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[General] 100% Pass Databricks - Valid Databricks-Certified-Data-Analyst-Associate Reliabl

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【General】 100% Pass Databricks - Valid Databricks-Certified-Data-Analyst-Associate Reliabl

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Databricks Databricks-Certified-Data-Analyst-Associate Exam Syllabus Topics:
TopicDetails
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.
Topic 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.
Topic 3
  • 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 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.
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.

Databricks Certified Data Analyst Associate Exam Sample Questions (Q27-Q32):NEW QUESTION # 27
In which of the following situations will the mean value and median value of variable be meaningfully different?
  • A. When the variable contains no outliers
  • B. When the variable contains no missing values
  • C. When the variable is of the categorical type
  • D. When the variable contains a lot of extreme outliers
  • E. When the variable is of the boolean type
Answer: D
Explanation:
The mean value of a variable is the average of all the values in a data set, calculated by dividing the sum of the values by the number of values. The median value of a variable is the middle value of the ordered data set, or the average of the middle two values if the data set has an even number of values. The mean value is sensitive to outliers, which are values that are very different from the rest of the data. Outliers can skew the mean value and make it less representative of the central tendency of the data. The median value is more robust to outliers, as it only depends on the middle values of the data. Therefore, when the variable contains a lot of extreme outliers, the mean value and the median value will be meaningfully different, as the mean value will be pulled towards the outliers, while the median value will remain close to the majority of the data1. Reference: Difference Between Mean and Median in Statistics (With Example) - BYJU'S

NEW QUESTION # 28
How can a data analyst determine if query results were pulled from the cache?
  • A. Go to the Alerts tab and check the Cache Status alert.
  • B. Go to the Data tab and click Last Query. The details of the query will show if the results came from the cache.
  • C. Go to the Queries tab and click on Cache Status. The status will be green if the results from the last run came from the cache.
  • D. Go to the SQL Warehouse (formerly SQL Endpoints) tab and click on Cache. The Cache file will show the contents of the cache.
  • E. Go to the Query History tab and click on the text of the query. The slideout shows if the results came from the cache.
Answer: E
Explanation:
Databricks SQL uses a query cache to store the results of queries that have been executed previously. This improves the performance and efficiency of repeated queries. To determine if a query result was pulled from the cache, you can go to the Query History tab in the Databricks SQL UI and click on the text of the query. A slideout will appear on the right side of the screen, showing the query details, including the cache status. If the result came from the cache, the cache status will show "Cached". If the result did not come from the cache, the cache status will show "Not cached". You can also see the cache hit ratio, which is the percentage of queries that were served from the cache. Reference: The answer can be verified from Databricks SQL documentation which provides information on how to use the query cache and how to check the cache status. Reference link: Databricks SQL - Query Cache

NEW QUESTION # 29
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. DROP DATABASE database_name;
  • C. DROP TABLE table_name FROM database_name;
  • D. DELETE TABLE database_name.table_name;
  • E. DELETE TABLE table_name FROM database_name;
Answer: A

NEW QUESTION # 30
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. Alerts don't work with queries that access tables.
  • B. The wrong query parameter is being used. Alerts only work with drogdown list query parameters, not dates.
  • C. The wrong query parameter is being used. Alerts only work with Date and Time query parameters.
  • D. Queries that use query parameters cannot be used with Alerts.
  • E. Queries that return results based on dates cannot be used with Alerts.
Answer: D
Explanation:
The reason the alert is not functioning as expected is because Databricks SQL Alerts do not support query parameters. This limitation applies to all types of parameters, including date parameters.
Here's why:
Alerts require static, deterministic query results so they can compare values consistently during scheduled executions.
When a query includes parameters (e.g., a date range parameter), its results may change based on user input or the default value set in the query editor.
However, Databricks SQL Alerts will always use the default value set for the parameter at the time the alert is created. This means the alert doesn't dynamically adapt to new date ranges and will not reflect changes unless the query is manually updated.
As a result, if the business logic behind the alert depends on changing date ranges or any user input, the alert will not trigger correctly, or may never trigger at all.
Therefore, the correct explanation contradicts Option B, which is incorrect in saying that alerts cannot work with date-based queries at all. In fact, they can-as long as the query is static (i.e., without parameters).
Reference:
Databricks SQL Alerts Documentation
Databricks Knowledge: "You cannot use alerts with queries that contain parameters."

NEW QUESTION # 31
After running DESCRIBE EXTENDED accounts.customers;, the following was returned:

Now, a data analyst runs the following command:
DROP accounts.customers;
Which of the following describes the result of running this command?
  • A. The accounts.customers table is removed from the metastore, and the underlying data files are deleted.
  • B. All files with the .customers extension are deleted.
  • C. The accounts.customers table is removed from the metastore, but the underlying data files are untouched.
  • D. Running SELECT * FROM accounts.customers will return all rows in the table.
  • E. Running SELECT * FROM delta. `dbfs:/stakeholders/customers` results in an error.
Answer: C
Explanation:
the accounts.customers table is an EXTERNAL table, which means that it is stored outside the default warehouse directory and is not managed by Databricks. Therefore, when you run the DROP command on this table, it only removes the metadata information from the metastore, but does not delete the actual data files from the file system. This means that you can still access the data using the location path (dbfs:/stakeholders/customers) or create another table pointing to the same location. However, if you try to query the table using its name (accounts.customers), you will get an error because the table no longer exists in the metastore. Reference: DROP TABLE | Databricks on AWS, Best practices for dropping a managed Delta Lake table - Databricks

NEW QUESTION # 32
......
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