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

Section	Weight	Objectives
Topic 1: Data Modeling with Databricks SQL	5%	- Modeling Concepts 1. Fact and Dimension Tables 2. Views and Relationships 3. Data Organization
Topic 2: Creating Dashboards and Visualizations	16%	- Visualization 1. Dashboards 2. Sharing and Publishing 3. Alerts and Filters 4. Charts and Graphs
Topic 3: Securing Data	8%	- Security and Access Control 1. Unity Catalog Security 2. Permissions and Roles 3. Data Access Policies
Topic 4: Importing Data	5%	- Data Import 1. Creating Tables from Imported Data 2. Loading CSV and JSON Data 3. Uploading Files
Topic 5: Analyzing Queries	15%	- Query Analysis 1. Monitoring Query Execution 2. Performance Tuning 3. Reading Query Plans
Topic 6: Developing AI/BI Genie Spaces	12%	- AI and BI Features 1. Workspace Configuration 2. AI/BI Genie 3. Natural Language Queries
Topic 7: Managing Data	8%	- Data Management 1. Discovering Data Assets 2. Handling Missing and Invalid Data 3. Tagging and Lineage 4. Cleaning Data with SQL
Topic 8: Understanding of Databricks Data Intelligence Platform	11%	- Platform Components 1. Delta Lake 2. Databricks SQL 3. Mosaic AI 4. Lakeflow Jobs 5. Unity Catalog - Catalog and Governance 1. Views and Certified Tables 2. Managed and External Tables 3. Data Lineage 4. Catalogs and Schemas
Topic 9: Executing Queries Using Databricks SQL and SQL Warehouses	20%	- SQL Querying 1. Joins and Subqueries 2. SQL Warehouses 3. Query Optimization 4. Writing SQL Queries 5. Filtering and Aggregation

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q22-Q27):

NEW QUESTION # 22
A data engineer wants to create a relational object by pulling data from two tables. The relational object does not need to be used by other data engineers in other sessions. In order to save on storage costs, the data engineer wants to avoid copying and storing physical data.
Which of the following relational objects should the data engineer create?

A. Temporary view
B. Spark SQL Table
C. View
D. Database
E. Delta Table

Answer: A

Explanation:
Option D is correct. A temporary view is session-scoped and does not store physical data. It is suitable when the relational object is only needed in the current session or query context and should not be available to others in other sessions. A regular view also avoids copying physical data, but it is persistent and can be shared beyond the current session. Official Databricks extract: a view is a "virtual table that has no physical data," and temporary views are scoped to the notebook/script or query level and cannot be referenced outside that scope.

NEW QUESTION # 23
A database was created in Databricks SQL using the following statement:
CREATE SCHEMA accounting LOCATION ' dbfs:/accounting/data ' ;
Where will data for this database be stored?

A. dbfs:/accounting/data/accounting.db
B. dbfs:/accounting/data
C. dbfs:/accounting/data.db
D. dbfs:/user/hive/warehouse/accounting.db

Answer: B

Explanation:
Option B is correct. The statement explicitly sets the schema/database location to dbfs:/accounting/data. In Databricks SQL, CREATE SCHEMA supports a LOCATION schema_directory, and the documentation explains that the schema directory is the filesystem path where the schema is created. Because the user supplied LOCATION ' dbfs:/accounting/data ' , Databricks uses that path instead of the default warehouse directory. Option D would apply only when no custom location is supplied. References: Databricks CREATE SCHEMA documentation.

NEW QUESTION # 24
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. Reference: 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 # 25
An analyst writes a query that contains a query parameter. They then add an area chart visualization to the query. While adding the area chart visualization to a dashboard, the analyst chooses " Dashboard Parameter " for the query parameter associated with the area chart.
Which of the following statements is true?

A. The area chart will use whatever value is chosen on the dashboard at the time the area chart is added to the dashboard.
B. The area chart will use whatever is selected in the Dashboard Parameter along with all of the other visualizations in the dashboard that use the same parameter.
C. The area chart will convert to a Dashboard Parameter.
D. The area chart will use whatever value is input by the analyst when the visualization is added to the dashboard. The parameter cannot be changed by the user afterwards.
E. The area chart will use whatever is selected in the Dashboard Parameter while all or the other visualizations will remain changed regardless of their parameter use.

Answer: B

Explanation:
A Dashboard Parameter is a parameter that is configured for one or more visualizations within a dashboard and appears at the top of the dashboard. The parameter values specified for a Dashboard Parameter apply to all visualizations reusing that particular Dashboard Parameter1. Therefore, if the analyst chooses "Dashboard Parameter" for the query parameter associated with the area chart, the area chart will use whatever is selected in the Dashboard Parameter along with all of the other visualizations in the dashboard that use the same parameter. This allows the user to filter the data across multiple visualizations using a single parameter widget2. References: Databricks SQL dashboards, Query parameters

NEW QUESTION # 26
A data scientist wants to tune a set of hyperparameters for a machine learning model. They have wrapped a Spark ML model in the objective function objective_function, and they have defined the search space search_space.
As a result, they have the following code block:
num_evals = 100
trials = SparkTrials()
best_hyperparam = fmin(
fn=objective_function,
space=search_space,
algo=tpe.suggest,
max_evals=num_evals,
trials=trials
)
Which of the following changes do they need to make to the above code block in order to accomplish the task?

A. Reduce num_evals to be less than 10
B. Change SparkTrials() to Trials()
C. Change fmin() to fmax()
D. Remove the algo=tpe.suggest argument
E. Remove the trials=trials argument

Answer: B

Explanation:
Option A is correct. The model being tuned is a Spark ML model, which is already distributed. SparkTrials is intended to distribute independent single-machine trials across Spark workers. For distributed ML algorithms such as Spark MLlib/Spark ML, Hyperopt should run trials from the driver so each trial can access the full cluster resources. Therefore, SparkTrials() should be changed to Trials(). Official Databricks documentation explains that this setup works for distributed machine learning algorithms including Apache Spark MLlib, and the Databricks notebook guidance states that SparkTrials is incompatible for that distributed-training pattern because each trial must be evaluated on the driver node.

NEW QUESTION # 27
......

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