Databricks-DA-Assoc Understanding the Databricks Platform — Timed 10 Questions

Question 1

A data analyst needs to share a dashboard with stakeholders who do not have access to the underlying Databricks workspace. Which feature should the analyst use to enable this access?

Accepted Answer

Configure the dashboard for 'Share with public' or specific external guest access.. Databricks Dashboards (formerly Lakeview) allow analysts to share dashboards with any user or group in the Databricks account, even if they do not have access to the specific workspace where the dashboard was created. This requires the workspace to be enabled for identity federation. This feature provides a secure, read-only experience for stakeholders without requiring them to be workspace members.

Answer

Export the dashboard as a PDF and email it directly.

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Provide credentials to a service account.

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Grant full workspace access to the stakeholders.

Question 2

Refer to the exhibit. What is the purpose of using the Databricks CLI command shown?

Accepted Answer

To list the contents of a specific folder in the workspace.. The Databricks Certified Data Analyst Associate exam focuses on Databricks SQL, data visualization, and workspace navigation via the UI. CLI operations are part of the Data Engineer or Platform Administrator tracks. Furthermore, the question refers to an exhibit that is not provided, making it impossible to verify the specific command or the correctness of the answer.

Answer

To install a new library on the cluster.

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To trigger an automated job execution.

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To view the logs of a failed cluster.

Question 3

A data analyst is building a dashboard in Databricks SQL and wants the underlying query results to refresh automatically every 15 minutes so stakeholders see near-real-time sales figures. The source is a Delta table that is updated by a streaming job throughout the day. Which Databricks SQL feature should the analyst configure?

Accepted Answer

A scheduled query with a refresh schedule on the dashboard. Databricks SQL dashboards can be refreshed by scheduled queries that run on a cadence and update the dashboard's datasets. A 15-minute schedule aligns with the near-real-time requirement and works against a Delta table that is continuously updated, without requiring manual refreshes or additional pipeline infrastructure.

Answer

Query caching on the SQL warehouse

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A materialized view with automatic refresh

Answer

Delta Live Tables with a continuous pipeline

Question 4

A data analyst has been using a shared all-purpose cluster to explore data but now must schedule a recurring dashboard refresh that runs every morning at 6:00 AM without manual intervention. The cluster is currently started manually each day. Which Databricks compute resource should the analyst use instead for the scheduled workload?

Accepted Answer

A Databricks SQL Warehouse configured with a schedule and auto-stop enabled. A Databricks SQL Warehouse is the correct compute for scheduled SQL analytics and dashboard refreshes. It supports scheduled start/stop, auto-stop, and scaling, so the 6:00 AM refresh runs without a person starting a cluster. All-purpose and personal compute are interactive resources, and a job cluster is for job execution rather than serving dashboard queries.

Answer

The existing all-purpose cluster with a cron schedule added to the notebook

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A job cluster created automatically for each scheduled notebook run

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A personal compute resource assigned only to the analyst's user account

Question 5

A data analyst is preparing to query a large sales Delta table from Databricks SQL. The analyst wants to reduce the volume of data scanned and lower query cost. Which TWO Databricks platform capabilities should the analyst rely on? (Choose two.)

Accepted Answer

Delta Lake data skipping using column statistics and file-level min/max metadata. Delta Lake data skipping prunes files using stored min/max statistics so fewer files are read, and Photon vectorizes execution to process the remaining data faster. Together they reduce bytes scanned and warehouse-seconds consumed when querying the large sales Delta table, directly lowering cost. The other choices affect idle billing, parallelism, or observability rather than scan volume.

Answer

Increasing the number of worker nodes on the SQL warehouse

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Setting the SQL warehouse to auto-stop after a long idle period

Answer

Enabling verbose query history logging for the warehouse

Question 6

A data analyst is new to a Databricks workspace and needs to run an ad-hoc SQL query on a table stored in Unity Catalog. The analyst wants to ensure that the query runs on a cluster configured with the appropriate runtime and libraries, and that the cluster is automatically terminated after a period of inactivity to save costs. Which cluster type should the analyst use?

Accepted Answer

All-purpose cluster. All-purpose clusters are the correct choice for interactive ad-hoc SQL queries because they allow custom runtime and library configurations and support auto-termination after inactivity. SQL warehouses are for SQL-only workloads without custom libraries, job clusters are for automated jobs, and instance pools are a supporting resource, not a cluster type for running queries.

Answer

SQL warehouse

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Job cluster

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Instance pool

Question 7

A data analyst is working in a Databricks workspace and needs to ensure that their notebook code is version-controlled and collaborative. Which TWO actions should they take?

Accepted Answer

Use the built-in Databricks Repos feature to connect to a Git provider.. Integrating version control and collaboration is essential for professional data engineering workflows. By linking notebooks to Git providers, analysts ensure that changes are tracked, auditable, and easily revertible. This standard practice prevents accidental data loss and fosters teamwork, allowing multiple contributors to manage code repositories effectively within the Databricks environment while maintaining a single source of truth for all analytical project documentation and complex transformation logic.

Answer

Copy and paste code into a local text file.

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Create a new notebook file for every code change.

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Download the notebook to their local machine every hour.

Question 8

Refer to the exhibit. Given the provided JSON configuration for a Databricks cluster, what is the primary use case for this resource?

Accepted Answer

Interactive data analysis and notebook development.. Refer to the exhibit. The configuration shows an all-purpose cluster with autoscaling enabled. All-purpose clusters are primarily used for interactive development and data exploration within notebooks. Because they consume more DBU resources compared to job clusters, setting an idle termination limit is a cost-optimization best practice. Understanding cluster types is fundamental for managing platform costs and ensuring that resources are allocated appropriately based on the specific requirements of the workload being executed.

Answer

Running automated production ETL jobs.

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Long-running streaming data ingestion.

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Batch processing of large ML models.

Question 9

Which component of the Databricks Data Intelligence Platform allows users to discover, govern, and share data across the entire organization?

Accepted Answer

Unity Catalog. Unity Catalog is the centralized governance solution for data, analytics, and AI on the Databricks platform. It provides a single interface to manage permissions, track data lineage, and discover assets. Mastery of Unity Catalog is essential for any data analyst as it ensures that data access is secure, compliant, and transparent, effectively bridging the gap between raw storage and meaningful, governed business insights for all users.

Answer

Databricks SQL

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Delta Live Tables

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Compute Clusters

Question 10

Which THREE of the following are benefits of using Delta Lake over standard Parquet files in Databricks?

Accepted Answer

ACID transaction support. Delta Lake introduces ACID transactions, time travel, and schema enforcement to the data lake, transforming it into a lakehouse. These features are critical for maintaining data integrity in complex production environments. Understanding these benefits allows analysts to make informed decisions about storage formats, ensuring that the data platform remains reliable, scalable, and capable of handling complex analytical requirements without risking data corruption or inconsistency.

Answer

Automatic data compression to non-standard formats

Answer

The ability to run queries without a compute engine