2026年01月 Databricks Databricks-Certified-Professional-Data-Engineer実際の問題とブレーン問題集
Databricks-Certified-Professional-Data-Engineer合格させる問題集でDatabricks24時間で試験合格できます
DCPDE試験は、DataBricksのデータエンジニアリングに関連する幅広いトピックをカバーする包括的な評価です。この試験は、候補者がDataBricksプラットフォームにスケーラブルなデータパイプラインを設計、構築、展開する能力を実証することを要求する複数選択の質問とパフォーマンスベースのタスクで構成されています。
質問 # 56
A data engineer is creating a data ingestion pipeline to understand where customers are taking their rented bicycles during use. The engineer noticed that, over time, data being transmitted from the bicycle sensors fail to include key details like latitude and longitude. Downstream analysts need both the clean records and the quarantined records available for separate processing.
The data engineer already has this code:
import dlt
from pyspark.sql.functions import expr
rules = {
"valid_lat": "(lat IS NOT NULL)",
"valid_long": "(long IS NOT NULL)"
}
quarantine_rules = "NOT({})".format(" AND ".join(rules.values()))
@dlt.view
def raw_trips_data():
return spark.readStream.table("ride_and_go.telemetry.trips")
How should the data engineer meet the requirements to capture good and bad data?
- A. @dlt.table
@dlt.expect_all_or_drop(rules)
def trips_data_quarantine():
return spark.readStream.table("raw_trips_data") - B. @dlt.view
@dlt.expect_or_drop("lat_long_present", "(lat IS NOT NULL AND long IS NOT NULL)") def trips_data_quarantine():
return spark.readStream.table("ride_and_go.telemetry.trips") - C. @dlt.table(partition_cols=["is_quarantined", ])
@dlt.expect_all(rules)
def trips_data_quarantine():
return (
spark.readStream.table("raw_trips_data")
.withColumn("is_quarantined", expr(quarantine_rules))
) - D. @dlt.table(name="trips_data_quarantine")
def trips_data_quarantine():
return (
spark.readStream.table("raw_trips_data")
.filter(expr(quarantine_rules))
)
正解：D
解説：
Comprehensive and Detailed
The requirement is that both valid (good) and invalid (bad) records must be captured and available separately for downstream processing. Invalid records should not simply be dropped; they must be quarantined in a dedicated table.
In Databricks Lakeflow Declarative Pipelines (DLT), this is achieved by creating separate output tables:
One table for valid records (Silver table) that pass the expectations.
Another quarantine table that explicitly captures records failing the expectations.
Option A correctly implements this by:
Declaring a DLT table trips_data_quarantine.
Using .filter(expr(quarantine_rules)) to isolate invalid records (records where latitude or longitude is NULL).
This ensures analysts can query both good records (from the main Silver pipeline table) and bad records (from the quarantine table).
Why not the others?
B: Uses @dlt.expect_or_drop, which drops invalid records instead of quarantining them. This violates the requirement that quarantined data should be available.
C: Same as B, but applies expectations in bulk with expect_all_or_drop. Again, bad data is dropped, not quarantined.
D: Adds an is_quarantined flag in the same table. While it marks bad records, it does not separate them into a distinct quarantine table as required by the business use case.
Therefore, Option A is the only solution aligned with Databricks documentation for quarantining invalid data into a dedicated table while keeping valid data in the main pipeline.
質問 # 57
Which REST API call can be used to review the notebooks configured to run as tasks in a multi-task job?
