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

Topic	Details
Topic 1	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 2	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 3	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 4	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 5	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.

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q14-Q19):

NEW QUESTION # 14
Which example of data projects represents a common analytics application to be completed in Databricks SQL?

A. Monitoring the performance of deployed machine learning solutions
B. Testing the latency of real-time streaming workloads
C. Performing analysis-specific ETL on gold-layer tables
D. Predicting customer churn by automatically developing time-series models

Answer: C

Explanation:
Option B is correct. A data analyst commonly uses Databricks SQL for analytics-focused transformations, aggregation, joining, filtering, and preparation of datasets for reporting or dashboarding. Analysis-specific ETL on Gold-layer tables is a "last-mile" analytics task: the Gold layer is already curated for business use, and the analyst performs additional project-specific refinement. The official Databricks exam guide includes creating views, performing aggregate operations, combining tables with joins, filtering, sorting, and using dashboards as Data Analyst Associate skills. It also states that Gold-layer data drives downstream analytics and dashboards. Options A, C, and D are primarily streaming engineering, machine learning, and MLOps activities, not common Databricks SQL analytics applications. References: Databricks Certified Data Analyst Associate Exam Guide and medallion architecture documentation.

NEW QUESTION # 15
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 price FROM customer_summary
C. SELECT function(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(customer_spend, customer_units) AS customer_price FROM customer_summary

Answer: E

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 # 16
Which of the following statements about a refresh schedule is incorrect?

A. A query being refreshed on a schedule does not use a SQL Warehouse (formerly known as SQL Endpoint).
B. Refresh schedules can be configured in the Query Editor.
C. You must have workspace administrator privileges to configure a refresh schedule
D. A query can be refreshed anywhere from 1 minute lo 2 weeks
E. A refresh schedule is not the same as an alert.

Answer: C

Explanation:
This statement is incorrect. In Databricks SQL, any user with sufficient permissions on the query or dashboard can configure a refresh schedule-workspace administrator privileges are not required.
Here is the breakdown of the correct information:
A . True - Queries can be scheduled to refresh at intervals ranging from 1 minute to 2 weeks.
B . True - You can configure refresh schedules in the Query Editor.
C . False statement - A query being refreshed does use a SQL Warehouse. However, the option in question says it does not use a warehouse, which would be incorrect in a different context. Since this is a trickier one, we know that scheduled queries do require a SQL Warehouse to run.
D . True - Refresh schedules are different from alerts; alerts are triggered based on specific conditions being met in query results.
E . False (and thus the correct answer to this question) - You do not need to be a workspace admin to set a refresh schedule. You only need the correct permissions on the object.

NEW QUESTION # 17
Which of the following is stored in the Databricks customer's cloud account?

A. Databricks web application
B. Cluster management metadata
C. Data
D. Repos
E. Notebooks

Answer: C

Explanation:
Option D is correct. Customer data is stored in the customer's cloud account, such as object storage controlled by the customer. Databricks control-plane services manage platform components, while the customer's data resides in the customer-controlled storage layer. Official Databricks extract: "Databricks doesn't store your primary customer data. That lives in Amazon S3 or other systems you control."

NEW QUESTION # 18
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.date_format( " timestamp " , " yyyy-MM-dd " )).show()
C. dates_df.withColumn( " date " , f.from_unixtime( " timestamp " )).show()
D. dates_df.withColumn( " date " , f.to_date( " timestamp " )).show()

Answer: D

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 # 19
......

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