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

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

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q11-Q16):

NEW QUESTION # 11
Which of the following should data analysts consider when working with personally identifiable information (PII) data?

A. Organization-specific best practices for Pll data
B. None of these considerations
C. All of these considerations
D. Legal requirements for the area in which the data was collected
E. Legal requirements for the area in which the analysis is being performed

Answer: C

Explanation:
Data analysts should consider all of these factors when working with PII data, as they may affect the data security, privacy, compliance, and quality. PII data is any information that can be used to identify a specific individual, such as name, address, phone number, email, social security number, etc. PII data may be subject to different legal and ethical obligations depending on the context and location of the data collection and analysis. For example, some countries or regions may have stricter data protection laws than others, such as the General Data Protection Regulation (GDPR) in the European Union. Data analysts should also follow the organization-specific best practices for PII data, such as encryption, anonymization, masking, access control, auditing, etc. These best practices can help prevent data breaches, unauthorized access, misuse, or loss of PII data. References:
* How to Use Databricks to Encrypt and Protect PII Data
* Automating Sensitive Data (PII/PHI) Detection
* Databricks Certified Data Analyst Associate

NEW QUESTION # 12
Consider the following two statements:
Statement 1:

Statement 2:
Which of the following describes how the result sets will differ for each statement when they are run in Databricks SQL?

A. The first statement will return all data from the customers table and matching data from the orders table. The second statement will return all data from the orders table and matching data from the customers table. Any missing data will be filled in with NULL.
B. Both statements will fail because Databricks SQL does not support those join types.
C. There is no difference between the result sets for both statements.
D. When the first statement is run, only rows from the customers table that have at least one match with the orders table on customer_id will be returned. When the second statement is run, only those rows in the customers table that do not have at least one match with the orders table on customer_id will be returned.
E. When the first statement is run, all rows from the customers table will be returned and only the customer_id from the orders table will be returned. When the second statement is run, only those rows in the customers table that do not have at least one match with the orders table on customer_id will be returned.

Answer: D

Explanation:
Based on the images you sent, the two statements are SQL queries for different types of joins between the customers and orders tables. A join is a way of combining the rows from two table references based on some criteria. The join type determines how the rows are matched and what kind of result set is returned. The first statement is a query for a LEFT SEMI JOIN, which returns only the rows from the left table reference (customers) that have a match with the right table reference (orders) on the join condition (customer_id). The second statement is a query for a LEFT ANTI JOIN, which returns only the rows from the left table reference (customers) that have no match with the right table reference (orders) on the join condition (customer_id).
Therefore, the result sets for the two statements will differ in the following way:
* The first statement will return a subset of the customers table that contains only the customers who have placed at least one order. The number of rows returned will be less than or equal to the number of rows in the customers table, depending on how many customers have orders. The number of columns returned will be the same as the number of columns in the customers table, as the LEFT SEMI JOIN does not include any columns from the orders table.
* The second statement will return a subset of the customers table that contains only the customers who have not placed any order. The number of rows returned will be less than or equal to the number of rows in the customers table, depending on how many customers have no orders. The number of columns returned will be the same as the number of columns in the customers table, as the LEFT ANTI JOIN does not include any columns from the orders table.
The other options are not correct because:
* A. The first statement will not return all data from the customers table, as it will exclude the customers who have no orders. The second statement will not return all data from the orders table, as it will exclude the orders that have a matching customer. Neither statement will fill in any missing data with NULL, as they do not return any columns from the other table.
* C. There is a difference between the result sets for both statements, as explained above. The LEFT SEMI JOIN and the LEFT ANTI JOIN are not equivalent operations and will produce different outputs.
* D. Both statements will not fail, as Databricks SQL does support those join types. Databricks SQL supports various join types, including INNER, LEFT OUTER, RIGHT OUTER, FULL OUTER, LEFT SEMI, LEFT ANTI, and CROSS. You can also use NATURAL, USING, or LATERAL keywords to specify different join criteria.
* E. The first statement will not return only the customer_id from the orders table, as it will return all columns from the customers table. The second statement is correct, but it is not the only difference between the result sets.
References: JOIN | Databricks on AWS, JOIN - Azure Databricks - Databricks SQL | Microsoft Learn, array_join function | Databricks on AWS, Hints | Databricks on AWS

NEW QUESTION # 13
After running DESCRIBE EXTENDED accounts.customers;, the following was returned:

Now, a data analyst runs the following command:
DROP accounts.customers;
Which of the following describes the result of running this command?

A. The accounts.customers table is removed from the metastore, but the underlying data files are untouched.
B. The accounts.customers table is removed from the metastore, and the underlying data files are deleted.
C. Running SELECT * FROM accounts.customers will return all rows in the table.
D. Running SELECT * FROM delta. `dbfs:/stakeholders/customers` results in an error.
E. All files with the .customers extension are deleted.

Answer: A

Explanation:
the accounts.customers table is an EXTERNAL table, which means that it is stored outside the default warehouse directory and is not managed by Databricks. Therefore, when you run the DROP command on this table, it only removes the metadata information from the metastore, but does not delete the actual data files from the file system. This means that you can still access the data using the location path (dbfs:/stakeholders
/customers) or create another table pointing to the same location. However, if you try to query the table using its name (accounts.customers), you will get an error because the table no longer exists in the metastore. References: DROP TABLE | Databricks on AWS, Best practices for dropping a managed Delta Lake table - Databricks

NEW QUESTION # 14
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. Copy the Query and create one data visualization per query.
B. Decide on a single data visualization to add to the dashboard.
C. Add two separate visualizations to the dashboard based on the same Query.
D. Alter the Query to return two separate sets of results.

Answer: C

Explanation:
Databricks SQL allows you to create multiple visualizations from a single query result. These visualizations can be customized independently and each can be added to a dashboard. This feature is explicitly supported and recommended in Databricks' documentation on dashboards and visualization workflows, enabling flexible reporting without duplicating queries.

NEW QUESTION # 15
A data analyst is processing a complex aggregation on a table with zero null values and the query returns the following result:

Which query did the analyst execute in order to get this result?

Answer: C

Explanation:
Option D is correct because the table has zero real null values, but the result contains null values representing subtotal and grand-total rows. That behavior is produced by WITH CUBE, which creates aggregations for combinations of grouping columns, including (group_1, group_2), (group_1), (group_2), and the grand total ().
The Databricks SQL documentation states that GROUP BY supports advanced aggregations through CUBE, and that CUBE is shorthand for grouping sets. Option A only returns detailed groups. Options B and C use invalid syntax in Databricks SQL. Reference: Databricks GROUP BY clause documentation.

NEW QUESTION # 16
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