Databricks Certified Data Engineer Associate Certification holds great value and can greatly enhance your career prospects. To assist you in your journey towards achieving the Data Engineer Associate certification, the Passcert team has worked diligently to crack the Databricks Certified Data Engineer Associate Exam Dumps which are designed to encompass all the necessary exam content, ensuring that you have all the resources at your disposal to confidently pass your exam with ease. With the help of these Databricks Certified Data Engineer Associate Exam Dumps, you can effectively prepare yourself and increase your chances of success in the certification exam.

A data engineer certification provides evidence that you possess the necessary skills for your job. Even if you are already employed in data engineering, it serves as validation of your knowledge and opens up new opportunities. Databricks allows you to prepare for both the data engineer associate and professional certifications, allowing you to enhance your current skills and acquire new ones as technology progresses. While the demand for data engineers is high, having credentials that distinguish you is still beneficial. Obtaining a data engineer certification demonstrates your dedication and effort in acquiring new skills, and it is also a rewarding achievement. Whether you are seeking a beginner-level data engineer certification or aiming for a professional data engineer certification, Databricks is there to assist you in learning the relevant skills and successfully passing the exam.

 

- Data Engineer Associate certification

- Data Engineer Professional certification

- Databricks Certified Associate Developer for Apache Spark

 

The Databricks Certified Data Engineer Associate certification exam assesses an individual’s ability to use the Databricks Lakehouse Platform to complete introductory data engineering tasks. This includes an understanding of the Lakehouse Platform and its workspace, its architecture, and its capabilities. It also assesses the ability to perform multi-hop architecture ETL tasks using Apache Spark? SQL and Python in both batch and incrementally processed paradigms. Finally, the exam assesses the tester’s ability to put basic ETL pipelines and Databricks SQL queries and dashboards into production while maintaining entity permissions. Individuals who pass this certification exam can be expected to complete basic data engineering tasks using Databricks and its associated tools.

 

Type: Proctored certification

Total number of questions: 45

Time limit: 90 minutes

Registration fee: $200 (Databricks partners get 50% off the registration fee)

Question types: Multiple choice

Languages: English

Delivery method: Online proctored

Prerequisites: None, but related training highly recommended

Recommended experience: 6+ months of hands-on experience performing the data engineering tasks outlined in the exam guide 

Validity period: 2 years

Recertification: Recertification is required to maintain your certification status. Databricks Certifications are valid for two years from issue date. 

 

● Describe the relationship between the data lakehouse and the data warehouse.

● Identify the improvement in data quality in the data lakehouse over the data lake.

● Compare and contrast silver and gold tables, which workloads will use a bronze table as a source, which workloads will use a gold table as a source.

● Identify elements of the Databricks Platform Architecture, such as what is located in the data plane versus the control plane and what resides in the customer’s cloud account

● Differentiate between all-purpose clusters and jobs clusters.

● Identify how cluster software is versioned using the Databricks Runtime.

● Identify how clusters can be filtered to view those that are accessible by the user.

● Describe how clusters are terminated and the impact of terminating a cluster.

● Identify a scenario in which restarting the cluster will be useful.

● Describe how to use multiple languages within the same notebook.

● Identify how to run one notebook from within another notebook.

● Identify how notebooks can be shared with others.

● Describe how Databricks Repos enables CI/CD workflows in Databricks.

● Identify Git operations available via Databricks Repos.

● Identify limitations in Databricks Notebooks version control functionality relative to Repos.

 

● Extract data from a single file and from a directory of files

● Identify the prefix included after the FROM keyword as the data type.

● Create a view, a temporary view, and a CTE as a reference to a file

● Identify that tables from external sources are not Delta Lake tables.

● Create a table from a JDBC connection and from an external CSV file

● Identify how the count_if function can be used

● Identify how the count where x is null can be used

● Identify how the count(row) skips NULL values.

● Deduplicate rows from an existing Delta Lake table.

● Create a new table from an existing table while removing duplicate rows.

● Deduplicate a row based on specific columns.