- A. /jobs/runs/get
- B. /jobs/runs/list
- C. /jobs/list
- D. /jobs/runs/get-output
- E. /jobs/get
正解：E
解説：
This is the correct answer because it is the REST API call that can be used to review the notebooks configured to run as tasks in a multi-task job. The REST API is an interface that allows programmatically interacting with Databricks resources, such as clusters, jobs, notebooks, or tables. The REST API uses HTTP methods, such as GET, POST, PUT, or DELETE, to perform operations on these resources. The /jobs/get endpoint is a GET method that returns information about a job given its job ID. The information includes the job settings, such as the name, schedule, timeout, retries, email notifications, and tasks. The tasks are the units of work that a job executes. A task can be a notebook task, which runs a notebook with specified parameters; a jar task, which runs a JAR uploaded to DBFS with specified main class and arguments; or a python task, which runs a Python file uploaded to DBFS with specified parameters. A multi-task job is a job that has more than one task configured to run in a specific order or in parallel. By using the /jobs/get endpoint, one can review the notebooks configured to run as tasks in a multi-task job. Verified Reference: [Databricks Certified Data Engineer Professional], under "Databricks Jobs" section; Databricks Documentation, under "Get" section; Databricks Documentation, under "JobSettings" section.
質問 # 58
A data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs. A DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens.
Which statement describes the contents of the workspace audit logs concerning these events?
- A. Because User A created the jobs, their identity will be associated with both the job creation events and the job run events.
- B. Because the REST API was used for job creation and triggering runs, a Service Principal will be automatically used to identity these events.
- C. Because the REST API was used for job creation and triggering runs, user identity will not be captured in the audit logs.
- D. Because User B last configured the jobs, their identity will be associated with both the job creation events and the job run events.
- E. Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events.
正解：E
解説：
The events are that a data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs, and a DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens. The workspace audit logs are logs that record user activities in a Databricks workspace, such as creating, updating, or deleting objects like clusters, jobs, notebooks, or tables. The workspace audit logs also capture the identity of the user who performed each activity, as well as the time and details of the activity. Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events in the workspace audit logs. Verified References: [Databricks Certified Data Engineer Professional], under
"Databricks Workspace" section; Databricks Documentation, under "Workspace audit logs" section.
質問 # 59
What is the best way to query external csv files located on DBFS Storage to inspect the data using SQL?
- A. SELECT CSV. * from 'dbfs:/location/csv_files/'
- B. SELECT * FROM CSV. 'dbfs:/location/csv_files/'
- C. SELECT * FROM 'dbfs:/location/csv_files/' USING CSV
- D. SELECT * FROM 'dbfs:/location/csv_files/' FORMAT = 'CSV'
- E. You can not query external files directly, us COPY INTO to load the data into a table first
正解：B
解説：
Explanation
Answer is, SELECT * FROM CSV. 'dbfs:/location/csv_files/'
you can query external files stored on the storage using below syntax
SELECT * FROM format.`/Location`
format - CSV, JSON, PARQUET, TEXT
質問 # 60
A junior data engineer on your team has implemented the following code block.
The view new_events contains a batch of records with the same schema as the events Delta table. The event_id field serves as a unique key for this table.
When this query is executed, what will happen with new records that have the same event_id as an existing record?
- A. They are deleted.
- B. They are merged.
- C. They are ignored.
- D. They are inserted.
- E. They are updated.
正解：C
解説：
This is the correct answer because it describes what will happen with new records that have the same event_id as an existing record when the query is executed. The query uses the INSERT INTO command to append new records from the view new_events to the table events. However, the INSERT INTO command does not check for duplicate values in the primary key column (event_id) and does not perform any update or delete operations on existing records. Therefore, if there are new records that have the same event_id as an existing record, they will be ignored and not inserted into the table events. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Append data using INSERT INTO" section.
"If none of the WHEN MATCHED conditions evaluate to true for a source and target row pair that matches the merge_condition, then the target row is left unchanged." https://docs.databricks.com/en/sql/language-manual/delta-merge-into.html#:~:text=If%20none%20of%20the%20WHEN%20MATCHED%20conditions%20evaluate%20to%20true%20for%20a%20source%20and%20target%20row%20pair%20that%20matches%20the%20merge_condition%2C%20then%20the%20target%20row%20is%20left%20unchanged.
質問 # 61
Which of the statements is correct when choosing between lakehouse and Datawarehouse?
