Databricks Certified-Data-Engineer-Professional : Databricks Certified Data Engineer Professional

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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:

Databricks Certified Data Engineer Professional Sample Questions:

1. 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 lightweight single-node cluster with low worker node count to reduce costs.
B) A high-concurrency cluster designed for interactive SQL workloads.
C) An all-purpose cluster always kept running to ensure low-latency job startup times.
D) A job cluster configured to autoscale across multiple workers during the pipeline run.

2. A senior data engineer is planning large-scale data workflows. The current task is to identify the considerations that form a foundation for creating scalable data models that are essential for effective management of large datasets. The data engineering team has identified the core capabilities as part of a scalable data model to build a modern data platform and provided their reasoning for considering Delta Lake for review. The senior data engineer is responsible for identifying the recommendations that are not valid. Which key features can be ignored while evaluating Delta Lake?

A) Delta Lake works with various data formats (e.g., Parquet, JSON, CSV) and integrates well with Spark and Databricks tools.
B) Delta Lake optimizes metadata handling, efficiently managing billions of files and facilitating scalability to petabyte-scale datasets.
C) Delta Lake's capability to process data in both batch and streaming modes seamlessly, providing flexibility in data ingestion and processing.
D) Delta Lake provides limited support for monitoring and troubleshooting data pipelines, so relevant partner tools have to be identified and set up for enhanced operational efficiency.

3. Two data engineers are working on the same Databricks notebook in separate branches. Both have edited the same section of code. When one tries to merge the other's branch into their own using the Databricks Git folders UI, a merge conflict occurs on that notebook file. The UI highlights the conflict and presents options for resolution. How should the data engineers resolve this merge conflict using Databricks Git folders?

A) Delete the conflicted notebook file via the Databricks workspace UI, commit the deletion, and recreate the notebook from scratch in a new commit to bypass the conflict entirely.
B) Use the Git folders UI to manually edit the notebook file, selecting the desired lines from both versions and removing the conflict markers, then mark the conflict as resolved.
C) Abort the merge, discard all local changes, and try the merge operation again without reviewing the conflicting code.
D) Use the Git CLI in the cluster's web terminal to force-push the conflicted merge (git push -force), overriding the remote branch with the local version and discarding changes.

4. An upstream source writes Parquet data as hourly batches to directories named with the current date. A nightly batch job runs the following code to ingest all data from the previous day as indicated by the date variable:

Assume that the fields customer_id and order_id serve as a composite key to uniquely identify each order.
If the upstream system is known to occasionally produce duplicate entries for a single order hours apart, which statement is correct?

A) Each write to the orders table will only contain unique records, and only those records without duplicates in the target table will be written.
B) Each write to the orders table will only contain unique records, but newly written records may have duplicates already present in the target table.
C) Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, the operation will tail.
D) Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, these records will be overwritten.
E) Each write to the orders table will run deduplication over the union of new and existing records, ensuring no duplicate records are present.

5. The view updates represents an incremental batch of all newly ingested data to be inserted or updated in the customers table.
The following logic is used to process these records.

Which statement describes this implementation?

A) The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.
B) The customers table is implemented as a Type 2 table; old values are maintained but marked as no longer current and new values are inserted.
C) The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.
D) The customers table is implemented as a Type 3 table; old values are maintained as a new column alongside the current value.
E) The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.

Solutions: