Databricks-Certified-Data-Analyst-Associate Official Study Guide - Databricks-Certified-Data-Analyst-Associate Latest Test Guide

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

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

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q49-Q54):

NEW QUESTION # 49
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, count(order_id)FROM customersJOIN ordersON customers.customer_id = orders.customer_id GROUP BY customerjiame;
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, (order_id) number_of_ordersFROM customersJOIN ordersON customers.
customer_id = orders.customer_id;
D. SELECT customer_name, count(order_id) number_of_ordersFROM customersJOIN ordersON customers.customer_id = orders.customer_id USE customer_name;

Answer: B

Explanation:
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.

NEW QUESTION # 50
Which of the following commands will return the location of database customer360?

A. DESCRIBE LOCATION customer360;
B. DROP DATABASE customer360;
C. USE DATABASE customer360;
D. ALTER DATABASE customer360 SET DBPROPERTIES ( ' location ' = ' /user ' );
E. DESCRIBE DATABASE customer360;

Answer: E

Explanation:
Option C is correct. DESCRIBE DATABASE customer360 returns metadata for the database/schema, including its filesystem location. DESCRIBE LOCATION is for external locations, not databases. DROP DATABASE deletes a database, ALTER DATABASE changes metadata/properties, and USE DATABASE changes the current database context. Official Databricks extract: DESCRIBE DATABASE is an alias for DESCRIBE SCHEMA, and DESCRIBE SCHEMA returns metadata including the schema's "location on the filesystem."

NEW QUESTION # 51
Which open-source project helps to enable the data lakehouse by adding organization, reliability, performance, and data governance to data lake architectures?

A. Delta Lake
B. Databricks SQL
C. Apache Spark
D. MLflow

Answer: A

Explanation:
The correct answer is C because Delta Lake is the open-source storage layer that provides reliability and structure for data lakes. Delta Lake brings ACID transactions, scalable metadata handling, and batch
/streaming support to data lake storage, which are core capabilities behind the lakehouse architecture. MLflow is for machine learning lifecycle management, Apache Spark is a distributed processing engine, and Databricks SQL is a Databricks product for SQL analytics, not the open-source storage project described.
Official documentation extract used: Databricks states that Delta Lake is "open source software" and provides
"ACID transactions and scalable metadata handling."

NEW QUESTION # 52
Which of the following statements about a refresh schedule is incorrect?

A. You must have workspace administrator privileges to configure a refresh schedule
B. A query being refreshed on a schedule does not use a SQL Warehouse (formerly known as SQL Endpoint).
C. A refresh schedule is not the same as an alert.
D. Refresh schedules can be configured in the Query Editor.
E. A query can be refreshed anywhere from 1 minute lo 2 weeks

Answer: A

Explanation:
This statement is incorrect. In Databricks SQL, any user with sufficient permissions on the query or dashboard can configure a refresh schedule-workspace administrator privileges are not required.
Here is the breakdown of the correct information:
* A. True - Queries can be scheduled to refresh at intervals ranging from 1 minute to 2 weeks.
* B. True - You can configure refresh schedules in the Query Editor.
* C. False statement - A query being refreshed does use a SQL Warehouse. However, the option in question says it does not use a warehouse, which would be incorrect in a different context. Since this is a trickier one, we know that scheduled queries do require a SQL Warehouse to run.
* D. True - Refresh schedules are different from alerts; alerts are triggered based on specific conditions being met in query results.
* E. False (and thus the correct answer to this question) - You do not need to be a workspace admin to set a refresh schedule. You only need the correct permissions on the object.
Reference: Schedule a Query in Databricks SQL

NEW QUESTION # 53
Delta Lake stores table data as a series of data files, but it also stores a lot of other information.
Which of the following is stored alongside data files when using Delta Lake?

A. Data summary visualizations
B. Owner account information
C. Table metadata, data summary visualizations, and owner account information
D. Table metadata
E. None of these

Answer: D

Explanation:
Delta Lake is a storage layer that enhances data lakes with features like ACID transactions, schema enforcement, and time travel. While it stores table data as Parquet files, Delta Lake also keeps a transaction log (stored in the _delta_log directory) that contains detailed table metadata.
This metadata includes:
* Table schema
* Partitioning information
* Data file paths
* Transactional operations like inserts, updates, and deletes
* Commit history and version control
This metadata is critical for supporting Delta Lake's advanced capabilities such as time travel and efficient query execution. Delta Lake does not store data summary visualizations or owner account information directly alongside the data files.
Reference: Delta Lake Table Features - Databricks Documentation

NEW QUESTION # 54
......

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