Databricks-Certified-Data-Analyst-Associate復習攻略問題 & Databricks-Certified-Data-Analyst-Associate試験解説

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Databricks Databricks-Certified-Data-Analyst-Associate Exam Syllabus Topics:

Section	Objectives
Topic 1: Data Governance and Security	- Access control and permissions 1. Row-level and column-level security concepts 2. Unity Catalog basics
Topic 2: Databricks SQL and SQL Analytics	- Querying data using SQL in Databricks 1. Joins and aggregations 2. SELECT statements and filtering data - Data transformation and analysis 1. Window functions 2. Common table expressions (CTEs)
Topic 3: Data Management in Lakehouse	- Delta Lake fundamentals 1. ACID transactions and versioning 2. Table optimization concepts - Data ingestion and preparation 1. Batch and streaming ingestion concepts
Topic 4: Data Visualization and Dashboards	- Creating dashboards in Databricks SQL 1. Dashboard configuration and sharing 2. Visualizing query results

>> Databricks-Certified-Data-Analyst-Associate復習攻略問題 <<

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Databricks Certified Data Analyst Associate Exam 認定 Databricks-Certified-Data-Analyst-Associate 試験問題 (Q64-Q69):

質問 # 64
A data analyst at an e-commerce company needs to process daily sales data. The data consists of approximately 50,000 records stored in a single CSV file, totaling about 20 MB. The analyst needs to perform aggregations and generate a summary report.
Which approach could the data analyst use in this situation?

A. Use a local Python script with the pandas library to read and analyze the CSV file.
B. Set up a Hadoop ecosystem with HDFS and MapReduce for distributed processing.
C. Deploy a real-time streaming solution using Spark Streaming to process incoming data.
D. Implement Apache Spark with a distributed cluster to process the data in parallel.

正解：A

解説：
Option B is correct. A 20 MB CSV file with about 50,000 records is small enough for a local pandas workflow. A real-time streaming solution, a distributed Spark cluster, or Hadoop MapReduce would add unnecessary complexity for a small single-file batch analysis. Databricks documentation explains that pandas is available in Databricks Runtime and can be used for data analysis, while pandas API on Spark is useful when pandas-style workloads need to scale beyond smaller datasets. Databricks also notes that pandas does not scale out to big data, which is exactly why Spark or pandas API on Spark is used for larger workloads, not for a small 20 MB file.

質問 # 65
A data analyst needs to use the Databricks Lakehouse Platform to quickly create SQL queries and data visualizations. It is a requirement that the compute resources in the platform can be made serverless, and it is expected that data visualizations can be placed within a dashboard.
Which of the following Databricks Lakehouse Platform services/capabilities meets all of these requirements?

A. Delta Lake
B. Databricks Machine Learning
C. Databricks SQL
D. Tableau
E. Databricks Notebooks

正解：C

解説：
Databricks SQL is a serverless data warehouse on the Lakehouse that lets you run all of your SQL and BI applications at scale with your tools of choice, all at a fraction of the cost of traditional cloud data warehouses1. Databricks SQL allows you to create SQL queries and data visualizations using the SQL Analytics UI or the Databricks SQL CLI2. You can also place your data visualizations within a dashboard and share it with other users in your organization3. Databricks SQL is powered by Delta Lake, which provides reliability, performance, and governance for your data lake4. References:
* Databricks SQL
* Query data using SQL Analytics
* Visualizations in Databricks notebooks
* Delta Lake

質問 # 66
A data analyst wants the following output:
customer_name
number_of_orders
John Doe
388
Zhang San
234
Which statement will produce this output?

A. SELECT customerjiame, (order_id) number_of_ordersFROM customersJOIN ordersON customers.
customer_id = orders.customer_id;
B. SELECT customer_name, count(order_id) AS number_of_ordersFROM customersJOIN ordersON customers.customer_id = orders.customer_idGROUP BY customer_name;
C. SELECT customerjiame, count(order_id)FROM customersJOIN ordersON customers.customer_id = orders.customer_id GROUP BY customerjiame;
D. SELECT customer_name, count(order_id) number_of_ordersFROM customersJOIN ordersON customers.customer_id = orders.customer_id USE customer_name;

正解：B

解説：
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.

質問 # 67
A data analyst has been asked to use the below table sales_table to rank products within region by the sales.
Input table:
region product sales
WEST A 1880.59
EAST A 2045.99
EAST B 4583.23
WEST B 3391.19
The result of the query should look like this:
region product rank
EAST B 1
EAST A 2
WEST B 1
WEST A 2
Which query will accomplish this task?

A. Option D
B. Option A
C. Option C
D. SELECT region, product, RANK() OVER ( ORDER BY sales DESC ) AS rankFROM sales_table;
E. SELECT region, product, RANK() OVER ( PARTITION BY region ORDER BY sales DESC ) AS rankFROM sales_table;
F. SELECT region, product, RANK() OVER ( PARTITION BY region ) AS rankFROM sales_table;
G. SELECT region, product, RANK() OVER ( PARTITION BY product ORDER BY sales DESC ) AS rankFROM sales_table;
H. Option B

正解：E

解説：
Option D is correct. The phrase "rank products within region by the sales" means each region must be ranked separately, so the window must use PARTITION BY region. The ranking must be based on highest sales first, so the window must use ORDER BY sales DESC. Option A ranks across all regions together. Option B ranks within product, not within region. Option C does not specify the sales ordering. Official Databricks extract:
rank() "returns the rank of a value compared to all values in the partition," and its OVER clause must include ORDER BY.

質問 # 68
A data analyst wants to generate insights from large, complex datasets. The analyst needs to quickly understand the meaning of various data columns, ask questions in natural language, and receive AI-driven recommendations for optimizing data queries and workflows.
Which Databricks component is primarily responsible for enabling these capabilities?

A. Databricks Assistant
B. Genie Spaces
C. Data Intelligence Engine
D. Unity Catalog

正解：C

解説：
Option A is correct. The Data Intelligence Engine is the platform-level intelligence layer that understands the semantics and uniqueness of an organization's data and enables AI-assisted experiences across Databricks.
Unity Catalog provides governance and metadata management, Genie Spaces provide a natural-language interface for curated business data, and Databricks Assistant is a user-facing assistant for code/query help.
However, the question asks which component is primarily responsible for enabling these capabilities across the platform; that is the Data Intelligence Engine. Databricks describes the platform as powered by a Data Intelligence Engine that understands the uniqueness and semantics of data and helps optimize performance.
References: Databricks Data Intelligence Platform documentation and Data Analyst Associate Exam Guide.

質問 # 69
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