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

Topic	Details
Topic 1	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.
Topic 2	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.
Topic 3	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.
Topic 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.
Topic 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.

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q13-Q18):

NEW QUESTION # 13
A data analyst has a series of queries in a SQL program. The data analyst wants this program to run every day. They only want the final query in the program to run on Sundays. They ask for help from the data engineering team to complete this task.
Which of the following approaches could be used by the data engineering team to complete this task?

A. They could only run the entire program on Sundays.
B. They could automatically restrict access to the source table in the final query so that it is only accessible on Sundays.
C. They could wrap the queries using PySpark and use Python's control flow system to determine when to run the final query.
D. They could redesign the data model to separate the data used in the final query into a new table.
E. They could submit a feature request with Databricks to add this functionality.

Answer: C

Explanation:
Option B is correct. The program can be scheduled to run daily as a Databricks job, and Python control flow can determine whether the final query runs only when the current day is Sunday. Databricks jobs support scheduled execution, and PySpark notebooks can run SQL through Spark while using Python logic around those commands. Official Databricks extract: jobs can be configured "to run it on a time-based schedule," and spark.sql/Spark DataFrame operations allow Python-based workflows to interact with SQL data.

NEW QUESTION # 14
Which of the following Structured Streaming queries is performing a hop from a Silver table to a Gold table?

A. ( spark.table( " sales " ) .filter(col( " units " ) > 0) .writeStream .option( " checkpointLocation " , checkpointPath) .outputMode( " append " ) .table( " newSales " ))
B. ( spark.readStream.load(rawSalesLocation) .writeStream .option( " checkpointLocation " , checkpointPath) .outputMode( " append " ) .table( " newSales " ))
C. Option A
D. Option E
E. ( spark.table( " sales " ) .groupBy( " store " ) .agg(sum( " sales " )) .writeStream .option( " checkpointLocation " , checkpointPath) .outputMode( " complete " ) .table( " newSales " ))
F. ( spark.table( " sales " ) .withColumn( " avgPrice " , col( " sales " ) / col( " units " )) .writeStream .option ( " checkpointLocation " , checkpointPath) .outputMode( " append " ) .table( " newSales " ))
G. ( spark.table( " sales " ) .withColumn( " avgPrice " , col( " sales " ) / col( " units " )) .writeStream .option ( " checkpointLocation " , checkpointPath) .outputMode( " append " ) .table( " newSales " ))
H. Option B
I. Option D
J. Option C

Answer: E

Explanation:
Option E is correct. A Silver-to-Gold hop typically reads cleaned/refined Silver data and writes aggregated, analytics-ready Gold data. The query reads from sales, groups by store, and aggregates sum( " sales " ), producing a summary table suitable for reporting or dashboarding. That matches the Gold layer. Option A reads from a raw location, which is not Silver-to-Gold. Option D filters invalid units, which is a cleaning step associated with Silver. Options B and C add a derived column but do not create a Gold-level aggregated table.
Official Databricks medallion architecture documentation states that Silver is where data cleanup and validation are performed, while the Gold layer "consists of aggregated data tailored for analytics and reporting." Databricks Structured Streaming documentation also shows .writeStream.outputMode( " complete
" ).toTable(...) as a valid output mode pattern for stateful streaming aggregations.

NEW QUESTION # 15
Where in the Databricks SQL workspace can a data analyst configure a refresh schedule for a query when the query is not attached to a dashboard or alert?

A. Data bxplorer
B. The Visualization editor
C. The Query Editor
D. The Dashboard Editor

Answer: C

Explanation:
In Databricks SQL, to configure a refresh schedule for a query that is not attached to a dashboard or alert, a data analyst should use the Query Editor. Within the Query Editor, there is an option to set up scheduled executions for queries. This feature enables the query to run at specified intervals, ensuring that the results are updated regularly. By scheduling queries in this manner, analysts can automate data refreshes and maintain up- to-date query results without manual intervention.
Reference: Schedule a query - Databricks Documentation

NEW QUESTION # 16
A data analyst is working on a DataFrame named dates_df and needs to add a new column, date, derived from the timestamp field.
Which code fragment should be used to extract the date from a timestamp?

A. dates_df.withColumn( " date " , f.from_unixtime( " timestamp " )).show()
B. dates_df.withColumn( " date " , f.unix_timestamp( " timestamp " )).show()
C. dates_df.withColumn( " date " , f.date_format( " timestamp " , " yyyy-MM-dd " )).show()
D. dates_df.withColumn( " date " , f.to_date( " timestamp " )).show()

Answer: D

Explanation:
Option B is correct. The function to_date converts a timestamp or date-like expression to a date value, which matches the requirement to extract the date from the timestamp field. unix_timestamp converts to a Unix timestamp value, date_format formats a date or timestamp as a string, and from_unixtime converts Unix time into a timestamp/string representation. Official Databricks documentation states that to_date(expr [, fmt]) returns the expression cast to a date.

NEW QUESTION # 17
A data analyst is troubleshooting a query in Databricks SQL that fails when processing large datasets and complex join operations. Logs indicate that the job consistently aborts due to resource constraint errors on the cluster.
Which Query Profile metric should the analyst use to identify the operator that is causing resource overuse?

A. Memory peak per operator
B. Bytes spilled to disk per operator
C. Time spent per operator
D. Shuffle read size per operator

Answer: A

Explanation:
The correct answer is C because the issue is a resource constraint failure, and the analyst needs to identify which operator is consuming excessive memory. In Query Profile, Memory peak shows memory usage at the operator level and helps identify the operator causing resource overuse. Time spent helps identify slow operators, shuffle read size helps analyze data movement, and bytes spilled to disk indicates spill behavior, but the most direct metric for resource overuse due to memory pressure is memory peak.
Official documentation extract used: Databricks Query Profile graph view shows metrics such as "Time spent, Memory peak, and Rows."

NEW QUESTION # 18
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