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Databricks Databricks-Certified-Data-Analyst-Associate Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Data Analyst Associate Exam
Exam Number:	Databricks-Certified-Data-Analyst-Associate
Real Exam Qty:	45
Available Languages:	English
Certificate Validity Period:	2 years
Exam Duration:	90 minutes
Exam Price:	$200 USD
Passing Score:	Approximately 70% (not officially disclosed)
Exam Format:	Multiple-choice, Scenario-based questions
Recommended Training:	Databricks Academy - Data Analyst Associate Learning Path
Exam Registration:	Databricks Certification Portal
Sample Questions:	Databricks Databricks-Certified-Data-Analyst-Associate Sample Questions
Exam Way:	Online proctored or authorized test center
Pre Condition:	No mandatory prerequisites; recommended 6+ months hands-on experience in data analysis and SQL usage with Databricks
Official Syllabus URL:	https://www.databricks.com/learn/certification/data-analyst-associate

>> Databricks-Certified-Data-Analyst-Associateシュミレーション問題集 <<

Databricks-Certified-Data-Analyst-Associate日本語関連対策、Databricks-Certified-Data-Analyst-Associateテスト対策書

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Databricks Databricks-Certified-Data-Analyst-Associate 認定試験の出題範囲：

トピック	出題範囲
トピック 1	Data Visualization and Dashboarding: Sub-topics of this topic are about of describing how notifications are sent, how to configure and troubleshoot a basic alert, how to configure a refresh schedule, the pros and cons of sharing dashboards, how query parameters change the output, and how to change the colors of all of the visualizations. It also discusses customized data visualizations, visualization formatting, Query Based Dropdown List, and the method for sharing a dashboard.
トピック 2	SQL in the Lakehouse: It identifies a query that retrieves data from the database, the output of a SELECT query, a benefit of having ANSI SQL, access, and clean silver-level data. It also compares and contrasts MERGE INTO, INSERT TABLE, and COPY INTO. Lastly, this topic focuses on creating and applying UDFs in common scaling scenarios.
トピック 3	Analytics applications: It describes key moments of statistical distributions, data enhancement, and the blending of data between two source applications. Moroever, the topic also explains last-mile ETL, a scenario in which data blending would be beneficial, key statistical measures, descriptive statistics, and discrete and continuous statistics.
トピック 4	Data Management: The topic describes Delta Lake as a tool for managing data files, Delta Lake manages table metadata, benefits of Delta Lake within the Lakehouse, tables on Databricks, a table owner’s responsibilities, and the persistence of data. It also identifies management of a table, usage of Data Explorer by a table owner, and organization-specific considerations of PII data. Lastly, the topic it explains how the LOCATION keyword changes, usage of Data Explorer to secure data.
トピック 5	Databricks SQL: This topic discusses key and side audiences, users, Databricks SQL benefits, complementing a basic Databricks SQL query, schema browser, Databricks SQL dashboards, and the purpose of Databricks SQL endpoints warehouses. Furthermore, the delves into Serverless Databricks SQL endpoint warehouses, trade-off between cluster size and cost for Databricks SQL endpoints warehouses, and Partner Connect. Lastly it discusses small-file upload, connecting Databricks SQL to visualization tools, the medallion architecture, the gold layer, and the benefits of working with streaming data.

Databricks Certified Data Analyst Associate Exam 認定 Databricks-Certified-Data-Analyst-Associate 試験問題 (Q48-Q53):

質問 # 48
A data analyst has been asked to count the number of customers in each region and has written the following query:

If there is a mistake in the query, which of the following describes the mistake?

A. The query is using ORDER BY. which is not allowed in an aggregation.
B. The query is using count( ' ). which will count all the customers in the customers table, no matter the region.
C. The query is selecting region but region should only occur in the ORDER BY clause.
D. There are no mistakes in the query.
E. The query is missing a GROUP BY region clause.

正解：E

解説：
In the provided SQL query, the data analyst is trying to count the number of customers in each region.
However, they made a mistake by not including the "GROUP BY" clause to group the results by region.
Without this clause, the query will not return counts for each distinct region but rather an error or incorrect result. References: The need for a GROUP BY clause in such queries can be understood from Databricks SQL documentation: Databricks SQL.
I also noticed that you uploaded an image with your question. The image shows a snippet of an SQL query written in plain text on a white background. The query is attempting to select regions and count customers from a "customers" table and order the results by region. There's no visible syntax highlighting or any other color - it's monochromatic. The query is the same as the one in your question. I'm not sure why you included the image, but maybe you wanted to show me the exact format of your query. If so, you can also use code blocks to display formatted content such as SQL queries. For example, you can write:
SELECT region, count(*) AS number_of_customers
FROM customers
ORDER BY region;
This way, you can avoid uploading images and make your questions more clear and concise. I hope this helps.
#

質問 # 49
In which of the following situations should a data analyst use higher-order functions?

