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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Analyzing Queries | 15% | - Query Analysis
|
| Topic 2: Securing Data | 8% | - Security and Access Control
|
| Topic 3: Data Modeling with Databricks SQL | 5% | - Modeling Concepts
|
| Topic 4: Creating Dashboards and Visualizations | 16% | - Visualization
|
| Topic 5: Understanding of Databricks Data Intelligence Platform | 11% | - Catalog and Governance
|
| Topic 6: Executing Queries Using Databricks SQL and SQL Warehouses | 20% | - SQL Querying
|
| Topic 7: Managing Data | 8% | - Data Management
|
| Topic 8: Importing Data | 5% | - Data Import
|
| Topic 9: Developing AI/BI Genie Spaces | 12% | - AI and BI Features
|
>> Databricks-Certified-Data-Analyst-Associate認證 <<
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問題 #88
What is an advantage of using a Delta Lake-based data lakehouse over classic enterprise data warehouse solutions?
答案：B
解題說明：
Option A is correct. Classic enterprise data warehouses commonly use proprietary storage formats, while the Databricks lakehouse is built around open table formats such as Delta Lake and Iceberg. Databricks documentation states that Delta Lake is open source and that the Databricks platform uses no proprietary data formats to avoid vendor lock-in. Schema enforcement and ACID transactions are important Delta Lake capabilities, but they are not the best differentiator "over classic enterprise data warehouse solutions," because enterprise warehouses also typically provide managed reliability and transactional behavior. The unique lakehouse advantage here is open-source/open-format interoperability. References: Databricks Delta Lake and lakehouse architecture documentation.
問題 #89
A data analyst has been asked to use the below table sales_table to get the percentage rank of products within region by the sales:
The result of the query should look like this:
Which of the following queries will accomplish this task?
A)
B)
C)
答案：D
解題說明：
The correct query to get the percentage rank of products within region by the sales is option B. This query uses the PERCENT_RANK() window function to calculate the relative rank of each product within each region based on the sales amount. The window function is partitioned by region and ordered by sales in descending order. The result is aliased as rank and displayed along with the region and product columns. The other options are incorrect because:
A) Option A uses the RANK() window function instead of the PERCENT_RANK() function. The RANK() function returns the rank of each row within the partition, but not the percentage rank. Also, the query does not have a GROUP BY clause, which is required for aggregate functions like SUM().
C) Option C uses the DENSE_RANK() window function instead of the PERCENT_RANK() function. The DENSE_RANK() function returns the rank of each row within the partition, but not the percentage rank. Also, the query does not have a GROUP BY clause, which is required for aggregate functions like SUM().
D) Option D uses the ROW_NUMBER() window function instead of the PERCENT_RANK() function. The ROW_NUMBER() function returns the sequential number of each row within the partition, but not the percentage rank. Also, the query does not have a GROUP BY clause, which is required for aggregate functions like SUM(). Reference:
1: PERCENT_RANK (Transact-SQL)
2: Window functions in Databricks SQL
3: Databricks Certified Data Analyst Associate Exam Guide
問題 #90
A data analyst is working on a DataFrame named dates_df and needs to add a new column, date, derived from the timestamp field.
Which code fragment should be used to extract the date from a timestamp?
答案：A
解題說明：
Option B is correct. The function to_date converts a timestamp or date-like expression to a date value, which matches the requirement to extract the date from the timestamp field. unix_timestamp converts to a Unix timestamp value, date_format formats a date or timestamp as a string, and from_unixtime converts Unix time into a timestamp/string representation. Official Databricks documentation states that to_date(expr [, fmt]) returns the expression cast to a date.
問題 #91
A data analyst is working with gold-layer tables to complete an ad-hoc project. A stakeholder has provided the analyst with an additional dataset that can be used to augment the gold-layer tables already in use.
Which of the following terms is used to describe this data augmentation?
答案：E
解題說明：
Data enhancement is the process of adding or enriching data with additional information to improve its quality, accuracy, and usefulness. Data enhancement can be used to augment existing data sources with new data sources, such as external datasets, synthetic data, or machine learning models. Data enhancement can help data analysts to gain deeper insights, discover new patterns, and solve complex problems. Data enhancement is one of the applications of generative AI, which can leverage machine learning to generate synthetic data for better models or safer data sharing1.
In the context of the question, the data analyst is working with gold-layer tables, which are curated business-level tables that are typically organized in consumption-ready project-specific databases234. The gold-layer tables are the final layer of data transformations and data quality rules in the medallion lakehouse architecture, which is a data design pattern used to logically organize data in a lakehouse2. The stakeholder has provided the analyst with an additional dataset that can be used to augment the gold-layer tables already in use. This means that the analyst can use the additional dataset to enhance the existing gold-layer tables with more information, such as new features, attributes, or metrics. This data augmentation can help the analyst to complete the ad-hoc project more effectively and efficiently.
Reference:
What is the medallion lakehouse architecture? - Databricks
Data Warehousing Modeling Techniques and Their Implementation on the Databricks Lakehouse Platform | Databricks Blog What is the medallion lakehouse architecture? - Azure Databricks What is a Medallion Architecture? - Databricks Synthetic Data for Better Machine Learning | Databricks Blog
問題 #92
Which of the following benefits of using Databricks SQL is provided by Data Explorer?
答案：C
解題說明：
Data Explorer is a user interface that allows you to discover and manage data, schemas, tables, models, and permissions in Databricks SQL. You can use Data Explorer to view schema details, preview sample data, and see table and model details and properties. Administrators can view and change owners, and admins and data object owners can grant and revoke permissions1. Reference: Discover and manage data using Data Explorer
問題 #93
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