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Databricks Databricks-Certified-Data-Analyst-Associate Exam Syllabus Topics:

Section	Objectives
Topic 1: Data Governance and Security	- Access control and permissions 1. Row-level and column-level security concepts 2. Unity Catalog basics
Topic 2: Data Management in Lakehouse	- Delta Lake fundamentals 1. ACID transactions and versioning 2. Table optimization concepts - Data ingestion and preparation 1. Batch and streaming ingestion concepts
Topic 3: Data Visualization and Dashboards	- Creating dashboards in Databricks SQL 1. Dashboard configuration and sharing 2. Visualizing query results
Topic 4: Databricks SQL and SQL Analytics	- Data transformation and analysis 1. Window functions 2. Common table expressions (CTEs) - Querying data using SQL in Databricks 1. Joins and aggregations 2. SELECT statements and filtering data

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q103-Q108):

NEW QUESTION # 103
Which of the following describes how Databricks SQL should be used in relation to other business intelligence (BI) tools like Tableau, Power BI, and looker?

A. As a complementary tool for professional-grade presentations
B. As a substitute with less functionality
C. As an exact substitute with the same level of functionality
D. As a complementary tool for quick in-platform Bl work
E. As a complete replacement with additional functionality

Answer: D

Explanation:
Databricks SQL is not meant to replace or substitute other BI tools, but rather to complement them by providing a fast and easy way to query, explore, and visualize data on the lakehouse using the built-in SQL editor, visualizations, and dashboards. Databricks SQL also integrates seamlessly with popular BI tools like Tableau, Power BI, and Looker, allowing analysts to use their preferred tools to access data through Databricks clusters and SQL warehouses. Databricks SQL offers low-code and no-code experiences, as well as optimized connectors and serverless compute, to enhance the productivity and performance of BI workloads on the lakehouse. Reference: Databricks SQL, Connecting Applications and BI Tools to Databricks SQL, Databricks integrations overview, Databricks SQL: Delivering a Production SQL Development Experience on the Lakehouse

NEW QUESTION # 104
In which of the following situations should a data analyst use higher-order functions?

A. When custom logic needs to be applied at scale to array data objects
B. When custom logic needs to be converted to Python-native code
C. When custom logic needs to be applied to simple, unnested data
D. When built-in functions are taking too long to perform tasks
E. When built-in functions need to run through the Catalyst Optimizer

Answer: A

Explanation:
Higher-order functions are a simple extension to SQL to manipulate nested data such as arrays. A higher- order function takes an array, implements how the array is processed, and what the result of the computation will be. It delegates to a lambda function how to process each item in the array. This allows you to define functions that manipulate arrays in SQL, without having to unpack and repack them, use UDFs, or rely on limited built-in functions. Higher-order functions provide a performance benefit over user defined functions. References: Higher-order functions | Databricks on AWS, Working with Nested Data Using Higher Order Functions in SQL on Databricks | Databricks Blog, Higher-order functions - Azure Databricks | Microsoft Learn, Optimization recommendations on Databricks | Databricks on AWS

NEW QUESTION # 105
A data analyst has created a Query in Databricks SQL, and now they want to create two data visualizations from that Query and add both of those data visualizations to the same Databricks SQL Dashboard.
Which of the following steps will they need to take when creating and adding both data visualizations to the Databricks SQL Dashboard?

A. They will need to create two separate dashboards.
B. They will need to decide on a single data visualization to add to the dashboard.
C. They will need to alter the Query to return two separate sets of results.
D. They will need to add two separate visualizations to the dashboard based on the same Query.
E. They will need to copy the Query and create one data visualization per query.

Answer: D

Explanation:
A data analyst can create multiple visualizations from the same query in Databricks SQL by clicking the + button next to the Results tab and selecting Visualization. Each visualization can have a different type, name, and configuration. To add a visualization to a dashboard, the data analyst can click the vertical ellipsis button beneath the visualization, select + Add to Dashboard, and choose an existing or new dashboard. The data analyst can repeat this process for each visualization they want to add to the same dashboard. References: Visualization in Databricks SQL, Visualize queries and create a dashboard in Databricks SQL

NEW QUESTION # 106
A Data Analyst is working on sensor_df; this DataFrame contains two columns: record_datetime timestamp and record array.
Which code fragment returns a DataFrame that splits the record column into separate columns and has one array item per row?

A. exploded_df = sensor_df.withColumn( " record_exploded " , explode( " record " ))exploded_df = exploded_df.select( " record_datetime " , " record_exploded.sensor_id " , " record_exploded.status " , " record_exploded.health " )
B. exploded_df = exploded_df.select( " record_datetime " , " record_exploded " )
C. Uses withColumn, but selects sensor_id, status, and health as if they are already top-level columns.
D. Selects nested fields before correctly creating the exploded column.

Answer: A

Explanation:
Option C is correct after correcting the formatting and typing errors in the uploaded option text. The analyst needs explode( " record " ) because the record column is an array, and the requirement is to return one array item per row. Then the analyst must select fields from the exploded struct using dot notation, such as record_exploded.sensor_id, record_exploded.status, and record_exploded.health. Databricks PySpark documentation states that explode "returns a new row for each element in the given array or map," and withColumn returns a new DataFrame by adding or replacing a column. The select method projects expressions or column names into the resulting DataFrame.

NEW QUESTION # 107
A data analyst has created a Query in Databricks SQL, and now wants to create two data visualizations from that Query and add both of those data visualizations to the same Databricks SQL Dashboard.
Which step will the data analyst need to take when creating and adding both data visualizations to the Databricks SQL Dashboard?

A. Alter the Query to return two separate sets of results.
B. Add two separate visualizations to the dashboard based on the same Query.
C. Copy the Query and create one data visualization per query.
D. Decide on a single data visualization to add to the dashboard.

Answer: B

NEW QUESTION # 108
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