- A. Traditional Data warehouses are the preferred choice if we need to support ACID, Lakehouse does not support ACID.
- B. Traditional Data warehouses have special indexes which are optimized for Machine learning
- C. SQL support is only available for Traditional Datawarehouse's, Lakehouses support Python and Scala
- D. Traditional Data warehouses can serve low query latency with high reliability for BI workloads
- E. Lakehouse replaces the current dependency on data lakes and data warehouses uses an open standard storage format and supports low latency BI workloads.
正解：E
解説：
Explanation
The lakehouse replaces the current dependency on data lakes and data warehouses for modern data companies that desire:
* Open, direct access to data stored in standard data formats.
* Indexing protocols optimized for machine learning and data science.
* Low query latency and high reliability for BI and advanced analytics.
質問 # 62
Which of the following is a true statement about the global temporary view?
- A. A global temporary view persists even if the cluster is restarted
- B. A global temporary view is stored in a user database
- C. A global temporary view is automatically dropped after 7 days
- D. A global temporary view is available on all clusters for a given workspace
- E. A global temporary view is available only on the cluster it was created, when the cluster restarts global temporary view is automatically dropped.
正解：E
解説：
Explanation
The answer is, A global temporary view is available only on the cluster it was created.
Two types of temporary views can be created Session scoped and Global
*A session scoped temporary view is only available with a spark session, so another notebook in the same cluster can not access it. if a notebook is detached and re attached the temporary view is lost.
*A global temporary view is available to all the notebooks in the cluster, if a cluster restarts global temporary view is lost.
質問 # 63
A junior developer complains that the code in their notebook isn't producing the correct results in the development environment. A shared screenshot reveals that while they're using a notebook versioned with Databricks Repos, they're using a personal branch that contains old logic. The desired branch named dev-2.3.9 is not available from the branch selection dropdown.
Which approach will allow this developer to review the current logic for this notebook?
- A. Merge all changes back to the main branch in the remote Git repository and clone the repo again
- B. Use Repos to make a pull request use the Databricks REST API to update the current branch to dev-2.3.9
- C. Use Repos to pull changes from the remote Git repository and select the dev-2.3.9 branch.
- D. Use Repos to checkout the dev-2.3.9 branch and auto-resolve conflicts with the current branch
- E. Use Repos to merge the current branch and the dev-2.3.9 branch, then make a pull request to sync with the remote repository
正解：C
解説：
This is the correct answer because it will allow the developer to update their local repository with the latest changes from the remote repository and switch to the desired branch. Pulling changes will not affect the current branch or create any conflicts, as it will only fetch the changes and not merge them. Selecting the dev-2.3.9 branch from the dropdown will checkout that branch and display its contents in the notebook. Verified Reference: [Databricks Certified Data Engineer Professional], under "Databricks Tooling" section; Databricks Documentation, under "Pull changes from a remote repository" section.
質問 # 64
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:
Which command allows manual confirmation that these three requirements have been met?
- A. DESCRIBE HISTORY dev.pii test
- B. SHOW TBLPROPERTIES dev.pii test
- C. DESCRIBE DETAIL dev.pii test
- D. SHOW TABLES dev
- E. DESCRIBE EXTENDED dev.pii test
正解：E
解説：
This is the correct answer because it allows manual confirmation that these three requirements have been met.
The requirements are that all tables containing Personal Identifiable Information (PII) must be clearly annotated, which includes adding column comments, table comments, and setting the custom table property
"contains_pii" = true. The DESCRIBE EXTENDED command is used to display detailed information about a table, such as its schema, location, properties, and comments. By using this command on the dev.pii_test table, one can verify that the table has been created with the correct column comments, table comment, and custom table property as specified in the SQL DDL statement. Verified References: [Databricks Certified Data Engineer Professional], under "Lakehouse" section; Databricks Documentation, under "DESCRIBE EXTENDED" section.
質問 # 65
A junior data engineer has configured a workload that posts the following JSON to the Databricks REST API endpoint2.0/jobs/create.
