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100％合格率のDatabricks-Certified-Data-Analyst-Associate資格復習テキスト試験-試験の準備方法-一番優秀なDatabricks-Certified-Data-Analyst-Associate認証資格
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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 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 3
- 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 4
- 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.
トピック 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 認定 Databricks-Certified-Data-Analyst-Associate 試験問題 (Q50-Q55):
質問 # 50
A data analyst created and is the owner of the managed table my_ table. They now want to change ownership of the table to a single other user using Data Explorer.
Which of the following approaches can the analyst use to complete the task?
- A. Edit the Owner field in the table page by removing their own account
- B. Edit the Owner field in the table page by selecting All Users
- C. Edit the Owner field in the table page by selecting the new owner's account
- D. Edit the Owner field in the table page by removing all access
- E. Edit the Owner field in the table page by selecting the Admins group
正解：C
解説：
The Owner field in the table page shows the current owner of the table and allows the owner to change it to another user or group. To change the ownership of the table, the owner can click on the Owner field and select the new owner from the drop-down list. This will transfer the ownership of the table to the selected user or group and remove the previous owner from the list of table access control entries1. The other options are incorrect because:
A) Removing the owner's account from the Owner field will not change the ownership of the table, but will make the table ownerless2.
B) Selecting All Users from the Owner field will not change the ownership of the table, but will grant all users access to the table3.
D) Selecting the Admins group from the Owner field will not change the ownership of the table, but will grant the Admins group access to the table3.
E) Removing all access from the Owner field will not change the ownership of the table, but will revoke all access to the table4. Reference:
1: Change table ownership
2: Ownerless tables
3: Table access control
4: Revoke access to a table
質問 # 51
A data analyst has created a Query in Databricks SQL, and now they want to create two data visualizations from that Query and add both of those data visualizations to the same Databricks SQL Dashboard.
Which of the following steps will they need to take when creating and adding both data visualizations to the Databricks SQL Dashboard?
- A. They will need to add two separate visualizations to the dashboard based on the same Query.
- B. They will need to create two separate dashboards.
- C. They will need to decide on a single data visualization to add to the dashboard.
- D. They will need to copy the Query and create one data visualization per query.
- E. They will need to alter the Query to return two separate sets of results.
正解：A
解説：
A data analyst can create multiple visualizations from the same query in Databricks SQL by clicking the + button next to the Results tab and selecting Visualization. Each visualization can have a different type, name, and configuration. To add a visualization to a dashboard, the data analyst can click the vertical ellipsis button beneath the visualization, select + Add to Dashboard, and choose an existing or new dashboard. The data analyst can repeat this process for each visualization they want to add to the same dashboard. Reference: Visualization in Databricks SQL, Visualize queries and create a dashboard in Databricks SQL
質問 # 52
A data analyst has recently joined a new team that uses Databricks SQL, but the analyst has never used Databricks before. The analyst wants to know where in Databricks SQL they can write and execute SQL queries.
On which of the following pages can the analyst write and execute SQL queries?
- A. SQL Editor page
- B. Alerts page
- C. Queries page
- D. Dashboards page
- E. Data page
正解：A
解説：
The SQL Editor page is where the analyst can write and execute SQL queries in Databricks SQL. The SQL Editor page has a query pane where the analyst can type or paste SQL statements, and a results pane where the analyst can view the query results in a table or a chart. The analyst can also browse data objects, edit multiple queries, execute a single query or multiple queries, terminate a query, save a query, download a query result, and more from the SQL Editor page. Reference: Create a query in SQL editor
質問 # 53
A data analyst has been asked to configure an alert for a query that returns the income in the accounts_receivable table for a date range. The date range is configurable using a Date query parameter.
The Alert does not work.
Which of the following describes why the Alert does not work?
- A. The wrong query parameter is being used. Alerts only work with drogdown list query parameters, not dates.
- B. Queries that use query parameters cannot be used with Alerts.
- C. The wrong query parameter is being used. Alerts only work with Date and Time query parameters.
- D. Queries that return results based on dates cannot be used with Alerts.
- E. Alerts don't work with queries that access tables.
正解：B
解説：
According to the Databricks documentation1, queries that use query parameters cannot be used with Alerts. This is because Alerts do not support user input or dynamic values. Alerts leverage queries with parameters using the default value specified in the SQL editor for each parameter. Therefore, if the query uses a Date query parameter, the alert will always use the same date range as the default value, regardless of the actual date. This may cause the alert to not work as expected, or to not trigger at all. Reference:
Databricks SQL alerts: This is the official documentation for Databricks SQL alerts, where you can find information about how to create, configure, and monitor alerts, as well as the limitations and best practices for using alerts.
質問 # 54
Which of the following describes how Databricks SQL should be used in relation to other business intelligence (BI) tools like Tableau, Power BI, and looker?
- A. As a substitute with less functionality
- B. As a complementary tool for quick in-platform Bl work
- C. As an exact substitute with the same level of functionality
- D. As a complementary tool for professional-grade presentations
- E. As a complete replacement with additional functionality
正解：B
解説：
Databricks SQL is not meant to replace or substitute other BI tools, but rather to complement them by providing a fast and easy way to query, explore, and visualize data on the lakehouse using the built-in SQL editor, visualizations, and dashboards. Databricks SQL also integrates seamlessly with popular BI tools like Tableau, Power BI, and Looker, allowing analysts to use their preferred tools to access data through Databricks clusters and SQL warehouses. Databricks SQL offers low-code and no-code experiences, as well as optimized connectors and serverless compute, to enhance the productivity and performance of BI workloads on the lakehouse. Reference: Databricks SQL, Connecting Applications and BI Tools to Databricks SQL, Databricks integrations overview, Databricks SQL: Delivering a Production SQL Development Experience on the Lakehouse
質問 # 55
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