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Databricks-Certified-Data-Engineer-Associate Latest Study Notes, Databricks-Certified-Data-Engineer-Associate Exam Paper Pdf

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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) certification exam is an excellent opportunity for data professionals to validate their skills in designing, building, and maintaining data pipelines using Databricks. Databricks Certified Data Engineer Associate Exam certification exam covers a wide range of topics and is designed to test the candidate's knowledge of data engineering fundamentals, Databricks architecture, data modeling, and data processing. Passing the certification exam provides an individual with a globally recognized certification that can enhance their career prospects in the data engineering field.

Databricks Certified Data Engineer Associate certification is a valuable credential for professionals who work in data engineering. Databricks Certified Data Engineer Associate Exam certification demonstrates that the candidate has a deep understanding of data engineering concepts and has the skills and experience to work with Databricks effectively. Employers often look for candidates who hold this certification when hiring for data engineering roles, as it validates the candidate's expertise and knowledge in the field.

The Databricks Databricks-Certified-Data-Engineer-Associate Exam is a 90-minute online exam that consists of 60 multiple-choice questions. Databricks-Certified-Data-Engineer-Associate exam covers various topics such as data ingestion, data transformation, data modeling, data warehousing, and data analysis. Databricks-Certified-Data-Engineer-Associate exam is designed to validate the candidate's ability to work with Databricks in a production environment.

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q22-Q27):

NEW QUESTION # 22
In which of the following scenarios should a data engineer use the MERGE INTO command instead of the INSERT INTO command?

A. When the target table cannot contain duplicate records
B. When the source is not a Delta table
C. When the source table can be deleted
D. When the location of the data needs to be changed
E. When the target table is an external table

Answer: A

Explanation:
Explanation
With merge , you can avoid inserting the duplicate records. The dataset containing the new logs needs to be deduplicated within itself. By the SQL semantics of merge, it matches and deduplicates the new data with the existing data in the table, but if there is duplicate data within the new dataset, it is inserted.https://docs.databricks.com/en/delta/merge.html#:~:text=With%20merge%20%2C%20you%20can%20a

NEW QUESTION # 23
Which of the following is stored in the Databricks customer's cloud account?

A. Notebooks
B. Repos
C. Cluster management metadata
D. Databricks web application
E. Data

Answer: E

NEW QUESTION # 24
Which of the following describes a scenario in which a data engineer will want to use a single-node cluster?

A. When they are running automated reports to be refreshed as quickly as possible
B. When they are manually running reports with a large amount of data
C. When they are working with SQL within Databricks SQL
D. When they are concerned about the ability to automatically scale with larger data
E. When they are working interactively with a small amount of data

Answer: E

Explanation:
The scenario in which a data engineer will want to use a single-node cluster is when they are working interactively with a small amount of data. A single-node cluster is a cluster consisting of an Apache Spark driver and no Spark workers1. A single-node cluster supports Spark jobs and all Spark data sources, including Delta Lake1. A single-node cluster is helpful for single-node machine learning workloads that use Spark to load and save data, and for lightweight exploratory data analysis1. A single-node cluster can run Spark locally, spawn one executor thread per logical core in the cluster, and save all log output in the driver log1. A single-node cluster can be created by selecting the Single Node button when configuring a cluster1.
The other options are not suitable for using a single-node cluster. When running automated reports to be refreshed as quickly as possible, a data engineer will want to use a multi-node cluster that can scale up and down automatically based on the workload demand2. When working with SQL within Databricks SQL, a data engineer will want to use a SQL Endpoint that can execute SQL queries on a serverless pool or an existing cluster3. When concerned about the ability to automatically scale with larger data, a data engineer will want to use a multi-node cluster that can leverage the Databricks Lakehouse Platform and the Delta Engine to handle large-scale data processing efficiently and reliably4. When manually running reports with a large amount of data, a data engineer will want to use a multi-node cluster that can distribute the computation across multiple workers and leverage the Spark UI to monitor the performance and troubleshoot the issues.
Reference:
1: Single Node clusters | Databricks on AWS
2: Autoscaling | Databricks on AWS
3: SQL Endpoints | Databricks on AWS
4: Databricks Lakehouse Platform | Databricks on AWS
5: [Spark UI | Databricks on AWS]

NEW QUESTION # 25
A dataset has been defined using Delta Live Tables and includes an expectations clause:
CONSTRAINT valid_timestamp EXPECT (timestamp > '2020-01-01') ON VIOLATION FAIL UPDATE What is the expected behavior when a batch of data containing data that violates these constraints is processed?

