検証済みのDatabricks-Certified-Professional-Data-Engineer問題集と解答で合格保証もしくは全額返金 [2024年12月]
Databricks-Certified-Professional-Data-EngineerのPDF問題集で2024年12月29日最近更新された問題
DataBricksは、データエンジニアリング、機械学習、分析サービスを提供するクラウドベースのプラットフォームです。 DataBricks認定プロフェッショナルデータエンジニア（DCPDE）試験は、データパイプラインの構築、データインフラストラクチャの管理、データアブリックプラットフォームでのデータ処理ワークフローの最適化を担当するデータエンジニアのスキルと知識をテストするように設計されています。
この試験は、Databricksを使用してデータパイプラインを構築および維持する能力と専門性を証明するために設計された、選択式問題と実技演習で構成される試験です。試験に合格した候補者には、「Databricks Certified Professional Data Engineer」認証が授与されます。この認証は、Databricksの使用における候補者の専門性と能力を世界中の雇用主に認めさせることができるため、ビッグデータエンジニアリングおよび分析分野でキャリアを進めたい人にとって貴重な資格です。
Databricks認定のプロフェッショナルData-Engineer認定試験は、データエンジニアリングのすべての側面をDatabricksとカバーする包括的なテストです。この試験は、DataBricksアーキテクチャ、データエンジニアリングの概念、データビリックによるデータ処理、およびデータビリックを使用したデータストレージに関する候補者の知識をテストするように設計されています。この試験では、DataBricksを使用してデータエンジニアリングソリューションを設計、実装、および維持する候補者の能力もテストします。
質問 # 49
You are trying to create an object by joining two tables that and it is accessible to data scientist's team, so it does not get dropped if the cluster restarts or if the notebook is detached. What type of object are you trying to create?
- A. Global Temporary view
- B. Temporary view
- C. External view
- D. View
- E. Global Temporary view with cache option
正解：D
解説：
Explanation
Answer is View, A view can be used to join multiple tables but also persist into meta stores so others can accesses it
質問 # 50
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 nameddev-2.3.9is not available from the branch selection dropdown.
Which approach will allow this developer to review the current logic for this notebook?
- A. Use Repos to pull changes from the remote Git repository and select the dev-2.3.9 branch.
- B. Merge all changes back to the main branch in the remote Git repository and clone the repo again
- C. 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
- D. Use Repos to make a pull request use the Databricks REST API to update the current branch to dev-2.3.9
- E. Use Repos to checkout the dev-2.3.9 branch and auto-resolve conflicts with the current branch
正解：A
解説：
Explanation
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 References: [Databricks Certified Data Engineer Professional], under "Databricks Tooling" section; Databricks Documentation, under "Pull changes from a remote repository" section.
質問 # 51
A denote the event 'student is female' and let B denote the event 'student is French'. In a class of 100 students
suppose 60 are French, and suppose that 10 of the French students are females. Find the probability that if I
pick a French student, it will be a girl, that is, find P(A|B).
- A. 1/6
- B. 1/3
- C. 2/3
- D. 2/6
正解：A
解説：
Explanation
Since 10 out of 100 students are both French and female, then
P(AandB)=10100
Also. 60 out of the 100 students are French, so
P(B)=60100
So the required probability is:
P(A|B)=P(AandB)P(B)=10/10060/100=16
質問 # 52
A data ingestion task requires a one-TB JSON dataset to be written out to Parquet with a target part-file size of 512 MB. Because Parquet is being used instead of Delta Lake, built-in file-sizing features such as Auto-Optimize & Auto-Compaction cannot be used.
Which strategy will yield the best performance without shuffling data?
- A. Set spark.sql.shuffle.partitions to 512, ingest the data, execute the narrow transformations, and then write to parquet.
- B. Ingest the data, execute the narrow transformations, repartition to 2,048 partitions (1TB* 1024*1024/512), and then write to parquet.
