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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	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 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 (Q79-Q84):

NEW QUESTION # 79
A data analyst is processing a complex aggregation on a table with zero null values and the query returns the following result:

Which query did the analyst execute in order to get this result?

Answer: A

NEW QUESTION # 80
Which of the following data workloads will utilize a Gold table as its source?

A. A job that enriches data by parsing its timestamps into a human-readable format.
B. A job that cleans data by removing malformatted records.
C. A job that aggregates uncleaned data to create standard summary statistics.
D. A job that queries aggregated data designed to feed into a dashboard.
E. A job that ingests raw data from a streaming source into the Lakehouse.

Answer: D

Explanation:
Option D is correct. Gold tables are refined, business-ready datasets used for analytics, dashboards, reporting, and other downstream consumption. A workload that queries aggregated data designed to feed a dashboard is therefore using Gold-layer data. Raw streaming ingestion belongs to Bronze, cleaning malformed records belongs to Silver, and aggregating uncleaned data is not the recommended Gold pattern. Official Databricks extract: the Gold layer "consists of aggregated data tailored for analytics and reporting" and is optimized for
"queries and dashboards."

NEW QUESTION # 81
An analyst writes a query that contains a query parameter. They then add an area chart visualization to the query. While adding the area chart visualization to a dashboard, the analyst chooses "Dashboard Parameter" for the query parameter associated with the area chart.
Which of the following statements is true?

A. The area chart will use whatever value is input by the analyst when the visualization is added to the dashboard. The parameter cannot be changed by the user afterwards.
B. The area chart will convert to a Dashboard Parameter.
C. The area chart will use whatever is selected in the Dashboard Parameter while all or the other visualizations will remain changed regardless of their parameter use.
D. The area chart will use whatever is selected in the Dashboard Parameter along with all of the other visualizations in the dashboard that use the same parameter.
E. The area chart will use whatever value is chosen on the dashboard at the time the area chart is added to the dashboard.

Answer: D

Explanation:
A Dashboard Parameter is a parameter that is configured for one or more visualizations within a dashboard and appears at the top of the dashboard. The parameter values specified for a Dashboard Parameter apply to all visualizations reusing that particular Dashboard Parameter1. Therefore, if the analyst chooses "Dashboard Parameter" for the query parameter associated with the area chart, the area chart will use whatever is selected in the Dashboard Parameter along with all of the other visualizations in the dashboard that use the same parameter. This allows the user to filter the data across multiple visualizations using a single parameter widget2. Reference: Databricks SQL dashboards, Query parameters

NEW QUESTION # 82
Data professionals with varying titles use the Databricks SQL service as the primary touchpoint with the Databricks Lakehouse Platform. However, some users will use other services like Databricks Machine Learning or Databricks Data Science and Engineering.
Which of the following roles uses Databricks SQL as a secondary service while primarily using one of the other services?

A. Data engineer
B. Business intelligence analyst
C. Data analyst
D. Business analyst
E. SQL analyst

Answer: A

Explanation:
Data engineers are primarily responsible for building, managing, and optimizing data pipelines and architectures. They use Databricks Data Science and Engineering service to perform tasks such as data ingestion, transformation, quality, and governance. Data engineers may use Databricks SQL as a secondary service to query, analyze, and visualize data from the lakehouse, but this is not their main focus. Reference: Databricks SQL overview, Databricks Data Science and Engineering overview, Data engineering with Databricks

NEW QUESTION # 83
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?

A. TRANSFORM(dollars_spent, value / 100) AS hundreds_spent
B. TRANSFORM(dollars_spent, value - > value / 100) AS hundreds_spent
C. TRANSFORM(dollars_spent, dollars_spent / 100) AS hundreds_spent
D. TRANSFORM(hundreds_spent, dollars_spent / 100)

Answer: B

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 # 84
......

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