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| Section | Weight | Objectives |
|---|---|---|
| Developing, Sharing, and Maintaining AI/BI Genie Spaces | 12% | - Maintenance and improvement - Natural language query setup - Genie space setup and configuration - Access control and sharing |
| Analyzing Queries | 15% | - Query history and auditing - Execution plans and analysis - Performance optimization - Liquid clustering and indexing |
| Executing Queries using Databricks SQL and Databricks SQL Warehouses | 20% | - Joining and combining datasets - Warehouse configuration and performance - Creating and managing views - Aggregations and grouping - ANSI SQL syntax and functions |
| Understanding of Databricks Data Intelligence Platform | 11% | - Core architecture and components - Workspace navigation and interface - Lakehouse platform fundamentals |
| Managing Data | 8% | - Discovering and registering datasets - Data cleaning and preparation - Unity Catalog usage - Dataset versioning and management |
| Data Modeling with Databricks SQL | 5% | - Performance-oriented modeling - Schema design principles - Delta table structure |
| Creating Dashboards and Visualizations in Databricks | 16% | - Visualization types and best practices - Dashboard creation and layout - Filtering and interactivity - Scheduling and sharing dashboards |
| Importing Data | 5% | - API and Auto Loader - S3 and cloud storage integration - Delta Sharing - UI-based data ingestion - Databricks Marketplace |
| Securing Data | 8% | - Access control and permissions - Secure storage and compliance - Data governance policies |
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NEW QUESTION # 98
A data engineering team has created a Structured Streaming pipeline that processes data in micro-batches and populates gold-level tables. The microbatches are triggered every 10 minutes.
A data analyst has created a dashboard based on this gold level data. The project stakeholders want to see the results in the dashboard updated within 10 minutes or less of new data becoming available within the gold- level tables.
What is the ability to ensure the streamed data is included in the dashboard at the standard requested by the project stakeholders?
Answer: A
Explanation:
In this scenario, the data engineering team has configured a Structured Streaming pipeline that updates the gold-level tables every 10 minutes. To ensure that the dashboard reflects the most recent data, it is essential to set the dashboard ' s refresh schedule to an interval of 10 minutes or less. This synchronization ensures that stakeholders view the latest information shortly after it becomes available in the gold-level tables. Options B, C, and D do not directly address the requirement of aligning the dashboard refresh frequency with the data update interval.
NEW QUESTION # 99
A data analyst is working with the following table my_table:
customer_name dollars_spent
Hex Sprockets [125.34, 100.15, 9003.99]
Dented Fenders [16.99, 200.85, 33.49, 88.17]
The analyst wants to divide each value in the dollars_spent array by 100 to get the spend in terms of hundreds of dollars using the following code block:
SELECT
customer_name,
_______
FROM my_table;
Which line of code can be used to fill in the blank so that the above code block successfully completes the task?
Answer: D
Explanation:
The correct answer is C because TRANSFORM applies a lambda function to each element of an array and returns a new array. The expression value - > value / 100 means: for each element named value in dollars_spent, divide that element by 100. The result is then aliased as hundreds_spent. Options A, B, and D do not use the required lambda syntax correctly.
Official documentation extract used: Databricks states that transform returns an array and that the lambda function "produces a new value for each element."
NEW QUESTION # 100
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.
Reference: Databricks SQL Query Syntax
NEW QUESTION # 101
Which of the following approaches can be used to ingest data directly from cloud-based object storage?
Answer: C
Explanation:
External tables are tables that are defined in the Databricks metastore using the information stored in a cloud object storage location. External tables do not manage the data, but provide a schema and a table name to query the data. To create an external table, you can use the CREATE EXTERNAL TABLE statement and specify the object storage path to the LOCATION clause. For example, to create an external table named ext_table on a Parquet file stored in S3, you can use the following statement:
SQL
CREATE EXTERNAL TABLE ext_table (
col1 INT,
col2 STRING
)
STORED AS PARQUET
LOCATION 's3://bucket/path/file.parquet'
AI-generated code. Review and use carefully. More info on FAQ.
NEW QUESTION # 102
A data analysis team is working with the table_bronze SQL table as a source for one of its most complex projects. A stakeholder of the project notices that some of the downstream data is duplicative. The analysis team identifies table_bronze as the source of the duplication.
Which of the following queries can be used to deduplicate the data from table_bronze and write it to a new table table_silver?
A)
CREATE TABLE table_silver AS
SELECT DISTINCT *
FROM table_bronze;
B)
CREATE TABLE table_silver AS
INSERT *
FROM table_bronze;
C)
CREATE TABLE table_silver AS
MERGE DEDUPLICATE *
FROM table_bronze;
D)
INSERT INTO TABLE table_silver
SELECT * FROM table_bronze;
E)
INSERT OVERWRITE TABLE table_silver
SELECT * FROM table_bronze;
Answer: C
Explanation:
Option A uses the SELECT DISTINCT statement to remove duplicate rows from the table_bronze and create a new table table_silver with the deduplicated data. This is the correct way to deduplicate data using Spark SQL12. Option B simply inserts all the rows from table_bronze into table_silver, without removing any duplicates. Option C is not a valid syntax for Spark SQL, as there is no MERGE DEDUPLICATE statement. Option D appends all the rows from table_bronze into table_silver, without removing any duplicates. Option E overwrites the existing data in table_silver with the data from table_bronze, without removing any duplicates. Reference: Delete Duplicate using SPARK SQL, Spark SQL - How to Remove Duplicate Rows
NEW QUESTION # 103
......
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