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

Section	Objectives
Topic 1: Data Visualization and Dashboards	- Creating dashboards in Databricks SQL 1. Dashboard configuration and sharing 2. Visualizing query results
Topic 2: Data Management in Lakehouse	- Data ingestion and preparation 1. Batch and streaming ingestion concepts - Delta Lake fundamentals 1. ACID transactions and versioning 2. Table optimization concepts
Topic 3: Databricks SQL and SQL Analytics	- Data transformation and analysis 1. Window functions 2. Common table expressions (CTEs) - Querying data using SQL in Databricks 1. SELECT statements and filtering data 2. Joins and aggregations
Topic 4: Data Governance and Security	- Access control and permissions 1. Unity Catalog basics 2. Row-level and column-level security concepts

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q70-Q75):

NEW QUESTION # 70
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 Notebooks
B. Tableau
C. Databricks Machine Learning
D. Databricks SQL
E. Delta Lake

Answer: D

Explanation:
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

NEW QUESTION # 71
Which of the following data workloads will utilize a Gold table as its source?

A. A job that enriches data by parsing its timestamps into a human-readable format.
B. A job that ingests raw data from a streaming source into the Lakehouse.
C. A job that cleans data by removing malformatted records.
D. A job that aggregates uncleaned data to create standard summary statistics.
E. A job that queries aggregated data designed to feed into a dashboard.

Answer: E

Explanation:
Option D is correct. Gold tables are refined, business-ready datasets used for analytics, dashboards, reporting, and other downstream consumption. A workload that queries aggregated data designed to feed a dashboard is therefore using Gold-layer data. Raw streaming ingestion belongs to Bronze, cleaning malformed records belongs to Silver, and aggregating uncleaned data is not the recommended Gold pattern. Official Databricks extract: the Gold layer "consists of aggregated data tailored for analytics and reporting" and is optimized for
"queries and dashboards."

NEW QUESTION # 72
Which of the following is a benefit of Databricks SQL using ANSI SQL as its standard SQL dialect?

A. It is more performant than other SQL dialects
B. It has increased customization capabilities
C. It is easy to migrate existing SQL queries to Databricks SQL
D. It allows for the use of Photon's computation optimizations
E. It is more compatible with Spark's interpreters

Answer: C

Explanation:
Databricks SQL uses ANSI SQL as its standard SQL dialect, which means it follows the SQL specifications defined by the American National Standards Institute (ANSI). This makes it easier to migrate existing SQL queries from other data warehouses or platforms that also use ANSI SQL or a similar dialect, such as PostgreSQL, Oracle, or Teradata. By using ANSI SQL, Databricks SQL avoids surprises in behavior or unfamiliar syntax that may arise from using a non-standard SQL dialect, such as Spark SQL or Hive SQL12. Moreover, Databricks SQL also adds compatibility features to support common SQL constructs that are widely used in other data warehouses, such as QUALIFY, FILTER, and user-defined functions2. Reference: ANSI compliance in Databricks Runtime, Evolution of the SQL language at Databricks: ANSI standard by default and easier migrations from data warehouses

NEW QUESTION # 73
Which of the following benefits of using Databricks SQL is provided by Data Explorer?

A. It can be used to connect to third party Bl cools.
B. It can be used to view metadata and data, as well as view/change permissions.
C. It can be used to run UPDATE queries to update any tables in a database.
D. It can be used to produce dashboards that allow data exploration.
E. It can be used to make visualizations that can be shared with stakeholders.

Answer: B

Explanation:
Data Explorer is a user interface that allows you to discover and manage data, schemas, tables, models, and permissions in Databricks SQL. You can use Data Explorer to view schema details, preview sample data, and see table and model details and properties. Administrators can view and change owners, and admins and data object owners can grant and revoke permissions1. References: Discover and manage data using Data Explorer

NEW QUESTION # 74
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 10 minutes.
A data analyst has created a dashboard based on this gold level dat
a. The project stakeholders want to see the results in the dashboard updated within 10 minutes or less of new data becoming available within the gold-level tables.
What is the ability to ensure the streamed data is included in the dashboard at the standard requested by the project stakeholders?

A. A refresh schedule with a Structured Streaming cluster
B. A refresh schedule with stakeholders included as subscribers
C. A refresh schedule with an interval of 10 minutes or less
D. A refresh schedule with an always-on SQL Warehouse (formerly known as SQL Endpoint

Answer: C

Explanation:
In this scenario, the data engineering team has configured a Structured Streaming pipeline that updates the gold-level tables every 10 minutes. To ensure that the dashboard reflects the most recent data, it is essential to set the dashboard's refresh schedule to an interval of 10 minutes or less. This synchronization ensures that stakeholders view the latest information shortly after it becomes available in the gold-level tables. Options B, C, and D do not directly address the requirement of aligning the dashboard refresh frequency with the data update interval.

NEW QUESTION # 75
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