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| Topic |
Details |
| Topic 1 |
- 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.
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| 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.
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| Topic 3 |
- 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.
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| 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.
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| 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 (Q50-Q55):
NEW QUESTION # 50
A data analyst creates a Databricks SQL Query where the result set has the following schema:
region STRING
number_of_customer INT
When the analyst clicks on the "Add visualization" button on the SQL Editor page, which of the following types of visualizations will be selected by default?
- A. Histogram
- B. Violin Chart
- C. There is no default. The user must choose a visualization type.
- D. Line Chart
- E. IBar Chart
Answer: E
Explanation:
According to the Databricks SQL documentation, when a data analyst clicks on the "Add visualization" button on the SQL Editor page, the default visualization type is Bar Chart. This is because the result set has two columns: one of type STRING and one of type INT. The Bar Chart visualization automatically assigns the STRING column to the X-axis and the INT column to the Y-axis. The Bar Chart visualization is suitable for showing the distribution of a numeric variable across different categories. Reference: Visualization in Databricks SQL, Visualization types
NEW QUESTION # 51
A data scientist has asked a data analyst to create histograms for every continuous variable in a data set. The data analyst needs to identify which columns are continuous in the data set.
What describes a continuous variable?
- A. A quantitative variable Chat can take on a finite or countably infinite set of values
- B. A categorical variable in which the number of categories continues to increase over time
- C. A quantitative variable that never stops changing
- D. A quantitative variable that can take on an uncountable set of values
Answer: D
Explanation:
A continuous variable is a type of quantitative variable that can assume an infinite number of values within a given range. This means that between any two possible values, there can be an infinite number of other values. For example, variables such as height, weight, and temperature are continuous because they can be measured to any level of precision, and there are no gaps between possible values. This is in contrast to discrete variables, which can only take on specific, distinct values (e.g., the number of children in a family). Understanding the nature of continuous variables is crucial for data analysts, especially when selecting appropriate statistical methods and visualizations, such as histograms, to accurately represent and analyze the data.
NEW QUESTION # 52
A data analyst runs the following command:
INSERT INTO stakeholders.suppliers TABLE stakeholders.new_suppliers;
What is the result of running this command?
- A. The command fails because it is written incorrectly.
- B. The suppliers table now contains both the data it had before the command was run and the data from the new suppliers table, and any duplicate data is deleted.
- C. The suppliers table now contains the data from the new suppliers table, and the new suppliers table now contains the data from the suppliers table.
- D. The suppliers table now contains both the data it had before the command was run and the data from the new suppliers table, including any duplicate data.
- E. The suppliers table now contains only the data from the new suppliers table.
Answer: A
Explanation:
The command INSERT INTO stakeholders.suppliers TABLE stakeholders.new_suppliers is not a valid syntax for inserting data into a table in Databricks SQL. According to the documentation12, the correct syntax for inserting data into a table is either:
INSERT { OVERWRITE | INTO } [ TABLE ] table_name [ PARTITION clause ] [ ( column_name [, ...] ) | BY NAME ] query INSERT INTO [ TABLE ] table_name REPLACE WHERE predicate query The command in the question is missing the OVERWRITE or INTO keyword, and the query part that specifies the source of the data to be inserted. The TABLE keyword is optional and can be omitted. The PARTITION clause and the column list are also optional and depend on the table schema and the data source. Therefore, the command in the question will fail with a syntax error.
Reference:
INSERT | Databricks on AWS
INSERT - Azure Databricks - Databricks SQL | Microsoft Learn
NEW QUESTION # 53
Delta Lake stores table data as a series of data files, but it also stores a lot of other information.
Which of the following is stored alongside data files when using Delta Lake?
- A. Table metadata
- B. None of these
- C. Data summary visualizations
- D. Table metadata, data summary visualizations, and owner account information
- E. Owner account information
Answer: A
Explanation:
Delta Lake is a storage layer that enhances data lakes with features like ACID transactions, schema enforcement, and time travel. While it stores table data as Parquet files, Delta Lake also keeps a transaction log (stored in the _delta_log directory) that contains detailed table metadata.
This metadata includes:
Table schema
Partitioning information
Data file paths
Transactional operations like inserts, updates, and deletes
Commit history and version control
This metadata is critical for supporting Delta Lake's advanced capabilities such as time travel and efficient query execution. Delta Lake does not store data summary visualizations or owner account information directly alongside the data files.
NEW QUESTION # 54
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. DROP TABLE database_name.table_name;
- B. DELETE TABLE table_name FROM database_name;
- C. DROP DATABASE database_name;
- D. DROP TABLE table_name FROM database_name;
- E. DELETE TABLE database_name.table_name;
Answer: A
NEW QUESTION # 55
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