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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 | - 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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| 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.
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| 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.
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| トピック 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.
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| トピック 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 認定 Databricks-Certified-Data-Analyst-Associate 試験問題 (Q79-Q84):
質問 # 79
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 table_name FROM database_name;
- B. DROP TABLE table_name FROM database_name;
- C. DROP TABLE database_name.table_name;
- D. DELETE TABLE database_name.table_name;
- E. DROP DATABASE database_name;
正解:C
解説:
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. References: Databricks - DROP TABLE
質問 # 80
A data engineer has been using a Databricks SQL dashboard to monitor the cleanliness of the input data to a data analytics dashboard for a retail use case. The job has a Databricks SQL query that returns the number of store-level records where sales is equal to zero. The data engineer wants their entire team to be notified via a messaging webhook whenever this value is greater than 0.
Which of the following approaches can the data engineer use to notify their entire team via a messaging webhook whenever the number of stores with $0 in sales is greater than zero?
- A. They can set up an Alert with a new email alert destination.
- B. They can set up an Alert with a custom template.
- C. They can set up an Alert with a new webhook alert destination.
- D. They can set up an Alert with one-time notifications.
- E. They can set up an Alert without notifications.
正解:C
解説:
Option D is correct. Databricks SQL alerts run a query on a schedule, evaluate a condition, and notify destinations when the condition is met. Since the requirement is notification through a messaging webhook, the alert should use a webhook alert destination. Official Databricks extract: alerts "run queries on a schedule and notify you when a condition that you define is met." Databricks notification documentation also states that admins can configure alternate notification destinations using webhooks.
質問 # 81
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. Alerts page
- B. Queries page
- C. Data page
- D. SQL Editor page
- E. Dashboards page
正解:D
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. References: Create a query in SQL editor
質問 # 82
Which of the following approaches can be used to connect Databricks to Fivetran for data ingestion?
- A. Use Workflows to establish a SQL warehouse (formerly known as a SQL endpoint) for Fivetran to interact with
- B. Use Partner Connect's automated workflow to establish a SQL warehouse (formerly known as a SQL endpoint) for Fivetran to interact with
- C. Use Partner Connect's automated workflow to establish a cluster for Fivetran to interact with
- D. Use Workflows to establish a cluster for Fivetran to interact with
- E. Use Delta Live Tables to establish a cluster for Fivetran to interact with
正解:C
解説:
Partner Connect is a feature that allows you to easily connect your Databricks workspace to Fivetran and other ingestion partners using an automated workflow. You can select a SQL warehouse or a cluster as the destination for your data replication, and the connection details are sent to Fivetran. You can then choose from over 200 data sources that Fivetran supports and start ingesting data into Delta Lake. Reference: Connect to Fivetran using Partner Connect, Use Databricks with Fivetran
質問 # 83
Which statement describes descriptive statistics?
- A. A branch of statistics that uses a variety of data analysis techniques to infer properties of an underlying distribution of probability.
- B. A branch of statistics that uses quantitative variables that must take on a finite or countably infinite set of values.
- C. A branch of statistics that uses summary statistics to categorically describe and summarize data.
- D. A branch of statistics that uses summary statistics to quantitatively describe and summarize data.
正解:D
質問 # 84
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