Databricks-Certified-Data-Analyst-Associate퍼펙트덤프공부문제 & Databricks-Certified-Data-Analyst-Associate퍼펙트덤프데모문제다운

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

Section	Weight	Objectives
Topic 1: Executing Queries Using Databricks SQL and SQL Warehouses	20%	- SQL Querying 1. Query Optimization 2. Joins and Subqueries 3. SQL Warehouses 4. Filtering and Aggregation 5. Writing SQL Queries
Topic 2: Managing Data	8%	- Data Management 1. Discovering Data Assets 2. Tagging and Lineage 3. Handling Missing and Invalid Data 4. Cleaning Data with SQL
Topic 3: Securing Data	8%	- Security and Access Control 1. Permissions and Roles 2. Data Access Policies 3. Unity Catalog Security
Topic 4: Developing AI/BI Genie Spaces	12%	- AI and BI Features 1. Natural Language Queries 2. AI/BI Genie 3. Workspace Configuration
Topic 5: Data Modeling with Databricks SQL	5%	- Modeling Concepts 1. Views and Relationships 2. Data Organization 3. Fact and Dimension Tables
Topic 6: Analyzing Queries	15%	- Query Analysis 1. Monitoring Query Execution 2. Performance Tuning 3. Reading Query Plans
Topic 7: Creating Dashboards and Visualizations	16%	- Visualization 1. Charts and Graphs 2. Alerts and Filters 3. Dashboards 4. Sharing and Publishing
Topic 8: Importing Data	5%	- Data Import 1. Uploading Files 2. Loading CSV and JSON Data 3. Creating Tables from Imported Data
Topic 9: Understanding of Databricks Data Intelligence Platform	11%	- Catalog and Governance 1. Catalogs and Schemas 2. Data Lineage 3. Managed and External Tables 4. Views and Certified Tables - Platform Components 1. Databricks SQL 2. Mosaic AI 3. Delta Lake 4. Unity Catalog 5. Lakeflow Jobs

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Databricks-Certified-Data-Analyst-Associate퍼펙트 덤프데모문제 다운, Databricks-Certified-Data-Analyst-Associate최신 시험기출문제

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최신 Data Analyst Databricks-Certified-Data-Analyst-Associate 무료샘플문제 (Q99-Q104):

질문 # 99
A data analyst runs the following command:
SELECT age, country
FROM my_table
WHERE age > = 75 AND country = ' canada ' ;
Which of the following tables represents the output of the above command?

정답：A

설명：
The SQL query provided is designed to filter out records from "my_table" where the age is 75 or above and the country is Canada. Since I can't view the content of the links provided directly, I need to rely on the image attached to this question for context. Based on that, Option E (the image attached) represents a table with columns "age" and "country", showing records where age is 75 or above and country is Canada. References:
The answer can be inferred from understanding SQL queries and their outputs as per Databricks documentation: Databricks SQL

질문 # 100
A data team has been given a series of projects by a consultant that need to be implemented in the Databricks Lakehouse Platform.
Which of the following projects should be completed in Databricks SQL?

A. Segmenting customers into like groups using a clustering algorithm
B. Tracking usage of feature variables for machine learning projects
C. Combining two data sources into a single, comprehensive dataset
D. Testing the quality of data as it is imported from a source
E. Automating complex notebook-based workflows with multiple tasks

정답：C

설명：
Databricks SQL is a service that allows users to query data in the lakehouse using SQL and create visualizations and dashboards1. One of the common use cases for Databricks SQL is to combine data from different sources and formats into a single, comprehensive dataset that can be used for further analysis or reporting2. For example, a data analyst can use Databricks SQL to join data from a CSV file and a Parquet file, or from a Delta table and a JDBC table, and create a new table or view that contains the combined data3. This can help simplify the data management and governance, as well as improve the data quality and consistency. Reference:
Databricks SQL overview
Databricks SQL use cases
Joining data sources

질문 # 101
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 customer_name, count(order_id) number_of_orders
FROM customers
JOIN orders
ON customers.customer_id = orders.customer_id USE customer_name;
B. SELECT customer_name, count(order_id) AS number_of_orders
FROM customers
JOIN orders
ON customers.customer_id = orders.customer_id
GROUP BY customer_name;
C. SELECT customerjiame, count(order_id)
FROM customers
JOIN orders
ON customers.customer_id = orders.customer_id GROUP BY customerjiame;
D. SELECT customerjiame, (order_id) number_of_orders
FROM customers
JOIN orders
ON customers.customer_id = orders.customer_id;

정답：B

질문 # 102
Which of the following layers of the medallion architecture is most commonly used by data analysts?

A. Gold
B. None of these layers are used by data analysts
C. All of these layers are used equally by data analysts
D. Silver
E. Bronze

정답：A

설명：
The gold layer of the medallion architecture contains data that is highly refined and aggregated, and powers analytics, machine learning, and production applications. Data analysts typically use the gold layer to access data that has been transformed into knowledge, rather than just information. The gold layer represents the final stage of data quality and optimization in the lakehouse. References: What is the medallion lakehouse architecture?

질문 # 103
Which of the following should data analysts consider when working with personally identifiable information (PII) data?

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

정답：D

설명：
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

질문 # 104
......

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