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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Securing Data | 8% | - Data governance policies - Access control and permissions - Secure storage and compliance |
| Topic 2: Managing Data | 8% | - Discovering and registering datasets - Unity Catalog usage - Dataset versioning and management - Data cleaning and preparation |
| Topic 3: Analyzing Queries | 15% | - Query history and auditing - Execution plans and analysis - Performance optimization - Liquid clustering and indexing |
| Topic 4: Developing, Sharing, and Maintaining AI/BI Genie Spaces | 12% | - Genie space setup and configuration - Access control and sharing - Maintenance and improvement - Natural language query setup |
| Topic 5: Understanding of Databricks Data Intelligence Platform | 11% | - Lakehouse platform fundamentals - Core architecture and components - Workspace navigation and interface |
| Topic 6: Creating Dashboards and Visualizations in Databricks | 16% | - Scheduling and sharing dashboards - Filtering and interactivity - Visualization types and best practices - Dashboard creation and layout |
| Topic 7: Importing Data | 5% | - UI-based data ingestion - Databricks Marketplace - API and Auto Loader - Delta Sharing - S3 and cloud storage integration |
| Topic 8: Data Modeling with Databricks SQL | 5% | - Delta table structure - Performance-oriented modeling - Schema design principles |
| Topic 9: Executing Queries using Databricks SQL and Databricks SQL Warehouses | 20% | - Aggregations and grouping - ANSI SQL syntax and functions - Creating and managing views - Joining and combining datasets - Warehouse configuration and performance |
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NEW QUESTION # 74
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?
Answer: C
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 # 75
Which statement about subqueries is correct?
Answer: B
Explanation:
In Databricks SQL, a subquery is a nested query within a larger SQL query that allows for the retrieval of data without the necessity of creating a table or view. This is particularly useful for simplifying complex queries by breaking them down into more manageable parts. Subqueries can be employed in various clauses such as SELECT, FROM, and WHERE to perform operations like filtering, transforming, and aggregating data on-the-fly. This flexibility enhances query efficiency and readability without the overhead of persisting intermediate results as separate tables or views.
NEW QUESTION # 76
A data analyst runs the following command:
SELECT age, country
FROM my_table
WHERE age > = 75 AND country = ' canada ' ;
Which of the following tables represents the output of the above command?
Answer: E
Explanation:
The SQL query provided is designed to filter out records from "my_table" where the age is 75 or above and the country is Canada. Since I can't view the content of the links provided directly, I need to rely on the image attached to this question for context. Based on that, Option E (the image attached) represents a table with columns "age" and "country", showing records where age is 75 or above and country is Canada. References:
The answer can be inferred from understanding SQL queries and their outputs as per Databricks documentation: Databricks SQL
NEW QUESTION # 77
A data engineer wants to create a relational object by pulling data from two tables. The relational object does not need to be used by other data engineers in other sessions. In order to save on storage costs, the data engineer wants to avoid copying and storing physical data.
Which of the following relational objects should the data engineer create?
Answer: C
Explanation:
Option D is correct. A temporary view is session-scoped and does not store physical data. It is suitable when the relational object is only needed in the current session or query context and should not be available to others in other sessions. A regular view also avoids copying physical data, but it is persistent and can be shared beyond the current session. Official Databricks extract: a view is a "virtual table that has no physical data," and temporary views are scoped to the notebook/script or query level and cannot be referenced outside that scope.
NEW QUESTION # 78
How can a data analyst determine if query results were pulled from the cache?
Answer: D
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 # 79
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