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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	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 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	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.

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q73-Q78):

NEW QUESTION # 73
Delta Lake stores table data as a series of data files, but it also stores a lot of other information.
Which of the following is stored alongside data files when using Delta Lake?

A. Owner account information
B. Data summary visualizations
C. Table metadata
D. None of these
E. Table metadata, data summary visualizations, and owner account information

Answer: C

Explanation:
Delta Lake is a storage layer that enhances data lakes with features like ACID transactions, schema enforcement, and time travel. While it stores table data as Parquet files, Delta Lake also keeps a transaction log (stored in the _delta_log directory) that contains detailed table metadata.
This metadata includes:
* Table schema
* Partitioning information
* Data file paths
* Transactional operations like inserts, updates, and deletes
* Commit history and version control
This metadata is critical for supporting Delta Lake's advanced capabilities such as time travel and efficient query execution. Delta Lake does not store data summary visualizations or owner account information directly alongside the data files.
Reference: Delta Lake Table Features - Databricks Documentation

NEW QUESTION # 74
Which of the following should data analysts consider when working with personally identifiable information (PII) data?

A. Organization-specific best practices for Pll data
B. Legal requirements for the area in which the data was collected
C. All of these considerations
D. Legal requirements for the area in which the analysis is being performed
E. None of these considerations

Answer: C

Explanation:
Data analysts should consider all of these factors when working with PII data, as they may affect the data security, privacy, compliance, and quality. PII data is any information that can be used to identify a specific individual, such as name, address, phone number, email, social security number, etc. PII data may be subject to different legal and ethical obligations depending on the context and location of the data collection and analysis. For example, some countries or regions may have stricter data protection laws than others, such as the General Data Protection Regulation (GDPR) in the European Union. Data analysts should also follow the organization-specific best practices for PII data, such as encryption, anonymization, masking, access control, auditing, etc. These best practices can help prevent data breaches, unauthorized access, misuse, or loss of PII data. Reference:
How to Use Databricks to Encrypt and Protect PII Data
Automating Sensitive Data (PII/PHI) Detection
Databricks Certified Data Analyst Associate

NEW QUESTION # 75
A data analyst wants to generate insights from large, complex datasets. The analyst needs to quickly understand the meaning of various data columns, ask questions in natural language, and receive AI-driven recommendations for optimizing data queries and workflows.
Which Databricks component is primarily responsible for enabling these capabilities?

A. Databricks Assistant
B. Genie Spaces
C. Unity Catalog
D. Data Intelligence Engine

Answer: D

Explanation:
Option A is correct. The Data Intelligence Engine is the platform-level intelligence layer that understands the semantics and uniqueness of an organization's data and enables AI-assisted experiences across Databricks.
Unity Catalog provides governance and metadata management, Genie Spaces provide a natural-language interface for curated business data, and Databricks Assistant is a user-facing assistant for code/query help.
However, the question asks which component is primarily responsible for enabling these capabilities across the platform; that is the Data Intelligence Engine. Databricks describes the platform as powered by a Data Intelligence Engine that understands the uniqueness and semantics of data and helps optimize performance.
References: Databricks Data Intelligence Platform documentation and Data Analyst Associate Exam Guide.

NEW QUESTION # 76
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 dat
a. 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 gold-level tables are not appropriately clean for business reporting
C. The streaming cluster is not fault tolerant
D. The streaming data is not an appropriate data source for a dashboard
E. The required compute resources could be costly

Answer: E

Explanation:
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

NEW QUESTION # 77
What does Partner Connect do when connecting Power Bl and Tableau?

A. Creates a Personal Access Token. downloads and installs an ODBC driver, and downloads a configuration file for connection by Power Bl or Tableau to a SQL Warehouse (formerly known as a SQL Endpoint).
B. Downloads a configuration file for connection by Power Bl or Tableau to a SQL Warehouse (formerly known as a SQL Endpoint).
C. Creates a Personal Access Token for authentication into Databricks SQL and emails it to you.
D. Downloads and installs an ODBC driver.

Answer: A

Explanation:
When connecting Power BI and Tableau through Databricks Partner Connect, the system automates several steps to streamline the integration process:
Personal Access Token Creation: Partner Connect generates a Databricks personal access token, which is essential for authenticating and establishing a secure connection between Databricks and the BI tools.
ODBC Driver Installation: The appropriate ODBC driver is downloaded and installed. This driver facilitates communication between the BI tools and Databricks, ensuring compatibility and optimal performance.
Configuration File Download: A configuration file tailored for the selected BI tool (Power BI or Tableau) is provided. This file contains the necessary connection details, simplifying the setup process within the BI tool.
By automating these steps, Partner Connect ensures a seamless and efficient integration, reducing manual configuration efforts and potential errors.

NEW QUESTION # 78
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