or views, not file or storage paths.

Therefore, the correct method involves defining an external table with the LOCATION keyword, specifying the cloud object storage path. This enables efficient, scalable, and cost-effective access to large datasets stored externally, making E the correct choice.

Question 9:

A data analyst is creating a single dashboard that includes three distinct operational environments: Development, Testing, and Production. They want each environment to be clearly identified and separated by textual labels within the dashboard.

Which feature should the analyst use to add and format these text labels effectively within the dashboard?

A. Create individual endpoints for each environment
B. Write separate queries for each environment
C. Use markdown text boxes to add section headers
D. Enter text directly in the dashboard's edit mode
E. Apply different color schemes for each environment

Correct Answer: C

Explanation:

When organizing a dashboard into clearly labeled sections such as Development, Testing, and Production, the most effective approach is to use markdown text boxes. Markdown is a lightweight formatting language that allows analysts to insert styled text with headings, bold or italic fonts, bullet points, and horizontal dividers. This capability enables clear visual separation of dashboard sections without altering the underlying data queries or architecture.

Markdown text boxes provide a flexible and user-friendly way to create distinct section headers or labels, enhancing the dashboard's readability and professional appearance. This method ensures that the textual designation for each environment stands out and helps viewers quickly identify different operational areas.

Other options are less suitable for this specific need. Creating separate endpoints (A) or writing separate queries (B) adds unnecessary complexity and is more relevant for data processing rather than dashboard labeling. Simply typing text directly in edit mode (D) is possible but lacks the formatting power of markdown, which helps improve clarity and visual structure. Applying different color palettes (E) might visually differentiate sections but does not offer explicit text labels, which are critical for clear identification.

Overall, markdown text boxes strike the right balance by allowing formatted, distinct, and visible labels within a single dashboard, making it easier for stakeholders to navigate and understand the different operational environments.

Question 10:

A data analyst needs to build SQL queries and generate data visualizations on the Databricks Lakehouse Platform. The compute resources must function in a serverless mode to reduce management overhead. Additionally, the visualizations should be easily incorporated into dashboards for monitoring and presentation.

Which Databricks service best fulfills these requirements?

A. Delta Lake
B. Databricks Notebooks
C. Tableau
D. Databricks Machine Learning
E. Databricks SQL

Correct Answer: E

Explanation:

For a data analyst aiming to write SQL queries, build visualizations, and leverage serverless compute on the Databricks Lakehouse Platform, Databricks SQL is the optimal choice. This service is specifically designed for analysts working with structured data, offering an intuitive SQL interface that enables fast querying directly on data stored in the Lakehouse.

A key advantage of Databricks SQL is its support for serverless compute. This means the platform automatically manages resource provisioning and scaling, eliminating the need for the analyst to configure or maintain clusters. This reduces operational complexity and allows faster query execution, especially when workload demands fluctuate.

Databricks SQL also features built-in visualization tools that let users easily transform query results into charts like bar graphs, pie charts, and scatter plots. These visualizations can then be assembled into dashboards for real-time monitoring and sharing of insights, providing an integrated experience without requiring third-party software.

The other options do not fully meet the analyst’s needs. Delta Lake (A) focuses on reliable data storage and versioning but doesn’t provide query or visualization capabilities. Databricks Notebooks (B) are powerful for collaborative data science and coding but lack a dedicated, serverless SQL querying environment and seamless dashboard visualization integration. Tableau (C) is a third-party visualization tool that would require additional setup and is not native to Databricks. Databricks Machine Learning (D) is tailored toward model development and deployment, not SQL-based querying or dashboard creation.

Thus, Databricks SQL offers a comprehensive, efficient, and serverless solution that meets all the analyst’s needs for SQL querying, visualization, and dashboard integration within the Databricks Lakehouse ecosystem.