- C. Set spark.sql.adaptive.advisoryPartitionSizeInBytes to 512 MB bytes, ingest the data, execute the narrow transformations, coalesce to 2,048 partitions (1TB*1024*1024/512), and then write to parquet.
- D. Set spark.sql.shuffle.partitions to 2,048 partitions (1TB*1024*1024/512), ingest the data, execute the narrow transformations, optimize the data by sorting it (which automatically repartitions the data), and then write to parquet.
- E. Set spark.sql.files.maxPartitionBytes to 512 MB, ingest the data, execute the narrow transformations, and then write to parquet.
正解：D
質問 # 53
A Delta Lake table was created with the below query:
Realizing that the original query had a typographical error, the below code was executed:
ALTER TABLE prod.sales_by_stor RENAME TO prod.sales_by_store
Which result will occur after running the second command?
- A. The table reference in the metastore is updated and all data files are moved.
- B. The table reference in the metastore is updated and no data is changed.
- C. The table name change is recorded in the Delta transaction log.
- D. All related files and metadata are dropped and recreated in a single ACID transaction.
- E. A new Delta transaction log Is created for the renamed table.
正解：B
解説：
Explanation
The query uses the CREATE TABLE USING DELTA syntax to create a Delta Lake table from an existing Parquet file stored in DBFS. The query also uses the LOCATION keyword to specify the path to the Parquet file as /mnt/finance_eda_bucket/tx_sales.parquet. By using the LOCATION keyword, the query creates an external table, which is a table that is stored outside of the default warehouse directory and whose metadata is not managed by Databricks. An external table can be created from an existing directory in a cloud storage system, such as DBFS or S3, that contains data files in a supported format, such as Parquet or CSV.
The result that will occur after running the second command is that the table reference in the metastore is updated and no data is changed. The metastore is a service that stores metadata about tables, such as their schema, location, properties, and partitions. The metastore allows users to access tables using SQL commands or Spark APIs without knowing their physical location or format. When renaming an external table using the ALTER TABLE RENAME TO command, only the table reference in the metastore is updated with the new name; no data files or directories are moved or changed in the storage system. The table will still point to the same location and use the same format as before. However, if renaming a managed table, which is a table whose metadata and data are both managed by Databricks, both the table reference in the metastore and the data files in the default warehouse directory are moved and renamed accordingly. Verified References:
[Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "ALTERTABLE RENAME TO" section; Databricks Documentation, under "Metastore" section; Databricks Documentation, under "Managed and external tables" section.
質問 # 54
A small company based in the United States has recently contracted a consulting firm in India to implement several new data engineering pipelines to power artificial intelligence applications. All the company's data is stored in regional cloud storage in the United States.
The workspace administrator at the company is uncertain about where the Databricks workspace used by the contractors should be deployed.
Assuming that all data governance considerations are accounted for, which statement accurately informs this decision?
- A. Cross-region reads and writes can incur significant costs and latency; whenever possible, compute should be deployed in the same region the data is stored.
- B. Databricks notebooks send all executable code from the user's browser to virtual machines over the open internet; whenever possible, choosing a workspace region near the end users is the most secure.
- C. Databricks runs HDFS on cloud volume storage; as such, cloud virtual machines must be deployed in the region where the data is stored.
- D. Databricks leverages user workstations as the driver during interactive development; as such, users should always use a workspace deployed in a region they are physically near.
- E. Databricks workspaces do not rely on any regional infrastructure; as such, the decision should be made based upon what is most convenient for the workspace administrator.
正解：A
解説：
Explanation
This is the correct answer because it accurately informs this decision. The decision is about where the Databricks workspace used by the contractors should be deployed. The contractors are based in India, while all the company's data is stored in regional cloud storage in the United States. When choosing a region for deploying a Databricks workspace, one of the important factors to consider is the proximity to the data sources and sinks. Cross-region reads and writes can incur significant costs and latency due to network bandwidth and data transfer fees. Therefore, whenever possible, compute should be deployed in the same region the data is stored to optimize performance and reduce costs. Verified References: [Databricks Certified Data Engineer Professional], under "Databricks Workspace" section; Databricks Documentation, under "Choose a region" section.
