Databricks-Certified-Data-Analyst-Associate Mock Exams, Databricks-Certified-Data-Analyst-Associate Valid Study Notes

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

Topic	Details
Topic 1	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 2	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 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	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.
Topic 5	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.

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q93-Q98):

NEW QUESTION # 93
A data analyst has recently joined a new team that uses Databricks SQL, but the analyst has never used Databricks before. The analyst wants to know where in Databricks SQL they can write and execute SQL queries.
On which of the following pages can the analyst write and execute SQL queries?

A. Dashboards page
B. Queries page
C. Alerts page
D. Data page
E. SQL Editor page

Answer: E

Explanation:
The SQL Editor page is where the analyst can write and execute SQL queries in Databricks SQL. The SQL Editor page has a query pane where the analyst can type or paste SQL statements, and a results pane where the analyst can view the query results in a table or a chart. The analyst can also browse data objects, edit multiple queries, execute a single query or multiple queries, terminate a query, save a query, download a query result, and more from the SQL Editor page. References: Create a query in SQL editor

NEW QUESTION # 94
Which of the following describes the relationship between Gold tables and Silver tables?

A. Gold tables are more likely to contain valuable data than Silver tables.
B. Gold tables are more likely to contain more data than Silver tables.
C. Gold tables are more likely to contain truthful data than Silver tables.
D. Gold tables are more likely to contain a less refined view of data than Silver tables.
E. Gold tables are more likely to contain aggregations than Silver tables.

Answer: E

Explanation:
Option A is correct. Silver tables are cleaned, validated, and enriched versions of data, often still retaining detailed records. Gold tables are typically business-ready, analytics-focused, and more likely to contain aggregations, dimensional models, and reporting-ready metrics. Official Databricks extract: Silver is associated with "Data cleaning and validation," while Gold is associated with "Dimensional modeling and aggregation." Databricks also states that Gold data is often highly aggregated and tailored for analytics and reporting.

NEW QUESTION # 95
How can a data analyst determine if query results were pulled from the cache?

A. Go to the Queries tab and click on Cache Status. The status will be green if the results from the last run came from the cache.
B. Go to the Query History tab and click on the text of the query. The slideout shows if the results came from the cache.
C. Go to the SQL Warehouse (formerly SQL Endpoints) tab and click on Cache. The Cache file will show the contents of the cache.
D. Go to the Data tab and click Last Query. The details of the query will show if the results came from the cache.
E. Go to the Alerts tab and check the Cache Status alert.

Answer: B

Explanation:
Databricks SQL uses a query cache to store the results of queries that have been executed previously. This improves the performance and efficiency of repeated queries. To determine if a query result was pulled from the cache, you can go to the Query History tab in the Databricks SQL UI and click on the text of the query. A slideout will appear on the right side of the screen, showing the query details, including the cache status. If the result came from the cache, the cache status will show "Cached". If the result did not come from the cache, the cache status will show "Not cached". You can also see the cache hit ratio, which is the percentage of queries that were served from the cache. References: The answer can be verified from Databricks SQL documentation which provides information on how to use the query cache and how to check the cache status.
Reference link: Databricks SQL - Query Cache

NEW QUESTION # 96
Data engineers and data analysts are working together on a data pipeline. The data engineer is working on the raw, bronze, and silver layers of the pipeline using Python, and the data analyst is working on the gold layer of the pipeline using SQL. The raw source of the pipeline is a streaming input. They now want to migrate their pipeline to use Delta Live Tables.
Which of the following changes will need to be made to the pipeline when migrating to Delta Live Tables?

A. The pipeline will need to be written entirely in Python.
B. The pipeline can have different notebook sources in SQL and Python.
C. The pipeline will need to be written entirely in SQL.
D. The pipeline will need to use a batch source in place of a streaming source.

Answer: B

Explanation:
Option A is correct. Delta Live Tables, now documented under Lakeflow Declarative Pipelines, supports both Python and SQL source files in the same pipeline. The pipeline does not need to be rewritten entirely in one language, and it can still use streaming inputs. Official Databricks extract: "You can add Python and SQL source files to the same pipeline." The same documentation also shows examples for batch and streaming reads in Python and SQL.

NEW QUESTION # 97
Which statement about subqueries is correct?

A. Subqueries can retrieve data without requiring the creation of a table or view.
B. Subqueries can be used like other built-in functions to transform data into different data types.
C. Subqueries are not available in Databricks SQL
D. Subqueries can be used like other user-defined functions to transform data into different data types.

Answer: A

Explanation:
In Databricks SQL, a subquery is a nested query within a larger SQL query that allows for the retrieval of data without the necessity of creating a table or view. This is particularly useful for simplifying complex queries by breaking them down into more manageable parts. Subqueries can be employed in various clauses such as SELECT, FROM, and WHERE to perform operations like filtering, transforming, and aggregating data on-the-fly. This flexibility enhances query efficiency and readability without the overhead of persisting intermediate results as separate tables or views.

NEW QUESTION # 98
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