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GAQM Databricks-Certified-Data-Engineer-Associate（DataBricks Certified Data Engineer Associate）認定試験は、データエンジニアリングタスクにDatabricksを使用する個人の知識とスキルをテストするように設計されています。この認定は、業界で高度に認識され、尊敬されており、採用担当者と雇用主がDatabricksを使用したデータエンジニアリングの能力を評価するためのベンチマークとして使用できます。
質問 # 21
Which of the following benefits of using the Databricks Lakehouse Platform is provided by Delta Lake?
- A. The ability to set up alerts for query failures
- B. The ability to distribute complex data operations
- C. The ability to manipulate the same data using a variety of languages
- D. The ability to collaborate in real time on a single notebook
- E. The ability to support batch and streaming workloads
正解：E
解説：
Delta Lake is the optimized storage layer that provides the foundation for storing data and tables in the Databricks lakehouse. Delta Lake is fully compatible with Apache Spark APIs, and was developed for tight integration with Structured Streaming, allowing you to easily use a single copy of data for both batch and streaming operations and providing incremental processing at scale1. Delta Lake supports upserts using the merge operation, which enables you to efficiently update existing data or insert new data into your Delta tables2. Delta Lake also provides time travel capabilities, which allow you to query previous versions of your data or roll back to a specific point in time3. References: 1: What is Delta Lake? | Databricks on AWS 2: Upsert into a table using merge | Databricks on AWS 3: [Query an older snapshot of a table (time travel) | Databricks on AWS] Learn more
1blob:https://www.bing.com/a746b4b4-48d0-4f44-9736-44d1ce0c4228
learn.microsoft.com2blob:https://www.bing.com/525fbb0f-e02f-4a70-8085-22c065fe0ca0 medium.com3blob:https://www.bing.com/5cb5bd07-1008-4cf7-9fa3-42a5a689c7d5 slideshare.net4blob:https://www.bing.com/9a7e8352-30c1-4356-a73f-a7253b607ef7 docs.databricks.com5blob:https://www.bing.com/3f65cc27-d573-4810-b272-01238a431c03 github.com6blob:https://www.bing.com/334f6880-dfeb-4e61-bd9a-76efae0a2d01 key2consulting.com
質問 # 22
Which of the following is stored in the Databricks customer's cloud account?
- A. Notebooks
- B. Databricks web application
- C. Repos
- D. Cluster management metadata
- E. Data
正解：E
質問 # 23
A team creates YAML manifests that declare jobs, resources, and dependencies, then deploys them to Databricks using the Databricks CLI. The deployment succeeds.
Which feature are they using?
- A. DataOps
- B. Terraform
- C. GitHub
- D. Databricks Asset Bundles
正解：D
解説：
Databricks Asset Bundles are a Databricks CLI feature that lets teams define Databricks projects "as code" using YAML configuration files. In a bundle, metadata is expressed in YAML (for example, a required databricks.yml at the root), and the configuration can declare resources (such as Databricks Jobs/workflows and other supported assets), artifacts, and deployment targets. After defining these YAML manifests, teams use the Databricks CLI to validate, deploy, and run the bundle into a Databricks workspace, which matches the scenario described (YAML + CLI deployment). This approach standardizes how workflows and related resources are packaged and promoted across environments (dev/test/prod) while keeping definitions version-controlled and reproducible. GitHub is only a hosting/version-control platform (not the YAML deployment feature itself), Terraform is a separate IaC tool, and "DataOps" is a methodology rather than a specific Databricks feature. (Databricks Documentation)
質問 # 24
Which of the following describes the relationship between Bronze tables and raw data?
- A. Bronze tables contain less data than raw data files.
- B. Bronze tables contain aggregates while raw data is unaggregated.
- C. Bronze tables contain more truthful data than raw data.
- D. Bronze tables contain raw data with a schema applied.
- E. Bronze tables contain a less refined view of data than raw data.
