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

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

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q29-Q34):

NEW QUESTION # 29
A data analyst runs the following command:
SELECT age, country
FROM my_table
WHERE age > = 75 AND country = ' canada ' ;
Which of the following tables represents the output of the above command?

Answer: C

Explanation:
The SQL query provided is designed to filter out records from "my_table" where the age is 75 or above and the country is Canada. Since I can't view the content of the links provided directly, I need to rely on the image attached to this question for context. Based on that, Option E (the image attached) represents a table with columns "age" and "country", showing records where age is 75 or above and country is Canada. References:
The answer can be inferred from understanding SQL queries and their outputs as per Databricks documentation: Databricks SQL

NEW QUESTION # 30
A data analyst has been asked to count the number of customers in each region and has written the following query:

If there is a mistake in the query, which of the following describes the mistake?

A. The query is using count('). which will count all the customers in the customers table, no matter the region.
B. The query is selecting region but region should only occur in the ORDER BY clause.
C. The query is using ORDER BY. which is not allowed in an aggregation.
D. There are no mistakes in the query.
E. The query is missing a GROUP BY region clause.

Answer: E

Explanation:
In the provided SQL query, the data analyst is trying to count the number of customers in each region. However, they made a mistake by not including the "GROUP BY" clause to group the results by region. Without this clause, the query will not return counts for each distinct region but rather an error or incorrect result. Reference: The need for a GROUP BY clause in such queries can be understood from Databricks SQL documentation: Databricks SQL.
I also noticed that you uploaded an image with your question. The image shows a snippet of an SQL query written in plain text on a white background. The query is attempting to select regions and count customers from a "customers" table and order the results by region. There's no visible syntax highlighting or any other color - it's monochromatic. The query is the same as the one in your question. I'm not sure why you included the image, but maybe you wanted to show me the exact format of your query. If so, you can also use code blocks to display formatted content such as SQL queries. For example, you can write:
SELECT region, count(*) AS number_of_customers
FROM customers
ORDER BY region;
This way, you can avoid uploading images and make your questions more clear and concise. I hope this helps.

NEW QUESTION # 31
A data analyst wants the following output:
customer_name
number_of_orders
John Doe
388
Zhang San
234
Which statement will produce this output?

A. SELECT customerjiame, (order_id) number_of_ordersFROM customersJOIN ordersON customers.
customer_id = orders.customer_id;
B. SELECT customerjiame, count(order_id)FROM customersJOIN ordersON customers.customer_id = orders.customer_id GROUP BY customerjiame;
C. SELECT customer_name, count(order_id) number_of_ordersFROM customersJOIN ordersON customers.customer_id = orders.customer_id USE customer_name;
D. SELECT customer_name, count(order_id) AS number_of_ordersFROM customersJOIN ordersON customers.customer_id = orders.customer_idGROUP BY customer_name;

Answer: D

Explanation:
To get the number of orders per customer, you need to join the customers and orders tables on the customer_id, count the order_id, and group the results by customer_name. The correct SQL syntax, as outlined in Databricks SQL documentation, is to use GROUP BY on the selected customer field and use COUNT for aggregation. Only option A does this correctly, while the other options contain syntax errors or incorrect field names.

NEW QUESTION # 32
A Data Analyst is working on employees_df and needs to add a new column where a 10% tax is calculated on the salary. Additionally, the DataFrame contains the column age, which is not needed.
Which code fragment adds the tax column and removes the age column?

A. employees_df = employees_df.withColumn( " tax " , employees_df.salary * 10).drop( " age " )
B. employees_df = employees_df.withColumn( " tax " , employees_df.salary * 0.1).drop( " age " )
C. employees_df = employees_df.withColumn( " tax " , lit(0.1) * col( " salary " )).dropField( " age " )
D. employees_df = employees_df.withColumn( " tax " , employees_df.salary * 10).dropField( " age " )

Answer: B

Explanation:
Option B is correct after correcting the uploaded typo withcolumn to withColumn. A 10% tax is calculated by multiplying salary by 0.1, not by 10. The age column should be removed using .drop( " age " ). Options A and D multiply salary by 10, which calculates 1000%, not 10%. Options A and C also use dropField, which is not the correct DataFrame method for removing a top-level column. Official Databricks PySpark documentation states that withColumn adds or replaces a column and that drop(*cols) returns a new DataFrame without the specified columns.

NEW QUESTION # 33
A data analyst is working on a DataFrame named dates_df and needs to add a new column, date, derived from the timestamp field.
Which code fragment should be used to extract the date from a timestamp?

A. dates_df.withColumn( " date " , f.unix_timestamp( " timestamp " )).show()
B. dates_df.withColumn( " date " , f.to_date( " timestamp " )).show()
C. dates_df.withColumn( " date " , f.from_unixtime( " timestamp " )).show()
D. dates_df.withColumn( " date " , f.date_format( " timestamp " , " yyyy-MM-dd " )).show()

Answer: B

Explanation:
Option B is correct. The function to_date converts a timestamp or date-like expression to a date value, which matches the requirement to extract the date from the timestamp field. unix_timestamp converts to a Unix timestamp value, date_format formats a date or timestamp as a string, and from_unixtime converts Unix time into a timestamp/string representation. Official Databricks documentation states that to_date(expr [, fmt]) returns the expression cast to a date.

NEW QUESTION # 34
......

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