質問 # 55
Spill occurs as a result of executing various wide transformations. However, diagnosing spill requires one to proactively look for key indicators.
Where in the Spark UI are two of the primary indicators that a partition is spilling to disk?
- A. Driver's and Executor's log files
- B. Stage's detail screen and Executor's files
- C. Executor's detail screen and Executor's log files
- D. Stage's detail screen and Query's detail screen
正解：D
解説：
In Apache Spark's UI, indicators of data spilling to disk during the execution of wide transformations can be found in the Stage's detail screen and the Query's detail screen. These screens provide detailed metrics about each stage of a Spark job, including information about memory usage and spill data. If a task is spilling data to disk, it indicates that the data being processed exceeds the available memory, causing Spark to spill data to disk to free up memory. This is an important performance metric as excessive spill can significantly slow down the processing.
References:
* Apache Spark Monitoring and Instrumentation: Spark Monitoring Guide
* Spark UI Explained: Spark UI Documentation
質問 # 56
What is the main difference between the silver layer and the gold layer in medalion architecture?
- A. Silver is a copy of bronze data
- B. Gold may contain aggregated data
- C. Data quality checks are applied in gold
- D. God is a copy of silver data
- E. Silver may contain aggregated data
正解：B
解説：
Explanation
Medallion Architecture - Databricks
Exam focus: Please review the below image and understand the role of each layer(bronze, silver, gold) in medallion architecture, you will see varying questions targeting each layer and its purpose.
Sorry I had to add the watermark some people in Udemy are copying my content.
A diagram of a house Description automatically generated with low confidence
質問 # 57
A data architect has designed a system in which two Structured Streaming jobs will concurrently write to a single bronze Delta table. Each job is subscribing to a different topic from an Apache Kafka source, but they will write data with the same schema. To keep the directory structure simple, a data engineer has decided to nest a checkpoint directory to be shared by both streams.
The proposed directory structure is displayed below:
Which statement describes whether this checkpoint directory structure is valid for the given scenario and why?
- A. No; only one stream can write to a Delta Lake table.
- B. No; Delta Lake manages streaming checkpoints in the transaction log.
- C. Yes; both of the streams can share a single checkpoint directory.
- D. No; each of the streams needs to have its own checkpoint directory.
- E. Yes; Delta Lake supports infinite concurrent writers.
正解：D
解説：
Explanation
This is the correct answer because checkpointing is a critical feature of Structured Streaming that provides fault tolerance and recovery in case of failures. Checkpointing stores the current state and progress of a streaming query in a reliable storage system, such as DBFS or S3. Each streaming query must have its own checkpoint directory that is unique and exclusive to that query. If two streaming queries share the same checkpoint directory, they will interfere with each other and cause unexpected errors or data loss. Verified References: [Databricks Certified Data Engineer Professional], under "Structured Streaming" section; Databricks Documentation, under "Checkpointing" section.
質問 # 58
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 nameddev-2.3.9is not available from the branch selection dropdown.
Which approach will allow this developer to review the current logic for this notebook?
- A. Use Repos to pull changes from the remote Git repository and select the dev-2.3.9 branch.
- B. Merge all changes back to the main branch in the remote Git repository and clone the repo again
- C. 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
- D. Use Repos to make a pull request use the Databricks REST API to update the current branch to dev-2.3.9
- E. Use Repos to checkout the dev-2.3.9 branch and auto-resolve conflicts with the current branch
正解：A
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 References: [Databricks Certified Data Engineer Professional], under "Databricks Tooling" section; Databricks Documentation, under "Pull changes from a remote repository" section.