● Validate that the primary key is unique across all rows.

● Validate that a field is associated with just one unique value in another field.

● Validate that a value is not present in a specific field.

● Cast a column to a timestamp.

● Extract calendar data from a timestamp.

● Extract a specific pattern from an existing string column.

● Utilize the dot syntax to extract nested data fields.

● Identify the benefits of using array functions.

● Parse JSON strings into structs.

● Identify which result will be returned based on a join query.

● Identify a scenario to use the explode function versus the flatten function

● Identify the PIVOT clause as a way to convert data from wide format to a long format.

● Define a SQL UDF.

● Identify the location of a function.

● Describe the security model for sharing SQL UDFs.

● Use CASE/WHEN in SQL code.

● Leverage CASE/WHEN for custom control flow

 

● Identify where Delta Lake provides ACID transactions.

● Identify the benefits of ACID transactions.

● Identify whether a transaction is ACID-compliant.

● Compare and contrast data and metadata.

● Compare and contrast managed and external tables.

● Identify a scenario to use an external table.

● Create a managed table.

● Identify the location of a table.

● Inspect the directory structure of Delta Lake files.

● Identify who has written previous versions of a table.

● Review a history of table transactions.

● Roll back a table to a previous version.

● Identify that a table can be rolled back to a previous version.

● Query a specific version of a table.

● Identify why Zordering is beneficial to Delta Lake tables.

● Identify how vacuum commits deletes.

● Identify the kind of files Optimize compacts.

● Identify CTAS as a solution.

● Create a generated column.

● Add a table comment.

● Use CREATE OR REPLACE TABLE and INSERT OVERWRITE

● Compare and contrast CREATE OR REPLACE TABLE and INSERT OVERWRITE

● Identify a scenario in which MERGE should be used.

● Identify MERGE as a command to deduplicate data upon writing.

● Describe the benefits of the MERGE command.

● Identify why a COPY INTO statement is not duplicating data in the target table.

● Identify a scenario in which COPY INTO should be used.

● Use COPY INTO to insert data

 

● Identify the components necessary to create a new DLT pipeline.

● Identify the purpose of the target and of the notebook libraries in creating a pipeline.

● Compare and contrast triggered and continuous pipelines in terms of cost and latency

● Identify which source location is utilizing Auto Loader.

● Identify a scenario in which Auto Loader is beneficial.

● Identify why Auto Loader has inferred all data to be STRING from a JSON source

● Identify the default behavior of a constraint violation

● Identify the impact of ON VIOLATION DROP ROW and ON VIOLATION FAIL UPDATEfor a constraint violation

● Explain change data capture and the behavior of APPLY CHANGES INTO

● Query the events log to get metrics, perform audit loggin, examine lineage.

● Troubleshoot DLT syntax: Identify which notebook in a DLT pipeline produced an error, identify the need for LIVE in create statement, identify the need for STREAM in from clause.

 

● Identify benefits of using multiple tasks in Jobs.

● Set up a predecessor task in Jobs.

● Identify a scenario in which a predecessor task should be set up.

● Review a task's execution history.

● Identify CRON as a scheduling opportunity.

● Debug a failed task.

● Set up a retry policy in case of failure.

● Create an alert in the case of a failed task.

● Identify that an alert can be sent via email.

 

● Identify one of the four areas of data governance.

● Compare and contrast metastores and catalogs.

● Identify Unity Catalog securables.

● Define a service principal.

● Identify the cluster security modes compatible with Unity Catalog.

● Create a UC-enabled all-purpose cluster.

● Create a DBSQL warehouse.

● Identify how to query a three-layer namespace.

● Implement data object access control

● Identify colocating metastores with a workspace as best practice

● Identify using service principals for connections as best practice.

● Identify the segregation of business units across catalog as best practice.

 

1. A data engineer needs to use a Delta table as part of a data pipeline, but they do not know if they have the appropriate permissions.

In which of the following locations can the data engineer review their permissions on the table?

