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

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

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q17-Q22):

NEW QUESTION # 17
A data analyst has a managed table table_name in database database_name. They would now like to remove the table from the database and all of the data files associated with the table. The rest of the tables in the database must continue to exist.
Which of the following commands can the analyst use to complete the task without producing an error?

A. DELETE TABLE table_name FROM database_name;
B. DROP DATABASE database_name;
C. DROP TABLE database_name.table_name;
D. DROP TABLE table_name FROM database_name;
E. DELETE TABLE database_name.table_name;

Answer: C

Explanation:
The DROP TABLE command removes a table from the metastore and deletes the associated data files. The syntax for this command is DROP TABLE [IF EXISTS] [database_name.]table_name;. The optional IF EXISTS clause prevents an error if the table does not exist. The optional database_name. prefix specifies the database where the table resides. If not specified, the current database is used. Therefore, the correct command to remove the table table_name from the database database_name and all of the data files associated with it is DROP TABLE database_name.table_name;. The other commands are either invalid syntax or would produce undesired results. Reference: Databricks - DROP TABLE

NEW QUESTION # 18
A data engineer wants to schedule their Databricks SQL dashboard to refresh once per day, but they only want the associated SQL endpoint to be running when it is necessary.
Which of the following approaches can the data engineer use to minimize the total running time of the SQL endpoint used in the refresh schedule of their dashboard?

A. They can reduce the cluster size of the SQL endpoint.
B. They can ensure the dashboard's SQL endpoint is not one of the included query's SQL endpoint.
C. They can turn on the Auto Stop feature for the SQL endpoint.
D. They can ensure the dashboard's SQL endpoint matches each of the queries' SQL endpoints.
E. They can set up the dashboard's SQL endpoint to be serverless.

Answer: C

Explanation:
Option C is correct because Auto Stop is specifically designed to stop a SQL warehouse when it has been idle for a configured number of minutes. This minimizes total warehouse running time after the scheduled dashboard refresh completes. Reducing cluster size may reduce hourly cost but does not stop the warehouse.
Serverless can help with management, but the feature directly matching "only running when necessary" is Auto Stop. Official Databricks extract: "Auto Stop determines whether the warehouse stops if it's idle for the specified number of minutes." Databricks also notes that idle SQL warehouses continue to accumulate charges until stopped.

NEW QUESTION # 19
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. None of these
D. Table metadata
E. Table metadata, data summary visualizations, and owner account information

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.

NEW QUESTION # 20
A data analyst is working in an organization that utilizes a multi-hop, medallion architecture. They have been tasked with creating a new Gold table from an existing Silver table.
Which query is performing a hop from a Silver table to a Gold table?

Answer: A

Explanation:
Option C is the correct answer because it creates a new aggregated table, store_sales, from the existing cleaned
/refined table cleaned_transactions. In a medallion architecture, a table such as cleaned_transactions represents a Silver-layer table because it contains cleaned or validated transaction-level data. The query then creates store_sales by grouping by store_id and calculating SUM(sales), which is an aggregation suitable for analytics and reporting. That is exactly the Silver-to-Gold hop.
Option A creates cleaned_transactions from transactions, so it is moving toward a cleaned Silver table, not creating a Gold table. Option B creates transactions from raw_transactions, which is closer to a raw-to-Bronze or Bronze preparation step. Option D filters rows from transactions into cleaned_transactions, which is also a cleaning/validation step associated with Silver, not Gold.
Exact extract from official Databricks documentation: Databricks describes medallion architecture as improving data through "Bronze # Silver # Gold layer tables." The same official page identifies Silver as
"Data cleaning and validation" and Gold as "Dimensional modeling and aggregation." Databricks also states that the Gold layer "consists of aggregated data tailored for analytics and reporting."

NEW QUESTION # 21
In which circumstance will there be a substantial difference between the variable's mean and median values?

A. When the variable is of the categorical type
B. When the variable contains no outliers
C. When the variable contains a lot of extreme outliers
D. When the variable is of the boolean type

Answer: C

Explanation:
The mean is sensitive to extreme values, often called outliers, which can significantly skew the average away from the true center of the data. The median, however, is a measure of central tendency that is resistant to such outliers because it only considers the middle value(s) when the data is ordered. Therefore, when a variable contains many extreme outliers, there will be a substantial difference between the mean and the median. According to Databricks data analysis materials, this is a fundamental concept when choosing summary statistics for reporting.

NEW QUESTION # 22
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