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| Section | Weight | Objectives |
|---|---|---|
| Data Modeling with Databricks SQL | 5% | - Performance-oriented modeling - Delta table structure - Schema design principles |
| Analyzing Queries | 15% | - Performance optimization - Execution plans and analysis - Query history and auditing - Liquid clustering and indexing |
| Understanding of Databricks Data Intelligence Platform | 11% | - Workspace navigation and interface - Core architecture and components - Lakehouse platform fundamentals |
| Executing Queries using Databricks SQL and Databricks SQL Warehouses | 20% | - ANSI SQL syntax and functions - Warehouse configuration and performance - Joining and combining datasets - Creating and managing views - Aggregations and grouping |
| Developing, Sharing, and Maintaining AI/BI Genie Spaces | 12% | - Maintenance and improvement - Genie space setup and configuration - Natural language query setup - Access control and sharing |
| Creating Dashboards and Visualizations in Databricks | 16% | - Scheduling and sharing dashboards - Filtering and interactivity - Dashboard creation and layout - Visualization types and best practices |
| Importing Data | 5% | - S3 and cloud storage integration - Delta Sharing - Databricks Marketplace - UI-based data ingestion - API and Auto Loader |
| Securing Data | 8% | - Access control and permissions - Data governance policies - Secure storage and compliance |
| Managing Data | 8% | - Discovering and registering datasets - Data cleaning and preparation - Unity Catalog usage - Dataset versioning and management |
>> Exam Databricks-Certified-Data-Analyst-Associate Outline <<
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NEW QUESTION # 117
A data analyst is working with a nested array column products in table transactions. The analyst wants to return the first item in the array for each row.
The data analyst is using the following incomplete command:
SELECT
transaction_id,
_____ AS first_product
FROM transactions;
Which line of code should the data analyst use to fill in the blank so that it successfully completes the task?
Answer: B
Explanation:
The correct answer is C because Databricks SQL supports bracket notation for arrays, and array indexing starts at 0. Therefore, the first element of the products array is accessed with products[0]. Options A and B use dot notation, which is not the correct syntax for array indexing. Option D returns the second element, not the first.
Official documentation extract used: Databricks states that the bracket operator returns an array element by index and that "the first element of an ARRAY is at index 0."
NEW QUESTION # 118
Delta Lake stores table data as a series of data files, but it also stores a lot of other information.
Which of the following is stored alongside data files when using Delta Lake?
Answer: A
Explanation:
Delta Lake is a storage layer that enhances data lakes with features like ACID transactions, schema enforcement, and time travel. While it stores table data as Parquet files, Delta Lake also keeps a transaction log (stored in the _delta_log directory) that contains detailed table metadata.
This metadata includes:
* Table schema
* Partitioning information
* Data file paths
* Transactional operations like inserts, updates, and deletes
* Commit history and version control
This metadata is critical for supporting Delta Lake's advanced capabilities such as time travel and efficient query execution. Delta Lake does not store data summary visualizations or owner account information directly alongside the data files.
Reference: Delta Lake Table Features - Databricks Documentation
NEW QUESTION # 119
Which statement about subqueries is correct?
Answer: A
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 # 120
What is a benefit of using Databricks SQL for business intelligence (Bl) analytics projects instead of using third-party Bl tools?
Answer: C
Explanation:
Databricks SQL offers a unified platform where computations, data storage, and analytical tools coexist seamlessly. This integration allows business intelligence (BI) analytics projects to be executed more efficiently, as users can perform data processing and analysis without the need to transfer data between disparate systems. By consolidating these components, Databricks SQL streamlines workflows, reduces latency, and enhances data governance. While third-party BI tools may offer advanced dashboarding capabilities, simultaneous multi-user support, and automated alerting systems, they often require integration with separate data processing platforms, which can introduce complexity and potential inefficiencies.
Reference: Databricks AI & BI: Transform Data into Actionable Insights
NEW QUESTION # 121
A data analyst wants the following output:
customer_name
number_of_orders
John Doe
388
Zhang San
234
Which statement will produce this output?
Answer: A
Explanation:
To get the number of orders per customer, you need to join the customers and orders tables on the customer_id, count the order_id, and group the results by customer_name. The correct SQL syntax, as outlined in Databricks SQL documentation, is to use GROUP BY on the selected customer field and use COUNT for aggregation. Only option A does this correctly, while the other options contain syntax errors or incorrect field names.
NEW QUESTION # 122
......
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