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

Topic	Details
Topic 1	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 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	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 4	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 5	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.

Databricks Certified Data Analyst Associate Exam Sample Questions (Q66-Q71):

NEW QUESTION # 66
Query History provides Databricks SQL users with a lot of benefits. A data analyst has been asked to share all of these benefits with their team as part of a training exercise. One of the benefit statements the analyst provided to their team is incorrect.
Which statement about Query History is incorrect?

A. It can be used to debug queries.
B. It can be used to automate query execution on multiple warehouses (formerly endpoints).
C. It can be used to troubleshoot slow running queries.
D. It can be used to view the query plan of queries that have run.

Answer: B

Explanation:
Query History in Databricks SQL is intended for reviewing executed queries, understanding their execution plans, and identifying performance issues or errors for debugging purposes. It allows users to analyze query duration, resources used, and potential bottlenecks. However, Query History does not provide any capability to automate the execution of queries across multiple warehouses; automation must be handled through jobs or external orchestration tools, not through the Query History feature itself.

NEW QUESTION # 67
The stakeholders.customers table has 15 columns and 3,000 rows of dat
a. The following command is run:

After running SELECT * FROM stakeholders.eur_customers, 15 rows are returned. After the command executes completely, the user logs out of Databricks.
After logging back in two days later, what is the status of the stakeholders.eur_customers view?

A. The view has been dropped.
B. The view remains available but attempting to SELECT from it results in an empty result set because data in views are automatically deleted after logging out.
C. The view remains available and SELECT * FROM stakeholders.eur_customers will execute correctly.
D. The view is not available in the metastore, but the underlying data can be accessed with SELECT * FROM delta. `stakeholders.eur_customers`.
E. The view has been converted into a table.

Answer: C

Explanation:
In Databricks, a view is a saved SQL query definition that references existing tables or other views. Once created, a view remains persisted in the metastore (such as Unity Catalog or Hive Metastore) until it is explicitly dropped.
Key points:
Views do not store data themselves but reference data from underlying tables.
Logging out or being inactive does not delete or alter views.
Unless a user or admin explicitly drops the view or the underlying data/table is deleted, the view continues to function as expected.
Therefore, after logging back in-even days later-a user can still run SELECT * FROM stakeholders.eur_customers, and it will return the same data (provided the underlying table hasn't changed).

NEW QUESTION # 68
A data analyst has set up a SQL query to run every four hours on a SQL endpoint, but the SQL endpoint is taking too long to start up with each run.
Which of the following changes can the data analyst make to reduce the start-up time for the endpoint while managing costs?

A. Reduce the SQL endpoint cluster size
B. Increase the SQL endpoint cluster size
C. Turn off the Auto stop feature
D. Increase the minimum scaling value
E. Use a Serverless SQL endpoint

Answer: E

Explanation:
A Serverless SQL endpoint is a type of SQL endpoint that does not require a dedicated cluster to run queries. Instead, it uses a shared pool of resources that can scale up and down automatically based on the demand. This means that a Serverless SQL endpoint can start up much faster than a SQL endpoint that uses a cluster, and it can also save costs by only paying for the resources that are used. A Serverless SQL endpoint is suitable for ad-hoc queries and exploratory analysis, but it may not offer the same level of performance and isolation as a SQL endpoint that uses a cluster. Therefore, a data analyst should consider the trade-offs between speed, cost, and quality when choosing between a Serverless SQL endpoint and a SQL endpoint that uses a cluster. Reference: Databricks SQL endpoints, Serverless SQL endpoints, SQL endpoint clusters

NEW QUESTION # 69
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 query being refreshed on a schedule does not use a SQL Warehouse (formerly known as SQL Endpoint).
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:
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 # 70
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 suppliers table now contains only the data from the new suppliers table.
B. 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.
C. The command fails because it is written incorrectly.
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 both the data it had before the command was run and the data from the new suppliers table, and any duplicate data is deleted.

Answer: C

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 # 71
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Databricks-Certified-Data-Analyst-Associate Exam Questions - Instant Access

Pass the Databricks Databricks-Certified-Data-Analyst-Associate certification exam with flying colors

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

Databricks Certified Data Analyst Associate Exam Sample Questions (Q66-Q71):

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