Assuming that all configurations and referenced resources are available, which statement describes the result of executing this workload three times?
- A. Three new jobs named "Ingest new data" will be defined in the workspace, but no jobs will be executed.
- B. Three new jobs named "Ingest new data" will be defined in the workspace, and they will each run once daily.
- C. The logic defined in the referenced notebook will be executed three times on the referenced existing all purpose cluster.
- D. One new job named "Ingest new data" will be defined in the workspace, but it will not be executed.
- E. The logic defined in the referenced notebook will be executed three times on new clusters with the configurations of the provided cluster ID.
正解：A
解説：
This is the correct answer because the JSON posted to the Databricks REST API endpoint 2.0/jobs/create defines a new job with a name, an existing cluster id, and a notebook task. However, it does not specify any schedule or trigger for the job execution. Therefore, three new jobs with the same name and configuration will be created in the workspace, but none of them will be executed until they are manually triggered or scheduled.
Verified References: [Databricks Certified Data Engineer Professional], under "Monitoring & Logging" section; [Databricks Documentation], under "Jobs API - Create" section.
質問 # 66
You were asked to create or overwrite an existing delta table to store the below transaction data.
- A. 1.CREATE OR REPLACE DELTA TABLE transactions (
2.transactionId int,
3.transactionDate timestamp,
4.unitsSold int) - B. 1.CREATE OR REPLACE TABLE transactions (
2.transactionId int,
3.transactionDate timestamp,
4.unitsSold int) - C. 1.CREATE IF EXSITS REPLACE TABLE transactions (
2.transactionId int,
3.transactionDate timestamp,
4.unitsSold int) - D. 1.CREATE OR REPLACE TABLE IF EXISTS transactions (
2.transactionId int,
3.transactionDate timestamp,
4.unitsSold int)
5.FORMAT DELTA
正解：B
解説：
Explanation
The answer is
1.CREATE OR REPLACE TABLE transactions (
2.transactionId int,
3.transactionDate timestamp,
4.unitsSold int)
When creating a table in Databricks by default the table is stored in DELTA format.
質問 # 67
A data engineer is tasked with ensuring that a Delta table in Databricks continuously retains deleted files for 15 days (instead of the default 7 days), in order to permanently comply with the organization's data retention policy.
Which code snippet correctly sets this retention period for deleted files?
- A. spark.conf.set("spark.databricks.delta.deletedFileRetentionDuration", "15 days")
- B. spark.sql("ALTER TABLE my_table SET TBLPROPERTIES ('delta.deletedFileRetentionDuration' = 'interval 15 days')")
- C. spark.sql("VACUUM my_table RETAIN 15 HOURS")
- D. from delta.tables import *
deltaTable = DeltaTable.forPath(spark, "/mnt/data/my_table")
deltaTable.deletedFileRetentionDuration = "interval 15 days"
正解：B
解説：
Comprehensive and Detailed
In Delta Lake, the property delta.deletedFileRetentionDuration controls how long deleted data files are retained before being permanently removed during a VACUUM operation.
By default, this retention duration is set to 7 days.
To comply with stricter retention requirements, organizations can explicitly update the table property using an ALTER TABLE statement.
Option A uses the correct SQL command:
ALTER TABLE my_table SET TBLPROPERTIES ('delta.deletedFileRetentionDuration' = 'interval 15 days') This updates the Delta table metadata so that all future operations respect the 15-day retention policy for deleted files.
Why not the others?
B: This code incorrectly tries to set the property via the DeltaTable API. Delta's Python API does not expose direct attributes like deletedFileRetentionDuration; instead, properties must be set through ALTER TABLE or DataFrameWriter options.
C: VACUUM ... RETAIN specifies a one-time file cleanup action (e.g., retaining 15 hours of history), not a persistent retention policy. It cannot be used to set a continuous retention duration.
D: Setting spark.conf applies a session-level configuration and does not permanently update the table's retention metadata. Once the session ends, this configuration is lost.
