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Databricks Databricks-Certified-Data-Analyst-Associate Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Data Analyst Associate Exam
Exam Number:	Databricks-Certified-Data-Analyst-Associate
Certificate Validity Period:	2 years
Exam Format:	Multiple Choice
Available Languages:	English
Exam Price:	$200 USD
Exam Duration:	90 minutes
Related Certifications:	Databricks Certified Machine Learning Associate Databricks Certified Data Engineer Associate
Real Exam Qty:	45
Passing Score:	70%
Sample Questions:	Databricks Databricks-Certified-Data-Analyst-Associate Sample Questions
Exam Way:	Online proctored or test center proctored
Pre Condition:	No formal prerequisites. Databricks recommends 6+ months of hands-on experience with data analysis and Databricks SQL.
Official Syllabus URL:	https://www.databricks.com/learn/certification/data-analyst-associate

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

Topic	Details
Topic 1	Data Management: The topic describes Delta Lake as a tool for managing data files, Delta Lake manages table metadata, benefits of Delta Lake within the Lakehouse, tables on Databricks, a table owner’s responsibilities, and the persistence of data. It also identifies management of a table, usage of Data Explorer by a table owner, and organization-specific considerations of PII data. Lastly, the topic it explains how the LOCATION keyword changes, usage of Data Explorer to secure data.
Topic 2	Databricks SQL: This topic discusses key and side audiences, users, Databricks SQL benefits, complementing a basic Databricks SQL query, schema browser, Databricks SQL dashboards, and the purpose of Databricks SQL endpoints warehouses. Furthermore, the delves into Serverless Databricks SQL endpoint warehouses, trade-off between cluster size and cost for Databricks SQL endpoints warehouses, and Partner Connect. Lastly it discusses small-file upload, connecting Databricks SQL to visualization tools, the medallion architecture, the gold layer, and the benefits of working with streaming data.
Topic 3	Data Visualization and Dashboarding: Sub-topics of this topic are about of describing how notifications are sent, how to configure and troubleshoot a basic alert, how to configure a refresh schedule, the pros and cons of sharing dashboards, how query parameters change the output, and how to change the colors of all of the visualizations. It also discusses customized data visualizations, visualization formatting, Query Based Dropdown List, and the method for sharing a dashboard.
Topic 4	SQL in the Lakehouse: It identifies a query that retrieves data from the database, the output of a SELECT query, a benefit of having ANSI SQL, access, and clean silver-level data. It also compares and contrasts MERGE INTO, INSERT TABLE, and COPY INTO. Lastly, this topic focuses on creating and applying UDFs in common scaling scenarios.
Topic 5	Analytics applications: It describes key moments of statistical distributions, data enhancement, and the blending of data between two source applications. Moroever, the topic also explains last-mile ETL, a scenario in which data blending would be beneficial, key statistical measures, descriptive statistics, and discrete and continuous statistics.

Databricks Certified Data Analyst Associate Exam Sample Questions (Q34-Q39):

NEW QUESTION # 34
In which of the following situations will the mean value and median value of variable be meaningfully different?

A. When the variable is of the boolean type
B. When the variable contains no outliers
C. When the variable contains no missing values
D. When the variable contains a lot of extreme outliers
E. When the variable is of the categorical type

Answer: D

Explanation:
The mean value of a variable is the average of all the values in a data set, calculated by dividing the sum of the values by the number of values. The median value of a variable is the middle value of the ordered data set, or the average of the middle two values if the data set has an even number of values. The mean value is sensitive to outliers, which are values that are very different from the rest of the data. Outliers can skew the mean value and make it less representative of the central tendency of the data. The median value is more robust to outliers, as it only depends on the middle values of the data. Therefore, when the variable contains a lot of extreme outliers, the mean value and the median value will be meaningfully different, as the mean value will be pulled towards the outliers, while the median value will remain close to the majority of the data1. Reference: Difference Between Mean and Median in Statistics (With Example) - BYJU'S

NEW QUESTION # 35
A data analyst runs the following command:
INSERT INTO stakeholders.suppliers TABLE stakeholders.new_suppliers;
What is the result of running this command?

A. The suppliers table now contains the data from the new suppliers table, and the new suppliers table now contains the data from the suppliers table.
B. The command fails because it is written incorrectly.
C. The suppliers table now contains both the data it had before the command was run and the data from the new suppliers table, and any duplicate data is deleted.
D. The suppliers table now contains only the data from the new suppliers table.
E. The suppliers table now contains both the data it had before the command was run and the data from the new suppliers table, including any duplicate data.

Answer: B

Explanation:
The command INSERT INTO stakeholders.suppliers TABLE stakeholders.new_suppliers is not a valid syntax for inserting data into a table in Databricks SQL. According to the documentation12, the correct syntax for inserting data into a table is either:
INSERT { OVERWRITE | INTO } [ TABLE ] table_name [ PARTITION clause ] [ ( column_name [, ...] ) | BY NAME ] query INSERT INTO [ TABLE ] table_name REPLACE WHERE predicate query The command in the question is missing the OVERWRITE or INTO keyword, and the query part that specifies the source of the data to be inserted. The TABLE keyword is optional and can be omitted. The PARTITION clause and the column list are also optional and depend on the table schema and the data source. Therefore, the command in the question will fail with a syntax error.
Reference:
INSERT | Databricks on AWS
INSERT - Azure Databricks - Databricks SQL | Microsoft Learn

NEW QUESTION # 36
A data analyst has created a user-defined function using the following line of code:
CREATE FUNCTION price(spend DOUBLE, units DOUBLE)
RETURNS DOUBLE
RETURN spend / units;
Which of the following code blocks can be used to apply this function to the customer_spend and customer_units columns of the table customer_summary to create column customer_price?

A. SELECT double(price(customer_spend, customer_units)) AS customer_price FROM customer_summary
B. SELECT function(price(customer_spend, customer_units)) AS customer_price FROM customer_summary
C. SELECT PRICE customer_spend, customer_units AS customer_price FROM customer_summary
D. SELECT price(customer_spend, customer_units) AS customer_price FROM customer_summary
E. SELECT price FROM customer_summary

Answer: D

Explanation:
A user-defined function (UDF) is a function defined by a user, allowing custom logic to be reused in the user environment1. To apply a UDF to a table, the syntax is SELECT udf_name(column_name) AS alias FROM table_name2. Therefore, option E is the correct way to use the UDF price to create a new column customer_price based on the existing columns customer_spend and customer_units from the table customer_summary. References:
* What are user-defined functions (UDFs)?
* User-defined scalar functions - SQL
V

NEW QUESTION # 37
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 = exploded_df.select( " record_datetime " , " record_exploded " )
B. Selects nested fields before correctly creating the exploded column.
C. 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 " )
D. Uses withColumn, but selects sensor_id, status, and health as if they are already top-level columns.

Answer: C

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 # 38
Which of the following should data analysts consider when working with personally identifiable information (PII) data?

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

Answer: E

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. Reference:
How to Use Databricks to Encrypt and Protect PII Data
Automating Sensitive Data (PII/PHI) Detection
Databricks Certified Data Analyst Associate

NEW QUESTION # 39
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