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Databricks Databricks-Certified-Data-Analyst-Associate 認定試験の出題範囲：

トピック	出題範囲
トピック 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.
トピック 2	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.
トピック 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.
トピック 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.
トピック 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 認定 Databricks-Certified-Data-Analyst-Associate 試験問題 (Q11-Q16):

質問 # 11
Data engineers and data analysts are working together on a data pipeline. The data engineer is working on the raw, bronze, and silver layers of the pipeline using Python, and the data analyst is working on the gold layer of the pipeline using SQL. The raw source of the pipeline is a streaming input. They now want to migrate their pipeline to use Delta Live Tables.
Which of the following changes will need to be made to the pipeline when migrating to Delta Live Tables?

A. The pipeline can have different notebook sources in SQL and Python.
B. The pipeline will need to be written entirely in Python.
C. The pipeline will need to use a batch source in place of a streaming source.
D. The pipeline will need to be written entirely in SQL.

正解：A

解説：
Option A is correct. Delta Live Tables, now documented under Lakeflow Declarative Pipelines, supports both Python and SQL source files in the same pipeline. The pipeline does not need to be rewritten entirely in one language, and it can still use streaming inputs. Official Databricks extract: "You can add Python and SQL source files to the same pipeline." The same documentation also shows examples for batch and streaming reads in Python and SQL.

質問 # 12
A data analyst has been asked to provide a list of options on how to share a dashboard with a client. It is a security requirement that the client does not gain access to any other information, resources, or artifacts in the database.
Which of the following approaches cannot be used to share the dashboard and meet the security requirement?

A. Take a screenshot of the dashboard and share it with the client.
B. Download a PNG file of the visualizations in the dashboard and share them with the client.
C. Download the Dashboard as a PDF and share it with the client.
D. Set a refresh schedule for the dashboard and enter the client ' s email address in the " Subscribers " box.
E. Generate a Personal Access Token that is good for 1 day and share it with the client.

正解：E

解説：
The approach that cannot be used to share the dashboard and meet the security requirement is D. Generating a Personal Access Token that is good for 1 day and sharing it with the client. This approach would give the client access to the Databricks workspace using the token owner's identity and permissions, which could expose other information, resources, or artifacts in the database1. The other approaches can be used to share the dashboard and meet the security requirement because:
* A. Downloading the Dashboard as a PDF and sharing it with the client would only provide a static snapshot of the dashboard without any interactive features or access to the underlying data2.
* B. Setting a refresh schedule for the dashboard and entering the client's email address in the
"Subscribers" box would send the client an email with the latest dashboard results as an attachment or a link to a secure web page3. The client would not be able to access the Databricks workspace or the dashboard itself.
* C. Taking a screenshot of the dashboard and sharing it with the client would also only provide a static snapshot of the dashboard without any interactive features or access to the underlying data4.
* E. Downloading a PNG file of the visualizations in the dashboard and sharing them with the client would also only provide a static snapshot of the visualizations without any interactive features or access to the underlying data5. References:
* 1: Personal access tokens
* 2: Download as PDF
* 3: Automatically refresh a dashboard
* 4: Take a screenshot
* 5: Download a PNG file

質問 # 13
A data engineer is running code in a Databricks Repo that is cloned from a central Git repository. A colleague informs them that changes have been made and synced to the central Git repository. The data engineer now needs to sync their Databricks Repo to get the changes from the central Git repository.
Which of the following Git operations does the data engineer need to run to accomplish this task?

A. Commit
B. Push
C. Clone
D. Merge
E. Pull

正解：E

解説：
Option C is correct. To retrieve changes from a remote central Git repository into a local Databricks Git folder
/Repo, the correct Git operation is Pull. Push sends local commits to the remote repository. Commit records local changes. Clone creates a new local copy. Merge combines branches but is not the operation described here. Official Databricks extract: "To pull changes from the remote Git repository, click Pull."

質問 # 14
A data analyst filters rows where the tags array includes the value ' sql ' using this query:
SELECT *
FROM main.analytics.articles
WHERE tags = ' sql ' ;
This query returns no results.
How should the analyst query to filter for rows where the tags array contains ' sql ' ?

A. SELECT * FROM main.analytics.articles WHERE tags LIKE ' %sql% ' ;
B. SELECT * FROM main.analytics.articles WHERE tags IN ( ' sql ' );
C. SELECT * FROM main.analytics.articles WHERE array_contains(tags, ' sql ' );
D. SELECT * FROM main.analytics.articles WHERE ' sql ' IN tags;

正解：C

解説：
Option D is correct. The column tags is an array, so comparing the entire array to the scalar string ' sql ' is incorrect. The correct Databricks SQL function is array_contains(array, value), which returns true when the array contains the specified value. The uploaded document has a spelling error, array_conatins; the corrected Databricks function name is array_contains. Official Databricks documentation states that array_contains
"returns true if array contains value."

質問 # 15
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. A refresh schedule is not the same as an alert.
C. A query can be refreshed anywhere from 1 minute lo 2 weeks
D. Refresh schedules can be configured in the Query Editor.
E. You must have workspace administrator privileges to configure a refresh schedule

正解：E

解説：
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.

質問 # 16
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