VALID DATABRICKS-CERTIFIED-DATA-ENGINEER-ASSOCIATE TEST CRAM & DATABRICKS-CERTIFIED-DATA-ENGINEER-ASSOCIATE NEW TEST BOOTCAMP

Valid Databricks-Certified-Data-Engineer-Associate Test Cram & Databricks-Certified-Data-Engineer-Associate New Test Bootcamp

Valid Databricks-Certified-Data-Engineer-Associate Test Cram & Databricks-Certified-Data-Engineer-Associate New Test Bootcamp

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Top Valid Databricks-Certified-Data-Engineer-Associate Test Cram | Reliable Databricks-Certified-Data-Engineer-Associate New Test Bootcamp: Databricks Certified Data Engineer Associate Exam

Everything needs a right way. The good method can bring the result with half the effort, the same different exam also needs the good test method. Our Databricks-Certified-Data-Engineer-Associate study materials in every year are summarized based on the test purpose, every answer is a template, there are subjective and objective exams of two parts, we have in the corresponding modules for different topic of deliberate practice. To this end, our Databricks-Certified-Data-Engineer-Associate Study Materials in the qualification exam summarize some problem- solving skills, and induce some generic templates.

The Databricks Databricks-Certified-Data-Engineer-Associate exam has 50 multiple-choice questions, and the candidate has 90 minutes to complete it. It is an online exam that can be taken at any time and from anywhere in the world. The questions in the exam are designed to test the candidate's ability to handle real-world data engineering challenges. Databricks-Certified-Data-Engineer-Associate Exam also tests the candidate's understanding of the Databricks platform and how it can be used to solve data engineering problems.

Databricks Certified Data Engineer Associate Exam Sample Questions (Q100-Q105):

NEW QUESTION # 100
A data engineer needs to create a table in Databricks using data from their organization's existing SQLite database.
They run the following command:

Which of the following lines of code fills in the above blank to successfully complete the task?

  • A. org.apache.spark.sql.sqlite
  • B. autoloader
  • C. org.apache.spark.sql.jdbc
  • D. sqlite
  • E. DELTA

Answer: A


NEW QUESTION # 101
A data architect has determined that a table of the following format is necessary:
Which of the following code blocks uses SQL DDL commands to create an empty Delta table in the above format regardless of whether a table already exists with this name?

  • A. Option C
  • B. Option B
  • C. Option A
  • D. Option D
  • E. Option E

Answer: E


NEW QUESTION # 102
A data architect has determined that a table of the following format is necessary:

Which of the following code blocks uses SQL DDL commands to create an empty Delta table in the above format regardless of whether a table already exists with this name?

  • A. Option C
  • B. Option B
  • C. Option A
  • D. Option D
  • E. Option E

Answer: E

Explanation:
References: Create a table using SQL | Databricks on AWS, Create a table using SQL - Azure Databricks, Delta Lake Quickstart - Azure Databricks


NEW QUESTION # 103
A data engineer has developed a data pipeline to ingest data from a JSON source using Auto Loader, but the engineer has not provided any type inference or schema hints in their pipeline. Upon reviewing the data, the data engineer has noticed that all of the columns in the target table are of the string type despite some of the fields only including float or boolean values.
Which of the following describes why Auto Loader inferred all of the columns to be of the string type?

  • A. There was a type mismatch between the specific schema and the inferred schema
  • B. Auto Loader only works with string data
  • C. All of the fields had at least one null value
  • D. JSON data is a text-based format
  • E. Auto Loader cannot infer the schema of ingested data

Answer: D

Explanation:
JSON data is a text-based format that represents data as a collection of name-value pairs. By default, when Auto Loader infers the schema of JSON data, it treats all columns as strings. This is because JSON data can have varying data types for the same column across different files or records, and Auto Loader does not attempt to reconcile these differences. For example, a column named "age" may have integer values in some files, but string values in others. To avoid data loss or errors, Auto Loader infers the column as a string type.
However, Auto Loader also provides an option to infer more precise column types based on the sample data.
This option is called cloudFiles.inferColumnTypes and it can be set to true or false. When set to true, Auto Loader tries to infer the exact data types of the columns, such as integers, floats, booleans, or nested structures.
When set to false, Auto Loader infers all columns as strings. The default value of this option is false. References: Configure schema inference and evolution in Auto Loader, Schema inference with auto loader (non-DLT and DLT), Using and Abusing Auto Loader's Inferred Schema, Explicit path to data or a defined schema required for Auto loader.


NEW QUESTION # 104
Which of the following is a benefit of the Databricks Lakehouse Platform embracing open source technologies?

  • A. Simplified governance
  • B. Ability to scale workloads
  • C. Avoiding vendor lock-in
  • D. Ability to scale storage
  • E. Cloud-specific integrations

Answer: C

Explanation:
One of the benefits of the Databricks Lakehouse Platform embracing open source technologies is that it avoids vendor lock-in. This means that customers can use the same open source tools and frameworks across different cloud providers, and migrate their data and workloads without being tied to a specific vendor. The Databricks Lakehouse Platform is built on open source projects such as Apache Spark™, Delta Lake, MLflow, and Redash, which are widely used and trusted by millions of developers. By supporting these open source technologies, the DatabricksLakehouse Platform enables customers to leverage the innovation and community of the open source ecosystem, and avoid the risk of being locked into proprietary or closed solutions. The other options are either not related to open source technologies (A, B, C, D), or not benefits of the Databricks Lakehouse Platform (A, B). References: Databricks Documentation - Built on open source, Databricks Documentation - What is the Lakehouse Platform?, Databricks Blog - Introducing the Databricks Lakehouse Platform.


NEW QUESTION # 105
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