質問 # 59
What is the underlying technology that makes the Auto Loader work?
- A. Structured Streaming
- B. Live DataFames
- C. Loader
- D. DataFrames
- E. Delta Live Tables
正解：A
質問 # 60
The data engineer team is configuring environment for development testing, and production before beginning migration on a new data pipeline. The team requires extensive testing on both the code and data resulting from code execution, and the team want to develop and test against similar production data as possible.
A junior data engineer suggests that production data can be mounted to the development testing environments, allowing pre production code to execute against production dat a. Because all users have Admin privileges in the development environment, the junior data engineer has offered to configure permissions and mount this data for the team.
Which statement captures best practices for this situation?
- A. All developer, testing and production code and data should exist in a single unified workspace; creating separate environments for testing and development further reduces risks.
- B. In environments where interactive code will be executed, production data should only be accessible with read permissions; creating isolated databases for each environment further reduces risks.
- C. Because delta Lake versions all data and supports time travel, it is not possible for user error or malicious actors to permanently delete production data, as such it is generally safe to mount production data anywhere.
- D. Because access to production data will always be verified using passthrough credentials it is safe to mount data to any Databricks development environment.
正解：B
解説：
The best practice in such scenarios is to ensure that production data is handled securely and with proper access controls. By granting only read access to production data in development and testing environments, it mitigates the risk of unintended data modification. Additionally, maintaining isolated databases for different environments helps to avoid accidental impacts on production data and systems.
Reference:
Databricks best practices for securing data: https://docs.databricks.com/security/index.html
質問 # 61
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. Use Repos to pull changes from the remote Git repository and select the dev-2.3.9 branch.
- B. Merge all changes back to the main branch in the remote Git repository and clone the repo again
- C. 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
- D. Use Repos to make a pull request use the Databricks REST API to update the current branch to dev-2.3.9
- E. Use Repos to checkout the dev-2.3.9 branch and auto-resolve conflicts with the current branch
正解：A
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 # 62
Which of the following python statements can be used to replace the schema name and table name in the query?
- A. 1.table_name = "sales"
2.query = "select * from {schema_name}.{table_name}" - B. 1.table_name = "sales"
2.query = f"select * from + schema_name +"."+table_name" - C. 1.table_name = "sales"
2.query = f"select * from {schema_name}.{table_name}" - D. 1.table_name = "sales"
2.schema_name = "bronze"
3.query = f"select * from schema_name.table_name"
正解：C
解説：
Explanation
The answer is
1.table_name = "sales"
2.query = f"select * from {schema_name}.{table_name}"
It is always best to use f strings to replace python variables, rather than using string concatenation.
質問 # 63
Which statement regarding stream-static joins and static Delta tables is correct?
- A. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch.
- B. The checkpoint directory will be used to track state information for the unique keys present in the join.
- C. Stream-static joins cannot use static Delta tables because of consistency issues.
- D. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of the job's initialization.
- E. The checkpoint directory will be used to track updates to the static Delta table.
正解：A
解説：
This is the correct answer because stream-static joins are supported by Structured Streaming when one of the tables is a static Delta table. A static Delta table is a Delta table that is not updated by any concurrent writes, such as appends or merges, during the execution of a streaming query. In this case, each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch, which means it will reflect any changes made to the static Delta table before the start of each microbatch. Verified References: [Databricks Certified Data Engineer Professional], under "Structured Streaming" section; Databricks Documentation, under "Stream and static joins" section.
質問 # 64
......
Databricks-Certified-Professional-Data-Engineer試験問題有効なDatabricks-Certified-Professional-Data-Engineer問題集PDF：https://jp.fast2test.com/Databricks-Certified-Professional-Data-Engineer-premium-file.html
Databricks-Certified-Professional-Data-Engineer練習テスト問題解答には更新された122問があります：https://drive.google.com/open?id=1K-ir8jIkdO4Q1vswSqr1ju1kDcB6BgvA