A. Records that violate the expectation cause the job to fail.
B. Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log.
C. Records that violate the expectation are dropped from the target dataset and loaded into a quarantine table.
D. Records that violate the expectation are added to the target dataset and recorded as invalid in the event log.
E. Records that violate the expectation are added to the target dataset and flagged as invalid in a field added to the target dataset.

Answer: A

Explanation:
The expected behavior when a batch of data containing data that violates the expectation is processed is that the job will fail. This is because the expectation clause has the ON VIOLATION FAIL UPDATE option, which means that if any record in the batch does not meet the expectation, the entire batch will be rejected and the job will fail. This option is useful for enforcing strict data quality rules and preventing invalid data from entering the target dataset.
Option A is not correct, as the ON VIOLATION FAIL UPDATE option does not drop the records that violate the expectation, but fails the entire batch. To drop the records that violate the expectation and record them as invalid in the event log, the ON VIOLATION DROP RECORD option should be used.
Option C is not correct, as the ON VIOLATION FAIL UPDATE option does not drop the records that violate the expectation, but fails the entire batch. To drop the records that violate the expectation and load them into a quarantine table, the ON VIOLATION QUARANTINE RECORD option should be used.
Option D is not correct, as the ON VIOLATION FAIL UPDATE option does not add the records that violate the expectation, but fails the entire batch. To add the records that violate the expectation and record them as invalid in the event log, the ON VIOLATION LOG RECORD option should be used.
Option E is not correct, as the ON VIOLATION FAIL UPDATE option does not add the records that violate the expectation, but fails the entire batch. To add the records that violate the expectation and flag them as invalid in a field added to the target dataset, the ON VIOLATION FLAG RECORD option should be used.
Reference:
Delta Live Tables Expectations
[Databricks Data Engineer Professional Exam Guide]

NEW QUESTION # 26
A data engineer is attempting to drop a Spark SQL table my_table and runs the following command:
DROP TABLE IF EXISTS my_table;
After running this command, the engineer notices that the data files and metadata files have been deleted from the file system.
Which of the following describes why all of these files were deleted?

A. The table's data was smaller than 10 GB
B. The table did not have a location
C. The table's data was larger than 10 GB
D. The table was managed
E. The table was external

Answer: D

Explanation:
The reason why all of the data files and metadata files were deleted from the file system after dropping the table is that the table was managed. A managed table is a table that is created and managed by Spark SQL. It stores both the data and the metadata in the default location specified by the spark.sql.warehouse.
dir configuration property. When a managed table is dropped, both the data and the metadata are deleted from the file system.
Option B is not correct, as the size of the table's data does not affect the behavior of dropping the table.
Whether the table's data is smaller or larger than 10 GB, the data files and metadata files will be deleted if the table is managed, and will be preserved if the table is external.
Option C is not correct, for the same reason as option B.
Option D is not correct, as an external table is a table that is created and managed by the user. It stores the data in a user-specified location, and only stores the metadata in the Spark SQL catalog. When an external table is dropped, only the metadata is deleted from the catalog, but the data files are preserved in the file system.
Option E is not correct, as a table must have a location to store the data. If the location is not specified by the user, it will use the default location for managed tables. Therefore, a table without a location is a managed table, and dropping it will delete both the data and the metadata.
:
Managing Tables
[Databricks Data Engineer Professional Exam Guide]

NEW QUESTION # 27
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