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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) certification exam is a credential that certifies individuals in their ability to design, develop, and manage data pipelines using Databricks. It is a globally recognized certification that validates the skills and knowledge required to work with big data processing and analytics using Databricks.
Reference: https://www.databricks.com/learn/certification/data-engineer-associate
The GAQM Databricks-Certified-Data-Engineer-Associate exam consists of 60 multiple-choice questions that cover a range of topics related to data engineering with Databricks. Databricks-Certified-Data-Engineer-Associate exam is designed to test the candidate's knowledge and skills in areas such as data ingestion, data transformation, data modeling, data integration, and data processing. Candidates who pass the exam will receive a certificate that validates their expertise in data engineering with Databricks and demonstrates their ability to work effectively with the platform. Databricks Certified Data Engineer Associate Exam certification is valuable for professionals working in data engineering roles, as it helps them to showcase their skills and expertise to potential employers and clients.
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The Databricks Databricks-Certified-Data-Engineer-Associate exam is conducted online and consists of 80 multiple-choice questions. The duration of the exam is 2 hours, and candidates must achieve a minimum score of 60% to pass. Databricks-Certified-Data-Engineer-Associate exam fee is $300, and candidates can schedule their exam at any time through the GAQM website. Databricks Certified Data Engineer Associate Exam certification is valid for three years and can be renewed by passing the latest version of the exam.
The GAQM Databricks-Certified-Data-Engineer-Associate certification is an excellent way for professionals to demonstrate their competence and expertise in working with Databricks. Databricks Certified Data Engineer Associate Exam certification validates your knowledge and skills in designing, building, and maintaining data pipelines with Databricks, which is an essential skill for any organization that is looking to leverage big data for business growth.
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Databricks Databricks-Certified-Data-Engineer-Associate Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Databricks Lakehouse Platform Fundamentals | - Clusters, notebooks, and basic Databricks environment usage - Workspace, architecture, and core platform concepts |
| Topic 2: Productionizing Data Pipelines | - Databricks Workflows / Jobs orchestration - Pipeline deployment and operationalization - Scheduling and monitoring jobs |
| Topic 3: Data Governance and Quality | - Unity Catalog basics - Data quality concepts and management - Data access control and governance |
| Topic 4: Data Ingestion and ELT Development | - ETL patterns and transformations - Data ingestion using Spark SQL and PySpark - Handling structured and semi-structured data |
| Topic 5: Data Processing and Transformations | - PySpark DataFrame transformations - Apache Spark SQL operations (joins, aggregations, filtering) - User-defined functions (UDFs) - Delta Lake fundamentals (tables, transactions, optimization) |
Databricks Certified Data Engineer Associate Sample Questions:
A data engineer is developing a small proof of concept in a notebook. When running the entire notebook, the Cluster usage spikes. The data engineer wants to keep the development requirements and get real-time results. Which Cluster meets these requirements?
- A. All-Purpose Cluster with a large fixed memory size
- B. Job Cluster with Photon enabled and autoscaling
- C. All Purpose Cluster with autoscaling
- D. Job Cluster with autoscaling enabled
Correct Answer: C 🗳️
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A data engineer wants to schedule their Databricks SQL dashboard to refresh every hour, but they only want the associated SQL endpoint to be running when it is necessary. The dashboard has multiple queries on multiple datasets associated with it. The data that feeds the dashboard is automatically processed using a Databricks Job.
Which of the following approaches can the data engineer use to minimize the total running time of the SQL endpoint used in the refresh schedule of their dashboard?
- A. They can set up the dashboard's SQL endpoint to be serverless.
- B. They can ensure the dashboard's SQL endpoint matches each of the queries' SQL endpoints.
- C. They can ensure the dashboard's SQL endpoint is not one of the included query's SQL endpoint.
- D. They can turn on the Auto Stop feature for the SQL endpoint.
- E. They can reduce the cluster size of the SQL endpoint.
Correct Answer: D 🗳️
A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then perform a streaming write into a new table.
The code block used by the data engineer is below:
If the data engineer only wants the query to execute a micro-batch to process data every 5 seconds, which of the following lines of code should the data engineer use to fill in the blank?
- A. trigger(processingTime="5 seconds")
- B. trigger(once="5 seconds")
- C. trigger("5 seconds")
- D. trigger()
- E. trigger(continuous="5 seconds")
Correct Answer: A 🗳️
A data engineer must create a copy of a production Delta table for a one-time schema experiment. The copy must be fully independent so that later VACUUM operations on the source cannot invalidate it.
Which command should be used?
- A. CREATE TABLE test_orders SHALLOW CLONE prod_orders
- B. CREATE VIEW test_orders AS SELECT * FROM prod_orders
- C. CREATE TABLE test_orders LIKE prod_orders
- D. CREATE TABLE test_orders DEEP CLONE prod_orders
Correct Answer: D 🗳️
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A data engineer is working in a Databricks notebook to design and manage a batch ETL pipeline.
The engineer is writing SQL and Python code to clean data, transform it, and join large datasets from different sources. The engineer wants to organize these steps into a structured process that can be run regularly and scheduled as part of a data pipeline. Which Databricks notebook feature is applicable in the use case?
- A. Real-time streaming support
- B. Collaborative editing
- C. Notebook version control
- D. Task workflows and job scheduling
Correct Answer: D 🗳️
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