A.Databricks Filesystem

B.Jobs

C.Dashboards

D.Repos

E.Data Explorer

Answer: E

 

2. A data engineer is designing a data pipeline. The source system generates files in a shared directory that is also used by other processes. As a result, the files should be kept as is and will accumulate in the directory. The data engineer needs to identify which files are new since the previous run in the pipeline, and set up the pipeline to only ingest those new files with each run.

Which of the following tools can the data engineer use to solve this problem?

A.Unity Catalog

B.Delta Lake

C.Databricks SQL

D.Data Explorer

E.Auto Loader

Answer: E

 

3. Which of the following benefits is provided by the array functions from Spark SQL?

A.An ability to work with data in a variety of types at once

B.An ability to work with data within certain partitions and windows

C.An ability to work with time-related data in specified intervals

D.An ability to work with complex, nested data ingested from JSON files

E.An ability to work with an array of tables for procedural automation

Answer: D

 

4. 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.Spark SQL Table

B.View

C.Database

D.Temporary view

E.Delta Table

Answer: D

 

5. A data engineer needs access to a table new_table, but they do not have the correct permissions. They can ask the table owner for permission, but they do not know who the table owner is.

Which of the following approaches can be used to identify the owner of new_table?

A.Review the Permissions tab in the table's page in Data Explorer

B.All of these options can be used to identify the owner of the table

C.Review the Owner field in the table's page in Data Explorer

D.Review the Owner field in the table's page in the cloud storage solution

E.There is no way to identify the owner of the table

Answer: C

 

6. A new data engineering team team has been assigned to an ELT project. The new data engineering team will need full privileges on the table sales 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 ALL PRIVILEGES ON TABLE sales TO team;

B.GRANT SELECT CREATE MODIFY ON TABLE sales TO team;

C.GRANT SELECT ON TABLE sales TO team;

D.GRANT USAGE ON TABLE sales TO team;

E.GRANT ALL PRIVILEGES ON TABLE team TO sales;

Answer: A

 

7. A data organization leader is upset about the data analysis team’s reports being different from the data engineering team’s reports. The leader believes the siloed nature of their organization’s data engineering and data analysis architectures is to blame.

Which of the following describes how a data lakehouse could alleviate this issue?

A.Both teams would autoscale their work as data size evolves

B.Both teams would use the same source of truth for their work

C.Both teams would reorganize to report to the same department

D.Both teams would be able to collaborate on projects in real-time

E.Both teams would respond more quickly to ad-hoc requests

Answer: B

 

8. A data engineer has been using a Databricks SQL dashboard to monitor the cleanliness of the input data to a data analytics dashboard for a retail use case. The job has a Databricks SQL query that returns the number of store-level records where sales is equal to zero. The data engineer wants their entire team to be notified via a messaging webhook whenever this value is greater than 0.

Which of the following approaches can the data engineer use to notify their entire team via a messaging webhook whenever the number of stores with $0 in sales is greater than zero?

A.They can set up an Alert with a custom template.

B.They can set up an Alert with a new email alert destination.

C.They can set up an Alert with one-time notifications.

D.They can set up an Alert with a new webhook alert destination.

E.They can set up an Alert without notifications.

Answer: D

 

9. A data engineer has three tables in a Delta Live Tables (DLT) pipeline. They have configured the pipeline to drop invalid records at each table. They notice that some data is being dropped due to quality concerns at some point in the DLT pipeline. They would like to determine at which table in their pipeline the data is being dropped.

Which of the following approaches can the data engineer take to identify the table that is dropping the records?

A.They can set up separate expectations for each table when developing their DLT pipeline.

B.They cannot determine which table is dropping the records.

C.They can set up DLT to notify them via email when records are dropped.

D.They can navigate to the DLT pipeline page, click on each table, and view the data quality statistics.

E.They can navigate to the DLT pipeline page, click on the “Error” button, and review the present errors.

Answer: D

 

10. Which of the following tools is used by Auto Loader process data incrementally?

A.Checkpointing

B.Spark Structured Streaming

C.Data Explorer

D.Unity Catalog

E.Databricks SQL

Answer: B