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Databricks Certified Data Engineer Professional 認定 Databricks-Certified-Data-Engineer-Professional 試験問題:

1. A data engineer needs to design an efficient pipeline that automatically processes new CSV files as they arrive in S3 storage. Which Databricks feature should the data engineer use to meet these requirements?

A) COPY INTO SQL command with parameters to track processed files
B) Traditional batch processing with scheduled Databricks Jobs
C) Streaming from cloud storage using standard Spark readStream with format ("csv") and format ("json")
D) Auto Loader with schema inference and evolution enabled

2. A production cluster has 3 executor nodes and uses the same virtual machine type for the driver and executor.
When evaluating the Ganglia Metrics for this cluster, which indicator would signal a bottleneck caused by code executing on the driver?

A) Overall cluster CPU utilization is around 25%
B) Bytes Received never exceeds 80 million bytes per second
C) Network I/O never spikes
D) The five Minute Load Average remains consistent/flat
E) Total Disk Space remains constant

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

4. A data engineer is configuring Delta Sharing for a Databricks-to-Databricks scenario to optimize read performance. The recipient needs to perform time travel queries and streaming reads on shared sales data. Which configuration will provide the optimal performance while enabling these capabilities?

A) Share tables WITHOUT HISTORY and enable partitioning for better query performance.
B) Use the open sharing protocol instead of Databricks-to-Databricks sharing for better performance.
C) Share tables WITH HISTORY, ensure tables don't have partitioning enabled, and enable CDF before sharing.
D) Share the entire schema WITHOUT HISTORY and rely on recipient-side caching for performance.

5. Given the following PySpark code snippet in a Databricks notebook:
filtered_df = spark.read.format("delta").load("/mnt/data/large_table")
\
.filter("event_date > '2024-01-01'")
filtered_df.count()
The data engineer notices from the Query Profiler that the scan operator for filtered_df is reading almost all files, despite the filter being applied.
What is the probable reason for poor data skipping?

A) The event_date column is outside the table's partitioning and Z-ordering scheme.
B) The filter condition involves a data type excluded from data skipping support.
C) The Delta table lacks optimization that enables dynamic file pruning.
D) The filter is executed only after the full data scan, preventing data skipping.

質問と回答：