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

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

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Databricks Certified Data Analyst Associate Exam Databricks-Certified-Data-Analyst-Associate Prüfungsfragen mit Lösungen (Q85-Q90):

85. Frage
What describes the variance of a set of values?

A. Variance is a measure of how far a set of values is spread out from the sets central value.
B. Variance is a measure of how far a single observed value is from a set ot va IN
C. Variance is a measure of how far an observed value is from the variable's maximum or minimum value.
D. Variance is a measure of central tendency of a set of values.

Antwort: A

Begründung:
Variance is a statistical measure that quantifies the dispersion or spread of a set of values around their mean (central value). It is calculated by taking the average of the squared differences between each value and the mean of the dataset. A higher variance indicates that the data points are more spread out from the mean, while a lower variance suggests that they are closer to the mean. This measure is fundamental in statistics to understand the degree of variability within a dataset.WikipediaWikipedia+1Investopedia+1

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

Antwort: A

Begründung:
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.
#

87. Frage
A data analyst needs to use the Databricks Lakehouse Platform to quickly create SQL queries and data visualizations. It is a requirement that the compute resources in the platform can be made serverless, and it is expected that data visualizations can be placed within a dashboard.
Which of the following Databricks Lakehouse Platform services/capabilities meets all of these requirements?

A. Databricks SQL
B. Databricks Machine Learning
C. Databricks Notebooks
D. Tableau
E. Delta Lake

Antwort: A

Begründung:
Databricks SQL is a serverless data warehouse on the Lakehouse that lets you run all of your SQL and BI applications at scale with your tools of choice, all at a fraction of the cost of traditional cloud data warehouses1. Databricks SQL allows you to create SQL queries and data visualizations using the SQL Analytics UI or the Databricks SQL CLI2. You can also place your data visualizations within a dashboard and share it with other users in your organization3. Databricks SQL is powered by Delta Lake, which provides reliability, performance, and governance for your data lake4. Reference:
Databricks SQL
Query data using SQL Analytics
Visualizations in Databricks notebooks
Delta Lake

88. Frage
A data engineering team has created a Structured Streaming pipeline that processes data in micro-batches and populates gold-level tables. The microbatches are triggered every minute.
A data analyst has created a dashboard based on this gold-level data. The project stakeholders want to see the results in the dashboard updated within one minute or less of new data becoming available within the gold-level tables.
Which of the following cautions should the data analyst share prior to setting up the dashboard to complete this task?

A. The dashboard cannot be refreshed that quickly
B. The streaming cluster is not fault tolerant
C. The required compute resources could be costly
D. The gold-level tables are not appropriately clean for business reporting
E. The streaming data is not an appropriate data source for a dashboard

Antwort: C

Begründung:
A Structured Streaming pipeline that processes data in micro-batches and populates gold-level tables every minute requires a high level of compute resources to handle the frequent data ingestion, processing, and writing. This could result in a significant cost for the organization, especially if the data volume and velocity are large. Therefore, the data analyst should share this caution with the project stakeholders before setting up the dashboard and evaluate the trade-offs between the desired refresh rate and the available budget. The other options are not valid cautions because:
B) The gold-level tables are assumed to be appropriately clean for business reporting, as they are the final output of the data engineering pipeline. If the data quality is not satisfactory, the issue should be addressed at the source or silver level, not at the gold level.
C) The streaming data is an appropriate data source for a dashboard, as it can provide near real-time insights and analytics for the business users. Structured Streaming supports various sources and sinks for streaming data, including Delta Lake, which can enable both batch and streaming queries on the same data.
D) The streaming cluster is fault tolerant, as Structured Streaming provides end-to-end exactly-once fault-tolerance guarantees through checkpointing and write-ahead logs. If a query fails, it can be restarted from the last checkpoint and resume processing.
E) The dashboard can be refreshed within one minute or less of new data becoming available in the gold-level tables, as Structured Streaming can trigger micro-batches as fast as possible (every few seconds) and update the results incrementally. However, this may not be necessary or optimal for the business use case, as it could cause frequent changes in the dashboard and consume more resources. Reference: Streaming on Databricks, Monitoring Structured Streaming queries on Databricks, A look at the new Structured Streaming UI in Apache Spark 3.0, Run your first Structured Streaming workload

89. Frage
Which of the following statements about a refresh schedule is incorrect?

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

Antwort: E

Begründung:
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.

90. Frage
......

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