Databricks-DA-Assoc Understanding the Databricks Platform — Timed 15 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.

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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.

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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.

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Query caching on the SQL warehouse

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A materialized view with automatic refresh

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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.

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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.

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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

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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.

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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.

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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.

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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.

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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.

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Automatic data compression to non-standard formats

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The ability to run queries without a compute engine

Question 11

A data analyst is troubleshooting a performance issue in a notebook. The query runs slowly when processing a large table. Which approach should the analyst take to improve performance?

Accepted Answer

Run the 'OPTIMIZE' command on the Delta table.. Performance tuning in Databricks often involves examining how data is stored and accessed. Identifying bottlenecks like small files or lack of partitioning is key. By using built-in optimization commands, analysts can improve query speed significantly. This process is a core skill for any Databricks analyst, as it directly impacts the cost of compute resources and the user experience when working with massive, real-world datasets.

Answer

Increase the number of users connected to the workspace.

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Delete the table and re-create it without Delta Lake.

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Force the notebook to run on a single-node cluster.

Question 12

Refer to the exhibit. What is the most appropriate action to resolve this access issue?

Accepted Answer

Request the workspace administrator to grant appropriate privileges in Unity Catalog.. Refer to the exhibit. This error indicates a failure in the Unity Catalog permission model. When a user lacks the necessary 'USE' or 'SELECT' grants on a catalog, they are blocked from accessing any data within it. Understanding how Unity Catalog manages access at the catalog, schema, and table levels is vital for analysts to troubleshoot access issues and comply with organizational security policies.

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Change the cluster configuration to use a different Spark version.

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Move the data to a local file in DBFS.

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Re-create the table in a different schema.

Question 13

A data analyst needs to ensure that sensitive information in a table is not visible to unauthorized users. Which Unity Catalog feature is the most efficient way to achieve this at the row level?

Accepted Answer

Apply Row Filters and Column Masks in Unity Catalog.. Row-level security (RLS) and column-level security are fundamental features of Unity Catalog that enable fine-grained access control. By applying these policies, organizations can ensure compliance with privacy regulations while maintaining data usability. Understanding how to define these filters allows analysts to secure data effectively without creating multiple copies of the same dataset for different user groups, which simplifies maintenance and ensures consistency.

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Create a separate view for each user group.

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Use hard-coded SQL 'WHERE' clauses in every notebook.

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Move sensitive data to a completely separate, private workspace.

Question 14

What is the primary function of the 'Databricks Assistant' within the notebook environment?

Accepted Answer

To assist in writing, debugging, and explaining notebook code.. The Databricks Assistant is an AI-powered coding companion designed to help users write, debug, and optimize code faster. By leveraging context-aware suggestions, it helps analysts become more efficient. Understanding how to use the Assistant is beneficial for streamlining development workflows, as it assists with complex syntax, troubleshooting common errors, and generating boilerplate code, allowing the analyst to focus on the core logic of their data analysis tasks.

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To automatically execute all notebooks in a production pipeline.

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To manage user permissions across the entire workspace.

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To store and back up notebook versions automatically.

Question 15

Which TWO of the following are true regarding Databricks SQL Warehouses?

Accepted Answer

They are specifically optimized for SQL-based analytical queries.. SQL Warehouses are specialized compute resources optimized for SQL workloads. Understanding the difference between these and general-purpose clusters is crucial for analysts, as warehouses offer features like serverless startup and auto-stop, which significantly optimize cost and performance. These warehouses enable the platform's BI and dashboarding capabilities, making them a cornerstone of the data analyst's toolkit for delivering consistent, reliable reports to business stakeholders.

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They must be manually started every time a user runs a query.

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They support the same multi-language notebook features as all-purpose clusters.

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They are only available for administrative users.