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

Topic
Details

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 contrast 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

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

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 5

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.

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q35-Q40):

NEW QUESTION # 35
A data analysis team is working with the table_bronze SQL table as a source for one of its most complex projects. A stakeholder of the project notices that some of the downstream data is duplicative. The analysis team identifies table_bronze as the source of the duplication.
Which of the following queries can be used to deduplicate the data from table_bronze and write it to a new table table_silver?
A)
CREATE TABLE table_silver AS
SELECT DISTINCT *
FROM table_bronze;
B)
CREATE TABLE table_silver AS
INSERT *
FROM table_bronze;
C)
CREATE TABLE table_silver AS
MERGE DEDUPLICATE *
FROM table_bronze;
D)
INSERT INTO TABLE table_silver
SELECT * FROM table_bronze;
E)
INSERT OVERWRITE TABLE table_silver
SELECT * FROM table_bronze;

A. Option D
B. Option C
C. Option A
D. Option E
E. Option B

Answer: C

Explanation:
Option A uses the SELECT DISTINCT statement to remove duplicate rows from the table_bronze and create a new table table_silver with the deduplicated data. This is the correct way to deduplicate data using Spark SQL12. Option B simply inserts all the rows from table_bronze into table_silver, without removing any duplicates. Option C is not a valid syntax for Spark SQL, as there is no MERGE DEDUPLICATE statement. Option D appends all the rows from table_bronze into table_silver, without removing any duplicates. Option E overwrites the existing data in table_silver with the data from table_bronze, without removing any duplicates. Reference: Delete Duplicate using SPARK SQL, Spark SQL - How to Remove Duplicate Rows

NEW QUESTION # 36
A data analyst has been asked to configure an alert for a query that returns the income in the accounts_receivable table for a date range. The date range is configurable using a Date query parameter.
The Alert does not work.
Which of the following describes why the Alert does not work?

A. The wrong query parameter is being used. Alerts only work with drogdown list query parameters, not dates.
B. Queries that return results based on dates cannot be used with Alerts.
C. The wrong query parameter is being used. Alerts only work with Date and Time query parameters.
D. Alerts don't work with queries that access tables.
E. Queries that use query parameters cannot be used with Alerts.

Answer: E

Explanation:
According to the Databricks documentation1, queries that use query parameters cannot be used with Alerts. This is because Alerts do not support user input or dynamic values. Alerts leverage queries with parameters using the default value specified in the SQL editor for each parameter. Therefore, if the query uses a Date query parameter, the alert will always use the same date range as the default value, regardless of the actual date. This may cause the alert to not work as expected, or to not trigger at all. Reference:
Databricks SQL alerts: This is the official documentation for Databricks SQL alerts, where you can find information about how to create, configure, and monitor alerts, as well as the limitations and best practices for using alerts.

NEW QUESTION # 37
Which of the following statements describes descriptive statistics?

A. A branch of statistics that uses summary statistics to quantitatively describe and summarize data.
B. A branch of statistics that uses quantitative variables that must take on an uncountable set of values.
C. A branch of statistics that uses a variety of data analysis techniques to infer properties of an underlying distribution of probability.
D. A branch of statistics that uses summary statistics to categorically describe and summarize data.
E. A branch of statistics that uses quantitative variables that must take on a finite or countably infinite set of values.

Answer: A

Explanation:
Descriptive statistics is a branch of statistics that uses summary statistics, such as mean, median, mode, standard deviation, range, frequency, or correlation, to quantitatively describe and summarize data. Descriptive statistics can help data analysts understand the main features of a data set, such as its central tendency, variability, or distribution. Descriptive statistics can also help data analysts visualize data using charts, graphs, or tables. Descriptive statistics do not make any inferences or predictions about the data, unlike inferential statistics, which use data analysis techniques to infer properties of an underlying population or probability distribution from a sample of data. Reference: Databricks - Descriptive Statistics, Databricks - Data Analysis with Databricks SQL

NEW QUESTION # 38
Which of the following is an advantage of using a Delta Lake-based data lakehouse over common data lake solutions?

A. Open-source formats
B. ACID transactions
C. Flexible schemas
D. Data deletion
E. Scalable storage

Answer: B

Explanation:
A Delta Lake-based data lakehouse is a data platform architecture that combines the scalability and flexibility of a data lake with the reliability and performance of a data warehouse. One of the key advantages of using a Delta Lake-based data lakehouse over common data lake solutions is that it supports ACID transactions, which ensure data integrity and consistency. ACID transactions enable concurrent reads and writes, schema enforcement and evolution, data versioning and rollback, and data quality checks. These features are not available in traditional data lakes, which rely on file-based storage systems that do not support transactions. Reference:
Delta Lake: Lakehouse, warehouse, advantages | Definition
Synapse - Data Lake vs. Delta Lake vs. Data Lakehouse
Data Lake vs. Delta Lake - A Detailed Comparison
Building a Data Lakehouse with Delta Lake Architecture: A Comprehensive Guide

NEW QUESTION # 39
A data analyst is attempting to drop a table my_table. The analyst wants to delete all table metadata and data.
They run the following command:
DROP TABLE IF EXISTS my_table;
While the object no longer appears when they run SHOW TABLES, the data files still exist.
Which of the following describes why the data files still exist and the metadata files were deleted?

A. The table did not have a location
B. The table's data was larger than 10 GB
C. The table was managed
D. The table's data was smaller than 10 GB
E. The table was external

Answer: E

Explanation:
An external table is a table that is defined in the metastore, but its data is stored outside of the Databricks environment, such as in S3, ADLS, or GCS. When an external table is dropped, only the metadata is deleted from the metastore, but the data files are not affected. This is different from a managed table, which is a table whose data is stored in the Databricks environment, and whose data files are deleted when the table is dropped. To delete the data files of an external table, the analyst needs to specify the PURGE option in the DROP TABLE command, or manually delete the files from the storage system. Reference: DROP TABLE, Drop Delta table features, Best practices for dropping a managed Delta Lake table

NEW QUESTION # 40
......

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