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The GAQM Databricks-Certified-Data-Engineer-Associate Exam is a certification that validates a candidate's proficiency in using Databricks to build and maintain data pipelines. Databricks is a cloud-based data platform that allows users to easily process large amounts of data using Apache Spark. Databricks Certified Data Engineer Associate Exam certification is designed for data engineers who want to demonstrate their expertise in building scalable, reliable, and performant data pipelines.
Databricks Certified Data Engineer Associate certification is essential for individuals who want to work with big data and build scalable data pipelines. Databricks Certified Data Engineer Associate Exam certification exam covers a range of topics, including data modeling, data ingestion, data transformation, data storage, and data processing. Candidates will also be tested on their knowledge of Apache Spark, a popular open-source data processing engine, and Databricks Unified Analytics Platform.
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NEW QUESTION # 134
A data engineer that is new to using Python needs to create a Python function to add two integers together and return the sum?
Which of the following code blocks can the data engineer use to complete this task?
Answer: B
Explanation:
https://www.w3schools.com/python/python_functions.asp
https://www.geeksforgeeks.org/python-functions/
NEW QUESTION # 135
Which of the following statements regarding the relationship between Silver tables and Bronze tables is always true?
Answer: B
Explanation:
In a medallion architecture, a common data design pattern for lakehouses, data flows from Bronze to Silver to Gold layer tables, with each layer progressively improving the structure and quality of data. Bronze tables store raw data ingested from various sources, while Silver tables apply minimal transformations and cleansing to create an enterprise view of the data. Silver tables can also join and enrich data from different Bronze tables to provide a more complete and consistent view of the data. Therefore, option D is the correct answer, as Silver tables contain a more refined and cleaner view of data than Bronze tables. Option A is incorrect, as it is the opposite of the correct answer. Option B is incorrect, as Silver tables do not necessarily contain aggregates, but can also store detailed records. Option C is incorrect, as Silver tables may contain less data than Bronze tables, depending on the transformations and cleansing applied. Option E is incorrect, as Silver tables may contain more data than Bronze tables, depending on the joins and enrichments applied. Reference: What is a Medallion Architecture?, Transforming Bronze Tables in Silver Tables, What is the medallion lakehouse architecture?
NEW QUESTION # 136
In which of the following scenarios should a data engineer use the MERGE INTO command instead of the INSERT INTO command?
Answer: A
Explanation:
The MERGE INTO command is used to perform upserts, which are a combination of insertions and updates, based on a source table into a target Delta table1. The MERGE INTO command can handle scenarios where the target table cannot contain duplicate records, such as when there is a primary key or a unique constraint on the target table. The MERGE INTO command can match the source and target rows based on a merge condition and perform different actions depending on whether the rows are matched or not. For example, the MERGE INTO command can update the existing target rows with the new source values, insert the new source rows that do not exist in the target table, or delete the target rows that do not exist in the source table1.
The INSERT INTO command is used to append new rows to an existing table or create a new table from a query result2. The INSERT INTO command does not perform any updates or deletions on the existing target table rows. The INSERT INTO command can handle scenarios where the location of the data needs to be changed, such as when the data needs to be moved from one table to another, or when the data needs to be partitioned by a certain column2. The INSERT INTO command can also handle scenarios where the target table is an external table, such as when the data is stored in an external storage system like Amazon S3 or Azure Blob Storage3. The INSERT INTO command can also handle scenarios where the source table can be deleted, such as when the source table is a temporary table or a view4. The INSERT INTO command can also handle scenarios where the source is not a Delta table, such as when the source is a Parquet, CSV, JSON, or Avro file5.
:
1: MERGE INTO | Databricks on AWS
2: [INSERT INTO | Databricks on AWS]
3: [External tables | Databricks on AWS]
4: [Temporary views | Databricks on AWS]
5: [Data sources | Databricks on AWS]
NEW QUESTION # 137
A data engineer that is new to using Python needs to create a Python function to add two integers together and return the sum?
Which of the following code blocks can the data engineer use to complete this task?
Answer: A
Explanation:
https://www.w3schools.com/python/python_functions.asp
https://www.geeksforgeeks.org/python-functions/
NEW QUESTION # 138
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?
Answer: D
Explanation:
A temporary view is a relational object that is defined in the metastore and points to an existing DataFrame. It does not copy or store any physical data, but only saves the query that defines the view. The lifetime of a temporary view is tied to the SparkSession that was used to create it, so it does not persist across different sessions or applications. A temporary view is useful for accessing the same data multiple times within the same notebook or session, without incurring additional storage costs. The other options are either materialized (A, E), persistent (B, C), or not relational objects . Reference: Databricks Documentation - Temporary View, Databricks Community - How do temp views actually work?, Databricks Community - What's the difference between a Global view and a Temp view?, Big Data Programmers - Temporary View in Databricks.
NEW QUESTION # 139
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