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試験コード：Databricks-Certified-Data-Engineer-Professional

試験名称：Databricks Certified Data Engineer Professional Exam

最近更新時間：2025-06-22

問題と解答：全127問

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質問 1：
A junior data engineer is migrating a workload from a relational database system to the Databricks Lakehouse. The source system uses a star schema, leveraging foreign key constrains and multi-table inserts to validate records on write.
Which consideration will impact the decisions made by the engineer while migrating this workload?
A. Databricks only allows foreign key constraints on hashed identifiers, which avoid collisions in highly-parallel writes.
B. Foreign keys must reference a primary key field; multi-table inserts must leverage Delta Lake's upsert functionality.
C. All Delta Lake transactions are ACID compliance against a single table, and Databricks does not enforce foreign key constraints.
D. Databricks supports Spark SQL and JDBC; all logic can be directly migrated from the source system without refactoring.
E. Committing to multiple tables simultaneously requires taking out multiple table locks and can lead to a state of deadlock.
正解：C
解説: (Topexam メンバーにのみ表示されます)

質問 2：
The data science team has requested assistance in accelerating queries on free form text from user reviews. The data is currently stored in Parquet with the below schema:
item_id INT, user_id INT, review_id INT, rating FLOAT, review STRING
The review column contains the full text of the review left by the user. Specifically, the data science team is looking to identify if any of 30 key words exist in this field.
A junior data engineer suggests converting this data to Delta Lake will improve query performance.
Which response to the junior data engineer s suggestion is correct?
A. Text data cannot be stored with Delta Lake.
B. The Delta log creates a term matrix for free text fields to support selective filtering.
C. ZORDER ON review will need to be run to see performance gains.
D. Delta Lake statistics are only collected on the first 4 columns in a table.
E. Delta Lake statistics are not optimized for free text fields with high cardinality.
正解：E
解説: (Topexam メンバーにのみ表示されます)

質問 3：
The following code has been migrated to a Databricks notebook from a legacy workload:

The code executes successfully and provides the logically correct results, however, it takes over
20 minutes to extract and load around 1 GB of data.
Which statement is a possible explanation for this behavior?
A. %sh does not distribute file moving operations; the final line of code should be updated to use %fs instead.
B. %sh executes shell code on the driver node. The code does not take advantage of the worker nodes or Databricks optimized Spark.
C. Instead of cloning, the code should use %sh pip install so that the Python code can get executed in parallel across all nodes in a cluster.
D. %sh triggers a cluster restart to collect and install Git. Most of the latency is related to cluster startup time.
E. Python will always execute slower than Scala on Databricks. The run.py script should be refactored to Scala.
正解：B
解説: (Topexam メンバーにのみ表示されます)

質問 4：
When evaluating the Ganglia Metrics for a given cluster with 3 executor nodes, which indicator would signal proper utilization of the VM's resources?
A. Bytes Received never exceeds 80 million bytes per second
B. The five Minute Load Average remains consistent/flat
C. Network I/O never spikes
D. Total Disk Space remains constant
E. CPU Utilization is around 75% Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from
正解：E
解説: (Topexam メンバーにのみ表示されます)

質問 5：
The data science team has created and logged a production model using MLflow. The following code correctly imports and applies the production model to output the predictions as a new DataFrame named preds with the schema "customer_id LONG, predictions DOUBLE, date DATE".
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The data science team would like predictions saved to a Delta Lake table with the ability to compare all predictions across time. Churn predictions will be made at most once per day.
Which code block accomplishes this task while minimizing potential compute costs?
A.

C. preds.write.format("delta").save("/preds/churn_preds")
D. preds.write.mode("append").saveAsTable("churn_preds")
E.

正解：D

質問 6：
A developer has successfully configured credential for Databricks Repos and cloned a remote Git repository. Hey don not have privileges to make changes to the main branch, which is the only branch currently visible in their workspace.
Use Response to pull changes from the remote Git repository commit and push changes to a branch that appeared as a changes were pulled.
A. Use repos to merge all difference and make a pull request back to the remote repository.
B. Use Repos to create a new branch commit all changes and push changes to the remote Git repertory.
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C. Use Repos to merge all differences and make a pull request back to the remote repository.
D. Use repos to create a fork of the remote repository commit all changes and make a pull request on the source repository
E. Use Repos to pull changes from the remote Git repository; commit and push changes to a branch that appeared as changes were pulled.
正解：B
解説: (Topexam メンバーにのみ表示されます)