Therefore, Option A is the correct and documented approach for persistently enforcing a 15-day deleted file retention period in Delta Lake.
質問 # 68
A Delta table of weather records is partitioned by date and has the below schema:
date DATE, device_id INT, temp FLOAT, latitude FLOAT, longitude FLOAT
To find all the records from within the Arctic Circle, you execute a query with the below filter:
latitude > 66.3
Which statement describes how the Delta engine identifies which files to load?
- A. The Hive metastore is scanned for min and max statistics for the latitude column
- B. The Delta log is scanned for min and max statistics for the latitude column
- C. All records are cached to attached storage and then the filter is applied
- D. All records are cached to an operational database and then the filter is applied
- E. The Parquet file footers are scanned for min and max statistics for the latitude column
正解：B
解説：
This is the correct answer because Delta Lake uses a transaction log to store metadata about each table, including min and max statistics for each column in each data file. The Delta engine can use this information to quickly identify which files to load based on a filter condition, without scanning the entire table or the file footers. This is called data skipping and it can improve query performance significantly. Verified References:
[Databricks Certified Data Engineer Professional], under "Delta Lake" section; [Databricks Documentation], under "Optimizations - Data Skipping" section.
In the Transaction log, Delta Lake captures statistics for each data file of the table. These statistics indicate per file:
- Total number of records
- Minimum value in each column of the first 32 columns of the table
- Maximum value in each column of the first 32 columns of the table
- Null value counts for in each column of the first 32 columns of the table When a query with a selective filter is executed against the table, the query optimizer uses these statistics to generate the query result. it leverages them to identify data files that may contain records matching the conditional filter.
For the SELECT query in the question, The transaction log is scanned for min and max statistics for the price column
質問 # 69
The default threshold of VACUUM is 7 days, internal audit team asked to certain tables to maintain at least
365 days as part of compliance requirement, which of the below setting is needed to implement.
- A. ALTER TABLE table_name set EXENDED TBLPROPERTIES (delta.vaccum.duration= 'interval 365 days')
- B. ALTER TABLE table_name set EXENDED TBLPROPERTIES (del-ta.deletedFileRetentionDuration=
'interval 365 days') - C. ALTER TABLE table_name set TBLPROPERTIES (del-ta.deletedFileRetentionDuration= 'interval 365 days')
- D. MODIFY TABLE table_name set TBLPROPERTY (delta.maxRetentionDays = 'inter-val 365 days')
正解：C
解説：
Explanation
1.ALTER TABLE table_name SET TBLPROPERTIES ( property_key [ = ] property_val [, ...] ) TBLPROPERTIES allow you to set key-value pairs Table properties and table options (Databricks SQL) | Databricks on AWS
質問 # 70
Which statement describes the correct use of pyspark.sql.functions.broadcast?
- A. It marks a DataFrame as small enough to store in memory on all executors, allowing a broadcast join.
- B. It marks a column as having low enough cardinality to properly map distinct values to available partitions, allowing a broadcast join.
- C. It marks a column as small enough to store in memory on all executors, allowing a broadcast join.
- D. It caches a copy of the indicated table on all nodes in the cluster for use in all future queries during the cluster lifetime.
- E. It caches a copy of the indicated table on attached storage volumes for all active clusters within a Databricks workspace.
正解：A
解説：
Explanation
https://spark.apache.org/docs/3.1.3/api/python/reference/api/pyspark.sql.functions.broadcast.html
質問 # 71
......
最新問題をダウンロードDatabricks-Certified-Professional-Data-Engineer問題集で2026年最新のDatabricks-Certified-Professional-Data-Engineer試験問題集：https://jp.fast2test.com/Databricks-Certified-Professional-Data-Engineer-premium-file.html
最新問題を使おうDatabricks-Certified-Professional-Data-Engineer試験問題と解答PDFで一年間無料更新：https://drive.google.com/open?id=12CWU92nghYtJUIefz-X5MLwwWIodKH1J