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

Databricks Certified Data Engineer Professional Sample Questions:

1. Which statement describes Delta Lake optimized writes?

A) Optimized writes logical partitions instead of directory partitions partition boundaries are only represented in metadata fewer small files are written.
B) Before a job cluster terminates, OPTIMIZE is executed on all tables modified during the most recent job.
C) A shuffle occurs prior to writing to try to group data together resulting in fewer files instead of each executor writing multiple files based on directory partitions.
D) An asynchronous job runs after the write completes to detect if files could be further compacted; yes, an OPTIMIZE job is executed toward a default of 1 GB.

2. A Data Engineer is building a simple data pipeline using Lakeflow Declarative Pipelines (LDP) in Databricks to ingest customer data. The raw customer data is stored in a cloud storage location in JSON format. The task is to create Lakeflow Declarative Pipelines that read the raw JSON data and write it into a Delta table for further processing. Which code snippet will correctly ingest the raw JSON data and create a Delta table using LDP?

A) import dlt
@dlt.table
def raw_customers():
return spark.read.json("s3://my-bucket/raw-customers/")
B) import dlt
@dlt.table
def raw_customers():
return spark.read.format("parquet").load("s3://my-bucket/raw-customers/")
C) import dlt
@dlt.view
def raw_customers():
return spark.format.json("s3://my-bucket/raw-customers/")
D) import dlt
@dlt.table
def raw_customers():
return spark.read.format("csv").load("s3://my-bucket/raw-customers/")

3. A faulty IoT sensor in a factory reports a temperature of -500, causing the LDP pipeline to fail the expectation, which only allows values between -100 and 200 degrees Celsius. The data engineer would like to further analyze the faulty data to better understand the reason behind this. How should the data engineer resolve the faulty data while ensuring data quality standards are maintained?

A) Remove all expectations form the pipeline to prevent any future failures, regardless of data quality.
B) Change the expectation action from fail to warn so that invalid records are included in the output and the pipeline does not fail.
C) Fix the pipeline code and implement a quarantine logic to isolate the faulty data before re-running the pipeline.
D) Ignore the error and simply re-run the pipeline, as Databricks will automatically skip the problematic record on the next run.

4. Incorporating unit tests into a PySpark application requires upfront attention to the design of your jobs, or a potentially significant refactoring of existing code.
Which statement describes a main benefit that offset this additional effort?

A) Yields faster deployment and execution times
B) Validates a complete use case of your application
C) Improves the quality of your data
D) Ensures that all steps interact correctly to achieve the desired end result
E) Troubleshooting is easier since all steps are isolated and tested individually

5. A data engineering team needs to implement a tagging system for their tables as part of an automated ETL process, and needs to apply tags programmatically to tables in Unity Catalog.
Which SQL command adds tags to a table programmatically?

A) ALTER TABLE table_name SET TAGS ('key1' = 'value1', 'key2' = 'value2');
B) COMMENT ON TABLE table_name TAGS ('key1' = 'value1', 'key2' = 'value2');
C) SET TAGS FOR table_name AS ('key1' = 'value1', 'key2' = 'value2');
D) APPLY TAGS ON table_name VALUES ('key1' = 'value1', 'key2' = 'value2');

Solutions: