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

Databricks Certified Data Engineer Professional Sample Questions:

1. A query is taking too long to run. After investigating the Spark UI, the data engineer discovered a significant amount of disk spill. The compute instance being used has a core-to-memory ratio of
1:2. What are the two steps the data engineer should take to minimize spillage? (Choose two.)

A) Reduce spark.sql.files.maxPartitionBytes.
B) Choose a compute instance with a higher core-to-memory ratio.
C) Increase spark.sql.files.maxPartitionBytes.
D) Choose a compute instance with more disk space.
E) Choose a compute instance with more network bandwidth.

2. A Delta Lake table representing metadata about content from user has the following schema:
user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE Based on the above schema, which column is a good candidate for partitioning the Delta Table?

A) Date
B) User_id
C) Post_time
D) latitude
E) Post_id

3. A table is registered with the following code:

Both users and orders are Delta Lake tables. Which statement describes the results of querying recent_orders?

A) Results will be computed and cached when the table is defined; these cached results will incrementally update as new records are inserted into source tables.
B) All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
C) All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.
D) All logic will execute when the table is defined and store the result of joining tables to the DBFS; this stored data will be returned when the table is queried.
E) The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.

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

5. A data engineer is designing a pipeline in Databricks that processes records from a Kafka stream where late-arriving data is common. Which approach should the data engineer use?

A) Use batch processing and overwrite the entire output table each time to ensure late data is incorporated correctly.
B) Use a watermark to specify the allowed lateness to accommodate records that arrive after their expected window, ensuring correct aggregation and state management.
C) Use an Auto CDC pipeline with batch tables to simplify late data handling.
D) Implement a custom solution using Databricks Jobs to periodically reprocess all historical data.

Solutions: