DATABRICKS DATABRICKS-CERTIFIED-DATA-ENGINEER-ASSOCIATE VALID TEST MATERIALS & EXAM DATABRICKS-CERTIFIED-DATA-ENGINEER-ASSOCIATE SIMULATOR

Databricks Databricks-Certified-Data-Engineer-Associate Valid Test Materials & Exam Databricks-Certified-Data-Engineer-Associate Simulator

Databricks Databricks-Certified-Data-Engineer-Associate Valid Test Materials & Exam Databricks-Certified-Data-Engineer-Associate Simulator

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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Certification Exam is a highly sought-after certification for professionals who want to validate their skills and knowledge in data engineering. Databricks-Certified-Data-Engineer-Associate Exam is designed to test the candidate's ability to work with data engineering tools and technologies, including Apache Spark, Apache Hadoop, and Databricks.

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q27-Q32):

NEW QUESTION # 27
Which of the following describes a benefit of creating an external table from Parquet rather than CSV when using a CREATE TABLE AS SELECT statement?

  • A. Parquet files can be partitioned
  • B. CREATE TABLE AS SELECT statements cannot be used on files
  • C. Parquet files have the ability to be optimized
  • D. Parquet files have a well-defined schema
  • E. Parquet files will become Delta tables

Answer: D

Explanation:
Explanation
https://www.databricks.com/glossary/what-is-parquet#:~:text=Columnar%20storage%20like%20Apache%20Par Columnar storage like Apache Parquet is designed to bring efficiency compared to row-based files like CSV.
When querying, columnar storage you can skip over the non-relevant data very quickly. As a result, aggregation queries are less time-consuming compared to row-oriented databases.


NEW QUESTION # 28
Which of the following describes the type of workloads that are always compatible with Auto Loader?

  • A. Streaming workloads
  • B. Machine learning workloads
  • C. Serverless workloads
  • D. Batch workloads
  • E. Dashboard workloads

Answer: A

Explanation:
Explanation
Auto Loader is a feature of Databricks that simplifies and automates the process of loading streaming data into Delta Lake tables. Auto Loader can detect new and updated files in cloud storage and efficiently load them as micro-batches or as a continuous stream. Auto Loader is always compatible with streaming workloads, as it is designed to handle streaming sources such as Amazon S3, Azure Data Lake Storage Gen2, and Azure Blob Storage. The other types of workloads may or may not be compatible with Auto Loader, depending on the data source and the use case. References: The information can be referenced from Databricks documentation on Auto Loader: Auto Loader.
https://community.databricks.com/t5/data-engineering/practice-exams-for-databricks-certified-data-engineer/td-p


NEW QUESTION # 29
A new data engineering team team. has been assigned to an ELT project. The new data engineering team will need full privileges on the database customers to fully manage the project.
Which of the following commands can be used to grant full permissions on the database to the new data engineering team?

  • A. GRANT SELECT CREATE MODIFY USAGE PRIVILEGES ON DATABASE customers TO team;
  • B. GRANT USAGE ON DATABASE customers TO team;
  • C. GRANT ALL PRIVILEGES ON DATABASE team TO customers;
  • D. GRANT SELECT PRIVILEGES ON DATABASE customers TO teams;
  • E. GRANT ALL PRIVILEGES ON DATABASE customers TO team;

Answer: E


NEW QUESTION # 30
A data engineer is using the following code block as part of a batch ingestion pipeline to read from a composable table:

Which of the following changes needs to be made so this code block will work when the transactions table is a stream source?

  • A. Replace spark.read with spark.readStream
  • B. Replace predict with a stream-friendly prediction function
  • C. Replace "transactions" with the path to the location of the Delta table
  • D. Replace format("delta") with format("stream")
  • E. Replace schema(schema) with option ("maxFilesPerTrigger", 1)

Answer: A

Explanation:
To read from a stream source, the data engineer needs to use the spark.readStream method instead of the spark.read method. The spark.readStream method returns a DataStreamReader object that can be used to specify the details of the input source, such as the format, the schema, the path, and the options. The spark.read method is only suitable for batch processing, not streaming processing. The other changes are not necessary or correct for reading from a stream source. References: Structured Streaming Programming Guide, Read a stream, Databricks Data Sources


NEW QUESTION # 31
A data engineer wants to create a relational object by pulling data from two tables. The relational object does not need to be used by other data engineers in other sessions. In order to save on storage costs, the data engineer wants to avoid copying and storing physical data.
Which of the following relational objects should the data engineer create?

  • A. Delta Table
  • B. View
  • C. Database
  • D. Spark SQL Table
  • E. Temporary view

Answer: E

Explanation:
Explanation
Temp view : session based Create temp view view_name as query All these are termed as session ended:
Opening a new notebook Detaching and reattaching a cluster Installing a python package Restarting a cluster


NEW QUESTION # 32
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