正解：D
解説：
Explanation
The Bronze layer is where we land all the data from external source systems. The table structures in this layer correspond to the source system table structures "as-is," along with any additional metadata columns that capture the load date/time, process ID, etc. The focus in this layer is quick Change Data Capture and the ability to provide an historical archive of source (cold storage), data lineage, auditability, reprocessing if needed without rereading the data from the source system.https://www.databricks.com/glossary/medallion-architecture#:~:text=Bronze%20layer%20%28raw%20d
質問 # 25
A data engineer needs to determine whether to use the built-in Databricks Notebooks versioning or version their project using Databricks Repos.
Which of the following is an advantage of using Databricks Repos over the Databricks Notebooks versioning?
- A. Databricks Repos supports the use of multiple branches
- B. Databricks Repos is wholly housed within the Databricks Lakehouse Platform
- C. Databricks Repos automatically saves development progress
- D. Databricks Repos allows users to revert to previous versions of a notebook
- E. Databricks Repos provides the ability to comment on specific changes
正解：A
解説：
Databricks Repos is a visual Git client and API in Databricks that supports common Git operations such as cloning, committing, pushing, pulling, and branch management. Databricks Notebooks versioning is a legacy feature that allows users to link notebooks to GitHub repositories and perform basic Git operations. However, Databricks Notebooks versioning does not support the use of multiple branches for development work, which is an advantage of using Databricks Repos. With Databricks Repos, users can create and manage branches for different features, experiments, or bug fixes, and merge, rebase, or resolve conflicts between them. Databricks recommends using a separate branch for each notebook and following data science and engineering code development best practices using Git for version control, collaboration, and CI/CD. Reference: Git integration with Databricks Repos - Azure Databricks | Microsoft Learn, Git version control for notebooks (legacy) | Databricks on AWS, Databricks Repos Is Now Generally Available - New 'Files' Feature in ..., Databricks Repos - What it is and how we can use it | Adatis.
質問 # 26
A data engineer wants to create a new table containing the names of customers that live in France.
They have written the following command:
A senior data engineer mentions that it is organization policy to include a table property indicating that the new table includes personally identifiable information (PII).
Which of the following lines of code fills in the above blank to successfully complete the task?
- A. TBLPROPERTIES PII
- B. There is no way to indicate whether a table contains PII.
- C. PII
- D. COMMENT "Contains PII"
- E. "COMMENT PII"
正解：D
解説：
In Databricks, when creating a table, you can add a comment to columns or the entire table to provide more information about the data it contains. In this case, since it's organization policy to indicate that the new table includes personally identifiable information (PII), option D is correct. The line of code would be added after defining the table structure and before closing with a semicolon. References: Data Engineer Associate Exam Guide, CREATE TABLE USING (Databricks SQL)
質問 # 27
A data engineering team has noticed that their Databricks SQL queries are running too slowly when they are submitted to a non-running SQL endpoint. The data engineering team wants this issue to be resolved.
Which of the following approaches can the team use to reduce the time it takes to return results in this scenario?
- A. They can increase the cluster size of the SQL endpoint.
- B. They can increase the maximum bound of the SQL endpoint's scaling range
- C. They can turn on the Serverless feature for the SQL endpoint.
- D. They can turn on the Auto Stop feature for the SQL endpoint.
- E. They can turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to "Reliability Optimized."
正解：C
解説：
Option D is the correct answer because it enables the Serverless feature for the SQL endpoint, which allows the endpoint to automatically scale up and down based on the query load. This way, the endpoint can handle more concurrent queries and reduce the time it takes to return results. The Serverless feature also reduces the cold start time of the endpoint, which is the time it takes to start the cluster when a query is submitted to a non-running endpoint. The Serverless feature is available for both AWS and Azure Databricks platforms.
質問 # 28
In which of the following scenarios should a data engineer use the MERGE INTO command instead of the INSERT INTO command?
- A. When the location of the data needs to be changed
- B. When the source table can be deleted
- C. When the target table cannot contain duplicate records
- D. When the source is not a Delta table
- E. When the target table is an external table
正解：C
解説：
The MERGE INTO command is used to perform upserts, which are a combination of insertions and updates, based on a source table into a target Delta table1. The MERGE INTO command can handle scenarios where the target table cannot contain duplicate records, such as when there is a primary key or a unique constraint on the target table. The MERGE INTO command can match the source and target rows based on a merge condition and perform different actions depending on whether the rows are matched or not. For example, the MERGE INTO command can update the existing target rows with the new source values, insert the new source rows that do not exist in the target table, or delete the target rows that do not exist in the source table1.
