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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Debugging and Deploying | - Deploying CI/CD
|
| Ensuring Data Security and Compliance | - Ensuring Compliance
|
| Monitoring and Alerting | - Alerting
|
| Developing Code for Data Processing using Python and SQL | - Building and Testing an ETL Pipeline with Lakeflow Declarative Pipelines, SQL, and Apache Spark
|
| Data Ingestion & Acquisition | - Design and implement data ingestion pipelines
|
| Data Sharing and Federation | - Share and federate data
|
| Cost & Performance Optimization | - Optimize cost and performance
|
| Data Modeling | - Design and optimize data models
|
| Data Transformation, Cleansing, and Quality | - Transform and validate data
|
| Data Governance | - Govern enterprise data
|
Databricks Certified Data Engineer Professional Sample Questions:
A data engineering team is setting up deployment automation. To deploy workspace assets remotely using the Databricks CLI command, they must configure it with proper authentication.
Which authentication approach will provide the highest level of security?
- A. Use a service principal and its Personal Access Token.
- B. Use a service principal with OAuth token federation.
- C. Use a service principal ID and its OAuth client secret.
- D. Use a shared user account and its OAuth client secret.
Correct Answer: B 🗳️
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An analytics team wants to run a short-term experiment in Databricks SQL on the customer transactions Delta table (about 20 billion records) created by the data engineering team. Which strategy should the data engineering team use to ensure minimal downtime and no impact on the ongoing ETL processes?
- A. Create a new table for the analytics team using a CTAS statement.
- B. Give the analytics team direct access to the production table.
- C. Deep clone the table for the analytics team.
- D. Shallow clone the table for the analytics team.
Correct Answer: D 🗳️
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A data engineer is tasked with building a nightly batch ETL pipeline that processes very large volumes of raw JSON logs from a data lake into Delta tables for reporting. The data arrives in bulk once per day, and the pipeline takes several hours to complete. Cost efficiency is important, but performance and reliability of completing the pipeline are the highest priorities. Which type of Databricks cluster should the data engineer configure?
- A. A high-concurrency cluster designed for interactive SQL workloads.
- B. An all-purpose cluster always kept running to ensure low-latency job startup times.
- C. A job cluster configured to autoscale across multiple workers during the pipeline run.
- D. A lightweight single-node cluster with low worker node count to reduce costs.
Correct Answer: C 🗳️
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A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Events are recorded once per minute per device.
Streaming DataFrame df has the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:
Choose the response that correctly fills in the blank within the code block to complete this task.
- A. window("event_time", "5 minutes").alias("time")
- B. to_interval("event_time", "5 minutes").alias("time")
- C. lag("event_time", "10 minutes").alias("time")
- D. window("event_time", "10 minutes").alias("time")
- E. "event_time"
Correct Answer: A 🗳️
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To reduce storage and compute costs, the data engineering team has been tasked with curating a series of aggregate tables leveraged by business intelligence dashboards, customer-facing applications, production machine learning models, and ad hoc analytical queries.
The data engineering team has been made aware of new requirements from a customer-facing application, which is the only downstream workload they manage entirely. As a result, an aggregate table used by numerous teams across the organization will need to have a number of fields renamed, and additional fields will also be added.
Which of the solutions addresses the situation while minimally interrupting other teams in the organization without increasing the number of tables that need to be managed?
- A. Create a new table with the required schema and new fields and use Delta Lake's deep clone functionality to sync up changes committed to one table to the corresponding table.
- B. Send all users notice that the schema for the table will be changing; include in the communication the logic necessary to revert the new table schema to match historic queries.
- C. Add a table comment warning all users that the table schema and field names will be changing on a given date; overwrite the table in place to the specifications of the customer-facing application.
- D. Configure a new table with all the requisite fields and new names and use this as the source for the customer-facing application; create a view that maintains the original data schema and table name by aliasing select fields from the new table.
- E. Replace the current table definition with a logical view defined with the query logic currently writing the aggregate table; create a new table to power the customer-facing application.
Correct Answer: D 🗳️
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