A. When custom logic needs to be applied at scale to array data objects
B. When custom logic needs to be applied to simple, unnested data
C. When built-in functions need to run through the Catalyst Optimizer
D. When custom logic needs to be converted to Python-native code
E. When built-in functions are taking too long to perform tasks

正解：A

解説：
Higher-order functions are a simple extension to SQL to manipulate nested data such as arrays. A higher-order function takes an array, implements how the array is processed, and what the result of the computation will be. It delegates to a lambda function how to process each item in the array. This allows you to define functions that manipulate arrays in SQL, without having to unpack and repack them, use UDFs, or rely on limited built-in functions. Higher-order functions provide a performance benefit over user defined functions. Reference: Higher-order functions | Databricks on AWS, Working with Nested Data Using Higher Order Functions in SQL on Databricks | Databricks Blog, Higher-order functions - Azure Databricks | Microsoft Learn, Optimization recommendations on Databricks | Databricks on AWS

質問 # 50
A data analyst creates a Databricks SQL Query where the result set has the following schema:
region STRING
number_of_customer INT
When the analyst clicks on the "Add visualization" button on the SQL Editor page, which of the following types of visualizations will be selected by default?

A. Histogram
B. Line Chart
C. There is no default. The user must choose a visualization type.
D. Violin Chart
E. IBar Chart

正解：E

解説：
According to the Databricks SQL documentation, when a data analyst clicks on the "Add visualization" button on the SQL Editor page, the default visualization type is Bar Chart. This is because the result set has two columns: one of type STRING and one of type INT. The Bar Chart visualization automatically assigns the STRING column to the X-axis and the INT column to the Y-axis. The Bar Chart visualization is suitable for showing the distribution of a numeric variable across different categories. Reference: Visualization in Databricks SQL, Visualization types

質問 # 51
Which of the following should data analysts consider when working with personally identifiable information (PII) data?

A. Legal requirements for the area in which the data was collected
B. All of these considerations
C. Legal requirements for the area in which the analysis is being performed
D. Organization-specific best practices for Pll data
E. None of these considerations

正解：B

解説：
Data analysts should consider all of these factors when working with PII data, as they may affect the data security, privacy, compliance, and quality. PII data is any information that can be used to identify a specific individual, such as name, address, phone number, email, social security number, etc. PII data may be subject to different legal and ethical obligations depending on the context and location of the data collection and analysis. For example, some countries or regions may have stricter data protection laws than others, such as the General Data Protection Regulation (GDPR) in the European Union. Data analysts should also follow the organization-specific best practices for PII data, such as encryption, anonymization, masking, access control, auditing, etc. These best practices can help prevent data breaches, unauthorized access, misuse, or loss of PII data. References:
* How to Use Databricks to Encrypt and Protect PII Data
* Automating Sensitive Data (PII/PHI) Detection
* Databricks Certified Data Analyst Associate

質問 # 52
A data analyst has created a Query in Databricks SQL, and now they want to create two data visualizations from that Query and add both of those data visualizations to the same Databricks SQL Dashboard.
Which of the following steps will they need to take when creating and adding both data visualizations to the Databricks SQL Dashboard?

A. They will need to create two separate dashboards.
B. They will need to add two separate visualizations to the dashboard based on the same Query.
C. They will need to decide on a single data visualization to add to the dashboard.
D. They will need to copy the Query and create one data visualization per query.
E. They will need to alter the Query to return two separate sets of results.

正解：B

解説：
A data analyst can create multiple visualizations from the same query in Databricks SQL by clicking the + button next to the Results tab and selecting Visualization. Each visualization can have a different type, name, and configuration. To add a visualization to a dashboard, the data analyst can click the vertical ellipsis button beneath the visualization, select + Add to Dashboard, and choose an existing or new dashboard. The data analyst can repeat this process for each visualization they want to add to the same dashboard. References: Visualization in Databricks SQL, Visualize queries and create a dashboard in Databricks SQL

質問 # 53
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