QUESTION: 13

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

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

Answer(s): E

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

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QUESTION: 14

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?

None of these
Table metadata, data summary visualizations, and owner account information
Table metadata
Data summary visualizations
Owner account information

Answer(s): C

Explanation:

Delta Lake stores table data as a series of data files in a specified location, but it also stores table metadata in a transaction log. The table metadata includes the schema, partitioning information, table properties, and other configuration details. The table metadata is stored alongside the data files and is updated atomically with every write operation. The table metadata can be accessed using the DESCRIBE DETAIL command or the DeltaTable class in Scala, Python, or Java. The table metadata can also be enriched with custom tags or user-defined commit messages using the TBLPROPERTIES or userMetadata options.

Reference:

Enrich Delta Lake tables with custom metadata

Delta Lake Table metadata - Stack Overflow

Metadata - The Internals of Delta Lake

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QUESTION: 15

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

ACID transactions
Flexible schemas
Data deletion
Scalable storage
Open-source formats

Answer(s): A

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

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QUESTION: 16

Which of the following benefits of using Databricks SQL is provided by Data Explorer?

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

Answer(s): 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.

Reference:

Discover and manage data using Data Explorer

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Free Databricks-Certified-Data-Analyst-Associate Exam Braindumps (page: 4)

QUESTION: 13

Explanation:

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QUESTION: 14

Explanation:

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QUESTION: 15

Explanation:

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QUESTION: 16

Explanation:

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