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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
Passing Score:70%
Available Languages:English
Related Certifications:Databricks Certified Machine Learning Associate
Databricks Certified Data Engineer Associate
Certificate Validity Period:2 years
Exam Price:$200 USD
Real Exam Qty:45–60
Exam Duration:90 minutes
Exam Format:Multiple Select, Multiple Choice
Recommended Training:Databricks Academy - Data Analyst Learning Path
Databricks SQL Training Courses
Exam Registration:Kryterion Webassessor Registration
Databricks Certification Portal
Sample Questions:Databricks Databricks-Certified-Data-Analyst-Associate Sample Questions
Exam Way:Online proctored exam via remote monitoring (Kryterion Webassessor platform)
Pre Condition:No formal prerequisites required, but familiarity with SQL and basic data analysis concepts is recommended.
Official Syllabus URL:https://www.databricks.com/learn/certification

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

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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.
Topic 4
  • 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 5
  • 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.

Databricks Certified Data Analyst Associate Exam Sample Questions (Q72-Q77):

NEW QUESTION # 72
Which of the following approaches can be used to ingest data directly from cloud-based object storage?

Answer: A

Explanation:
External tables are tables that are defined in the Databricks metastore using the information stored in a cloud object storage location. External tables do not manage the data, but provide a schema and a table name to query the data. To create an external table, you can use the CREATE EXTERNAL TABLE statement and specify the object storage path to the LOCATION clause. For example, to create an external table named ext_table on a Parquet file stored in S3, you can use the following statement:
SQL
CREATE EXTERNAL TABLE ext_table (
col1 INT,
col2 STRING
)
STORED AS PARQUET
LOCATION ' s3://bucket/path/file.parquet '
AI-generated code. Review and use carefully. More info on FAQ.
External tables


NEW QUESTION # 73
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?

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 # 74
What describes the variance of a set of values?

Answer: B

Explanation:
Variance is a statistical measure that quantifies the dispersion or spread of a set of values around their mean (central value). It is calculated by taking the average of the squared differences between each value and the mean of the dataset. A higher variance indicates that the data points are more spread out from the mean, while a lower variance suggests that they are closer to the mean. This measure is fundamental in statistics to understand the degree of variability within a dataset.WikipediaWikipedia+1Investopedia+1 Reference: Variance - Wikipedia


NEW QUESTION # 75
A data analyst is working with the following table my_table:
customer_name dollars_spent
Hex Sprockets [125.34, 100.15, 9003.99]
Dented Fenders [16.99, 200.85, 33.49, 88.17]
The analyst wants to divide each value in the dollars_spent array by 100 to get the spend in terms of hundreds of dollars using the following code block:
SELECT
customer_name,
_______
FROM my_table;
Which line of code can be used to fill in the blank so that the above code block successfully completes the task?

Answer: C

Explanation:
The correct answer is C because TRANSFORM applies a lambda function to each element of an array and returns a new array. The expression value - > value / 100 means: for each element named value in dollars_spent, divide that element by 100. The result is then aliased as hundreds_spent. Options A, B, and D do not use the required lambda syntax correctly.
Official documentation extract used: Databricks states that transform returns an array and that the lambda function "produces a new value for each element."


NEW QUESTION # 76
Which data lakehouse feature results in improved data quality over a traditional data lake?

Answer: C

Explanation:
Option D is correct. ACID-compliant transactions improve reliability and data quality because writes are consistent and atomic rather than leaving partially written or corrupted states. Open formats and SQL access are important lakehouse capabilities, but the feature most directly tied to improved data quality over a traditional data lake is ACID transactions. Official Databricks extract: Delta Lake extends Parquet with a transaction log for "ACID transactions," and Databricks states that Delta Lake adds ACID transactions and schema evolution for reliable, high-quality data.


NEW QUESTION # 77
......

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