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

TopicDetails
Topic 1
  • 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 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
  • 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 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
  • 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 (Q53-Q58):

NEW QUESTION # 53
Which of the following is an advantage of using a Delta Lake-based data lakehouse over common data lake solutions?

Answer: D

Explanation:
A Delta Lake-based data lakehouse is a data platform architecture that combines the scalability and flexibility of a data lake with the reliability and performance of a data warehouse. One of the key advantages of using a Delta Lake-based data lakehouse over common data lake solutions is that it supports ACID transactions, which ensure data integrity and consistency. ACID transactions enable concurrent reads and writes, schema enforcement and evolution, data versioning and rollback, and data quality checks. These features are not available in traditional data lakes, which rely on file-based storage systems that do not support transactions. Reference:
Delta Lake: Lakehouse, warehouse, advantages | Definition
Synapse - Data Lake vs. Delta Lake vs. Data Lakehouse
Data Lake vs. Delta Lake - A Detailed Comparison
Building a Data Lakehouse with Delta Lake Architecture: A Comprehensive Guide


NEW QUESTION # 54
How can a data analyst determine if query results were pulled from the cache?

Answer: C

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. Reference: 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 # 55
Data professionals with varying titles use the Databricks SQL service as the primary touchpoint with the Databricks Lakehouse Platform. However, some users will use other services like Databricks Machine Learning or Databricks Data Science and Engineering.
Which of the following roles uses Databricks SQL as a secondary service while primarily using one of the other services?

Answer: C

Explanation:
Data engineers are primarily responsible for building, managing, and optimizing data pipelines and architectures. They use Databricks Data Science and Engineering service to perform tasks such as data ingestion, transformation, quality, and governance. Data engineers may use Databricks SQL as a secondary service to query, analyze, and visualize data from the lakehouse, but this is not their main focus. Reference: Databricks SQL overview, Databricks Data Science and Engineering overview, Data engineering with Databricks


NEW QUESTION # 56
A data analyst has come across a column in a table that contains personally identifiable information (PII). The data analyst should not have access to this type of PII data.
How should the data analyst proceed?

Answer: D

Explanation:
Option C is correct. If an analyst encounters PII that they should not access, they should stop using that data and escalate through the appropriate organizational process. They should not delete the table, drop the column, or silently continue. Databricks security and privacy guidance emphasizes least-privilege access, data classification by sensitivity, access control, and meeting compliance/privacy requirements, including redaction or obfuscation of PII when required. Therefore, notifying a supervisor or data owner is the correct professional and governance-aligned response. References: Databricks security, compliance, and privacy documentation.


NEW QUESTION # 57
A data analyst has opened the SQL Editor page and written a new SQL statement. The data analyst now wants to save that statement to easily refer back to it later and add it to a dashboard. The results of the SQL statement must be able to be displayed as a counter, table, or data visualization.
Which approach should the data analyst use to accomplish this task?

Answer: D

Explanation:
The correct answer is C because Databricks SQL Editor is used to create and save SQL queries. A saved query can later be opened, reused, shared, visualized, and added to a dashboard. A dashboard is not the saved SQL statement itself; it is a reporting object that can contain query results and visualizations. Query History stores past executions, but it is not the correct object for saving a reusable SQL statement.
Official documentation extract used: Databricks states that the SQL editor can be used to "author queries" and
"create visualizations." It also states that the Save button "saves your query," and query results can be added to a dashboard.


NEW QUESTION # 58
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