質問 7：
An upstream system has been configured to pass the date for a given batch of data to the Databricks Jobs API as a parameter. The notebook to be scheduled will use this parameter to load data with the following code:
df = spark.read.format("parquet").load(f"/mnt/source/(date)")
Which code block should be used to create the date Python variable used in the above code block?
A. import sys
date = sys.argv[1]
B. date = spark.conf.get("date")
C. input_dict = input()
date= input_dict["date"]
D. date = dbutils.notebooks.getParam("date")
E. dbutils.widgets.text("date", "null")
date = dbutils.widgets.get("date")
正解：E
解説: (Topexam メンバーにのみ表示されます)

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

1. 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:
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Choose the response that correctly fills in the blank within the code block to complete this task.

A) "event_time"
B) window("event_time", "5 minutes").alias("time")
C) lag("event_time", "10 minutes").alias("time")
D) window("event_time", "10 minutes").alias("time")
E) to_interval("event_time", "5 minutes").alias("time")

2. The data governance team has instituted a requirement that all tables containing Personal Identifiable Information (PH) must be clearly annotated. This includes adding column comments, table comments, and setting the custom table property "contains_pii" = true.
The following SQL DDL statement is executed to create a new table:
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Which command allows manual confirmation that these three requirements have been met?

A) SHOW TABLES dev
B) DESCRIBE DETAIL dev.pii test
C) DESCRIBE HISTORY dev.pii test
D) SHOW TBLPROPERTIES dev.pii test
E) DESCRIBE EXTENDED dev.pii test

3. Two of the most common data locations on Databricks are the DBFS root storage and external object storage mounted with dbutils.fs.mount().
Which of the following statements is correct?

A) Neither the DBFS root nor mounted storage can be accessed when using %sh in a Databricks notebook.
B) By default, both the DBFS root and mounted data sources are only accessible to workspace administrators.
C) DBFS is a file system protocol that allows users to interact with files stored in object storage using syntax and guarantees similar to Unix file systems.
D) The DBFS root is the most secure location to store data, because mounted storage volumes must have full public read and write permissions.
E) The DBFS root stores files in ephemeral block volumes attached to the driver, while mounted directories will always persist saved data to external storage between sessions.

4. A Structured Streaming job deployed to production has been resulting in higher than expected cloud storage costs. At present, during normal execution, each microbatch of data is processed in less than 3s; at least 12 times per minute, a microbatch is processed that contains 0 records. The streaming write was configured using the default trigger settings. The production job is currently scheduled alongside many other Databricks jobs in a workspace with instance pools provisioned to reduce start-up time for jobs with batch execution.
Holding all other variables constant and assuming records need to be processed in less than 10 minutes, which adjustment will meet the requirement?

A) Increase the number of shuffle partitions to maximize parallelism, since the trigger interval cannot be modified without modifying the checkpoint directory.
B) Use the trigger once option and configure a Databricks job to execute the query every 10 minutes; this approach minimizes costs for both compute and storage.
C) Set the trigger interval to 500 milliseconds; setting a small but non-zero trigger interval ensures that the source is not queried too frequently.
D) Set the trigger interval to 3 seconds; the default trigger interval is consuming too many records per batch, resulting in spill to disk that can increase volume costs.
E) Set the trigger interval to 10 minutes; each batch calls APIs in the source storage account, so decreasing trigger frequency to maximum allowable threshold should minimize this cost.

5. Review the following error traceback:
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Which statement describes the error being raised?

A) There is a type error because a DataFrame object cannot be multiplied.
B) The code executed was PvSoark but was executed in a Scala notebook.
C) There is no column in the table named heartrateheartrateheartrate
D) There is a type error because a column object cannot be multiplied.
E) There is a syntax error because the heartrate column is not correctly identified as a column.

質問と回答：

質問 # 1
正解： B

質問 # 2
正解： E

質問 # 3
正解： C

質問 # 4
正解： E

質問 # 5
正解： C

Databricks-Certified-Data-Engineer-Professional 関連試験: Databricks-Certified-Data-Engineer-Associate - Databricks Certified Data Engineer Associate Exam; Databricks-Certified-Professional-Data-Scientist - Databricks Certified Professional Data Scientist Exam; Databricks-Certified-Professional-Data-Engineer - Databricks Certified Professional Data Engineer Exam; Associate-Developer-Apache-Spark-3.5 - Databricks Certified Associate Developer for Apache Spark 3.5 - Python; Associate-Developer-Apache-Spark - Databricks Certified Associate Developer for Apache Spark 3.0 Exam

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