Databricks-Certified-Data-Analyst-Associate Paper - Databricks Databricks Certified Data Analyst Associate Exam - The Best Exam Databricks-Certified-Data-Analyst-Associate Cram

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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 contrasts MERGE INTO, INSERT TABLE, and COPY INTO. Lastly, this topic focuses on creating and applying UDFs in common scaling scenarios.
Topic 2	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 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	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	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 (Q95-Q100):

NEW QUESTION # 95
A data analyst is working with a nested array column products in table transactions. The analyst wants to return the first item in the array for each row.
The data analyst is using the following incomplete command:
SELECT
transaction_id,
_____ AS first_product
FROM transactions;
Which line of code should the data analyst use to fill in the blank so that it successfully completes the task?

A. products[1]
B. products.0
C. products.1
D. products[0]

Answer: D

Explanation:
The correct answer is C because Databricks SQL supports bracket notation for arrays, and array indexing starts at 0. Therefore, the first element of the products array is accessed with products[0]. Options A and B use dot notation, which is not the correct syntax for array indexing. Option D returns the second element, not the first.
Official documentation extract used: Databricks states that the bracket operator returns an array element by index and that "the first element of an ARRAY is at index 0."

NEW QUESTION # 96
A data analyst has been asked to produce a visualization that shows the flow of users through a website.
Which of the following is used for visualizing this type of flow?

A. Sankey
B. Word Cloud
C. IChoropleth
D. Pivot Table
E. Heatmap

Answer: A

Explanation:
A Sankey diagram is a type of visualization that shows the flow of data between different nodes or categories.
It is often used to represent the movement of users through a website, as it can show the paths they take, the sources they come from, the pages they visit, and the outcomes they achieve. A Sankey diagram consists of links and nodes, where the links represent the volume or weight of the flow, and the nodes represent the stages or steps of the flow. The width of the links is proportional to the amount of flow, and the color of the links can indicate different attributes or segments of the flow. A Sankey diagram can help identify the most common or popular user journeys, the bottlenecks or drop-offs in the flow, and the opportunities for improvement or optimization. References: The answer can be verified from Databricks documentation which provides examples and instructions on how to create Sankey diagrams using Databricks SQL Analytics and Databricks Visualizations. Reference links: Databricks SQL Analytics - Sankey Diagram, Databricks Visualizations - Sankey Diagram

NEW QUESTION # 97
A data analyst has a managed table table_name in database database_name. They would now like to remove the table from the database and all of the data files associated with the table. The rest of the tables in the database must continue to exist.
Which of the following commands can the analyst use to complete the task without producing an error?

A. DELETE TABLE database_name.table_name;
B. DROP TABLE table_name FROM database_name;
C. DROP TABLE database_name.table_name;
D. DELETE TABLE table_name FROM database_name;
E. DROP DATABASE database_name;

Answer: C

Explanation:
The DROP TABLE command removes a table from the metastore and deletes the associated data files. The syntax for this command is DROP TABLE [IF EXISTS] [database_name.]table_name;. The optional IF EXISTS clause prevents an error if the table does not exist. The optional database_name. prefix specifies the database where the table resides. If not specified, the current database is used. Therefore, the correct command to remove the table table_name from the database database_name and all of the data files associated with it is DROP TABLE database_name.table_name;. The other commands are either invalid syntax or would produce undesired results. Reference: Databricks - DROP TABLE

NEW QUESTION # 98
Which of the following statements describes descriptive statistics?

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

Answer: E

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 # 99
Which of the following statements about a refresh schedule is incorrect?

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

Answer: D

Explanation:
Refresh schedules are used to rerun queries at specified intervals, and these queries typically require computational resources to execute. In the context of a cloud data service like Databricks, this would typically involve the use of a SQL Warehouse (or a SQL Endpoint, as they were formerly known) to provide the necessary computational resources. Therefore, the statement is incorrect because scheduled query refreshes would indeed use a SQL Warehouse/Endpoint to execute the query.

NEW QUESTION # 100
......

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