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Databricks - Data Engineer Associate Practice Exam 2 questions with correct answers 2024.
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Databricks - Data Engineer Associate Practice Exam 2 questions with correct answers 2024.
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Databricks - Data Engineer AssociatePractice Exam 2 questions with correct
answers 2024
What is the best way to describe a data lakehouse compared to a data warehouse?
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a. A data lakehouse provides a relational system of data management
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b. A data lakehouse captures snapshots of data for version control purposes.
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c. A data lakehouse couples storage and compute for complete control.
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d. A data lakehouse utilizes proprietary storage formats for data.
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e. A data lakehouse enables both batch and streaming analytics. - ANSWER: ➡ e. A data
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lakehouse enables both batch and streaming analytics.
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Explanation
Answer is A data lakehouse enables both batch and streaming analytics.
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A lakehouse has the following key features:
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Transaction support: In an enterprise lakehouse many data pipelines will often be reading
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and writing data concurrently. Support for ACID transactions ensures consistency as
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multiple parties concurrently read or write data, typically using SQL.
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Schema enforcement and governance: The Lakehouse should have a way to support schema
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enforcement and evolution, supporting DW schema architectures such as star/snowflake-
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schemas. The system should be able to reason about data integrity, and it should have robust
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governance and auditing mechanisms.
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BI support: Lakehouses enable using BI tools directly on the source data. This reduces
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staleness and improves recency, reduces latency, and lowers the cost of having to
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operationalize two copies of the data in both a data lake and a warehouse.
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,Storage is decoupled from compute: In practice this means storage and compute use separate
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clusters, thus these systems are able to scale to many more concurrent users and larger data
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sizes. Some modern data warehouses also have this property.
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Openness: The storage formats they use are open and standardized, such as Parquet, and
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they provide an API so a variety of tools and engines, including machine learning and
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Python/R libraries, can efficiently access the data directly.
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Support for diverse data types ranging from unstructured to structured data: The lakehouse
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can be used to store, refine, analyze, and access data types needed for many new data
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applications, including images, video, audio, semi-structured data, and text.
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Support for diverse workloads: including data science, machine learning, and SQL and
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analytics. Multiple tools might be needed to s
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You are designing an analytical to store structured data from your e-commerce platform and
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unstructured data from website traffic and app store, how would you approach where you
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store this data?
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a. Use traditional data warehouse for structured data and use data lakehouse for
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unstructured data.
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b. Data lakehouse can only store unstructured data but cannot enforce a schema
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c. Data lakehouse can store structured and unstructured data and can enforce schema
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d. Traditional data warehouses are good for storing structured data and enforcing schema -
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ANSWER: ➡ c. Data lakehouse can store structured and unstructured data and can enforce
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schema
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explanation
The answer is, Data lakehouse can store structured and unstructured data and can enforce
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schema
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,What Is a Lakehouse? - The Databricks Blog
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You are currently working on a production job failure with a job set up in job clusters due to a
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data issue, what cluster do you need to start to investigate and analyze the data?
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a. A Job cluster can be used to analyze the problem
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b. All-purpose cluster/ interactive cluster is the recommended way to run commands and
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view the data.
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c. Existing job cluster can be used to investigate the issue
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d. Databricks SQL Endpoint can be used to investigate the issue - ANSWER: ➡ b. All-purpose
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cluster/ interactive cluster is the recommended way to run commands and view the data.
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explanation
Answer is All-purpose cluster/ interactive cluster is the recommended way to run commands
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and view the data.
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A job cluster can not provide a way for a user to interact with a notebook once the job is
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submitted, but an Interactive cluster allows to you display data, view visualizations write or
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edit quries, which makes it a perfect fit to investigate and analyze the data.
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Which of the following describes how Databricks Repos can help facilitate CI/CD workflows
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on the Databricks Lakehouse Platform?
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a. Databricks Repos can facilitate the pull request, review, and approval process before
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merging branches
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b. Databricks Repos can merge changes from a secondary Git branch into a main Git branch
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, c. Databricks Repos can be used to design, develop, and trigger Git automation pipelines
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d. Databricks Repos can store the single-source-of-truth Git repository
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(Incorrect)
e. Databricks Repos can commit or push code changes to trigger a CI/CD process - ANSWER:
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➡ e. Databricks Repos can commit or push code changes to trigger a CI/CD process
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explanation
Answer is Databricks Repos can commit or push code changes to trigger a CI/CD process
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See below diagram to understand the role Databricks Repos and Git provider plays when
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building a CI/CD workdlow.
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All the steps highlighted in yellow can be done Databricks Repo, all the steps highlighted in
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Gray are done in a git provider like Github or Azure Devops.
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You noticed that colleague is manually copying the notebook with _bkp to store the previous
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versions, which of the following feature would you recommend instead.
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a. Databricks notebooks support change tracking and versioning
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b. Databricks notebooks should be copied to a local machine and setup source control locally
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to version the notebooks
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c. Databricks notebooks can be exported into dbc archive files and stored in data lake
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d. Databricks notebook can be exported as HTML and imported at a later time - ANSWER: ➡
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a. Databricks notebooks support change tracking and versioning
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Explanation
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