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

Section	Objectives
Databricks Lakehouse Platform Architecture	- Data governance concepts (Unity Catalog basics) - Medallion architecture (Bronze, Silver, Gold) - Workspace and cluster architecture
Data Ingestion and Processing	- ETL pipeline design patterns - Batch and streaming ingestion with Auto Loader - Structured Streaming fundamentals
Data Modeling and Transformation	- Performance optimization techniques - Spark SQL transformations - Dimensional modeling concepts
Delta Lake and Data Management	- Time travel and versioning - Schema evolution and enforcement - Delta Lake transactions and ACID properties
Production Pipelines and Orchestration	- Error handling and recovery strategies - Job scheduling and monitoring - Databricks Workflows

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Databricks Certified Data Engineer Professional Exam 認定 Databricks-Certified-Data-Engineer-Professional 試験問題 (Q139-Q144):

質問 # 139
A company processes semi-structured JSON files from an external source using Auto Loader in a classic Databricks job. Occasionally, records arrive with null critical fields, invalid types, or unexpected nested schema variations. The engineer must ensure that malformed or non- conforming records are not dropped silently and are captured in a separate quarantine table. The pipeline should continue processing good records into the Bronze layer without failing the job, and the approach must support both batch and streaming ingestion.
The data engineer needs to build a robust ingestion pattern that automatically routes bad records to a quarantine Delta table, while still ingesting good records into the Bronze layer for further processing.
Which approach fulfills the quarantine mechanism in this ingestion architecture?

A. Create a notebook job with inferSchema=True, write a streaming query with .foreachBatch() and catch exceptions using try/except to redirect failed batches to quarantine.
B. Use Auto Loader with failFast mode to set to false, and enable schema evolution; invalid records will be silently ignored during ingestion.
C. Use Lakeflow Spark Declarative Pipelines with a SQL pipeline; configure it to drop rows with nulls using where critical_fields is not null, and rely on audit logs for malformed data.
D. Use Auto Loader with LDP and implement an EXPECT () constraint with a record audit logic to route bad records.

正解：D

解説：
Lakeflow Spark Declarative Pipelines provide built-in support for data quality enforcement using EXPECT clauses. By defining expectations on critical fields and configuring the pipeline to audit or quarantine failed records, malformed or non-conforming JSON records are automatically routed to a quarantine Delta table while valid records continue flowing into the Bronze layer. This approach is robust, auditable, works for both batch and streaming ingestion, and avoids silently dropping bad data or failing the pipeline.

質問 # 140
The data engineering team has configured a job to process customer requests to be forgotten (have their data deleted). All user data that needs to be deleted is stored in Delta Lake tables using default table settings.
The team has decided to process all deletions from the previous week as a batch job at 1am each Sunday. The total duration of this job is less than one hour. Every Monday at 3am, a batch job executes a series of VACUUM commands on all Delta Lake tables throughout the organization.
The compliance officer has recently learned about Delta Lake's time travel functionality. They are concerned that this might allow continued access to deleted data.
Assuming all delete logic is correctly implemented, which statement correctly addresses this concern?

A. Because the default data retention threshold is 7 days, data files containing deleted records will be retained until the vacuum job is run 8 days later.
B. Because Delta Lake time travel provides full access to the entire history of a table, deleted records can always be recreated by users with full admin privileges.
C. Because the default data retention threshold is 24 hours, data files containing deleted records will be retained until the vacuum job is run the following day.
D. Because Delta Lake's delete statements have ACID guarantees, deleted records will be permanently purged from all storage systems as soon as a delete job completes.
E. Because the vacuum command permanently deletes all files containing deleted records, deleted records may be accessible with time travel for around 24 hours.

正解：A

解説：
https://learn.microsoft.com/en-us/azure/databricks/delta/vacuum

質問 # 141
A nightly job ingests data into a Delta Lake table using the following code:

The next step in the pipeline requires a function that returns an object that can be used to manipulate new records that have not yet been processed to the next table in the pipeline.
Which code snippet completes this function definition?

A. def new_records():
B. return spark.readStream.table("bronze")
C. return spark.readStream.load("bronze")
D.
E. return spark.read.option("readChangeFeed", "true").table ("bronze")

正解：D

解説：
https://docs.databricks.com/en/delta/delta-change-data-feed.html
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質問 # 142
Although the Databricks Utilities Secrets module provides tools to store sensitive credentials and avoid accidentally displaying them in plain text users should still be careful with which credentials are stored here and which users have access to using these secrets.
Which statement describes a limitation of Databricks Secrets?

A. The Databricks REST API can be used to list secrets in plain text if the personal access token has proper credentials.
B. Because the SHA256 hash is used to obfuscate stored secrets, reversing this hash will display Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from the value in plain text.
C. Iterating through a stored secret and printing each character will display secret contents in plain text.
D. Account administrators can see all secrets in plain text by logging on to the Databricks Accounts console.
E. Secrets are stored in an administrators-only table within the Hive Metastore; database administrators have permission to query this table by default.

正解：A

解説：
This is the correct answer because it describes a limitation of Databricks Secrets. Databricks Secrets is a module that provides tools to store sensitive credentials and avoid accidentally displaying them in plain text. Databricks Secrets allows creating secret scopes, which are collections of secrets that can be accessed by users or groups. Databricks Secrets also allows creating and managing secrets using the Databricks CLI or the Databricks REST API. However, a limitation of Databricks Secrets is that the Databricks REST API can be used to list secrets in plain text if the personal access token has proper credentials. Therefore, users should still be careful with which credentials are stored in Databricks Secrets and which users have access to using these secrets.

質問 # 143
The Databricks workspace administrator has configured interactive clusters for each of the data engineering groups. To control costs, clusters are set to terminate after 30 minutes of inactivity.
Each user should be able to execute workloads against their assigned clusters at any time of the day.
Assuming users have been added to a workspace but not granted any permissions, which of the following describes the minimal permissions a user would need to start and attach to an already configured cluster.

A. Cluster creation allowed. "Can Restart" privileges on the required cluster
B. Workspace Admin privileges, cluster creation allowed. "Can Attach To" privileges on the required cluster
C. Cluster creation allowed. "Can Attach To" privileges on the required cluster
D. "Can Manage" privileges on the required cluster
E. "Can Restart" privileges on the required cluster

正解：E

解説：
https://learn.microsoft.com/en-us/azure/databricks/security/auth-authz/access-control/cluster-acl
https://docs.databricks.com/en/security/auth-authz/access-control/cluster-acl.html

質問 # 144
......

テストの準備に多くの時間を費やし、それでも何度も失敗するのは馬鹿げていますか？一部の受験者は、Databricks Databricks-Certified-Data-Engineer-Professional試験ダンプ問題で簡単に試験に合格しますか？試験に合格し、認定を取得することが目標である場合、Databricks-Certified-Data-Engineer-Professional試験ダンプは、目標を簡単に達成するのに役立ちます。選択してみませんか？ Databricks-Certified-Data-Engineer-Professional試験ダンプ問題を含むテストの前にわずか数十のお金と20〜35時間の有効な準備で、確実に試験をクリアできます。では、なぜあなたは無駄な努力をするのに多くの時間を無駄にしているのですか？

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