The INSERT INTO command is used to append new rows to an existing table or create a new table from a query result2. The INSERT INTO command does not perform any updates or deletions on the existing target table rows. The INSERT INTO command can handle scenarios where the location of the data needs to be changed, such as when the data needs to be moved from one table to another, or when the data needs to be partitioned by a certain column2. The INSERT INTO command can also handle scenarios where the target table is an external table, such as when the data is stored in an external storage system like Amazon S3 or Azure Blob Storage3. The INSERT INTO command can also handle scenarios where the source table can be deleted, such as when the source table is a temporary table or a view4. The INSERT INTO command can also handle scenarios where the source is not a Delta table, such as when the source is a Parquet, CSV, JSON, or Avro file5.
Reference:
1: MERGE INTO | Databricks on AWS
2: [INSERT INTO | Databricks on AWS]
3: [External tables | Databricks on AWS]
4: [Temporary views | Databricks on AWS]
5: [Data sources | Databricks on AWS]
質問 # 29
Identify a scenario to use an external table.
A Data Engineer needs to create a parquet bronze table and wants to ensure that it gets stored in a specific path in an external location.
Which table can be created in this scenario?
- A. A managed table where the catalog has managed location pointing to specific path in external location.
- B. A managed table where the location is pointing to specific path in external location.
- C. An external table where the location is pointing to specific path in external location.
- D. An external table where the schema has managed location pointing to specific path in external location.
正解：C
質問 # 30
Which of the following code blocks will remove the rows where the value in column age is greater than 25 from the existing Delta table my_table and save the updated table?
- A. DELETE FROM my_table WHERE age <= 25;
- B. SELECT * FROM my_table WHERE age > 25;
- C. UPDATE my_table WHERE age > 25;
- D. UPDATE my_table WHERE age <= 25;
- E. DELETE FROM my_table WHERE age > 25;
正解：E
解説：
The DELETE command in Delta Lake allows you to remove data that matches a predicate from a Delta table. This command will delete all the rows where the value in the column age is greater than 25 from the existing Delta table my_table and save the updated table. The other options are either incorrect or do not achieve the desired result. Option A will only select the rows that match the predicate, but not delete them. Option B will update the rows that match the predicate, but not delete them. Option D will update the rows that do not match the predicate, but not delete them. Option E will delete the rows that do not match the predicate, which is the opposite of what we want. Reference: Table deletes, updates, and merges - Delta Lake Documentation
質問 # 31
Which of the following benefits of using the Databricks Lakehouse Platform is provided by Delta Lake?
- A. The ability to set up alerts for query failures
- B. The ability to distribute complex data operations
- C. The ability to manipulate the same data using a variety of languages
- D. The ability to collaborate in real time on a single notebook
- E. The ability to support batch and streaming workloads
正解：E
解説：
Delta Lake is the optimized storage layer that provides the foundation for storing data and tables in the Databricks lakehouse. Delta Lake is fully compatible with Apache Spark APIs, and was developed for tight integration with Structured Streaming, allowing you to easily use a single copy of data for both batch and streaming operations and providing incremental processing at scale1. Delta Lake supports upserts using the merge operation, which enables you to efficiently update existing data or insert new data into your Delta tables2. Delta Lake also provides time travel capabilities, which allow you to query previous versions of your data or roll back to a specific point in time3. Reference: 1: What is Delta Lake? | Databricks on AWS 2: Upsert into a table using merge | Databricks on AWS 3: [Query an older snapshot of a table (time travel) | Databricks on AWS] Learn more
質問 # 32
A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE. Three datasets are defined against Delta Lake table sources using LIVE TABLE.
The table is configured to run in Production mode using the Continuous Pipeline Mode.
Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after clicking Start to update the pipeline?
- A. All datasets will be updated once and the pipeline will persist without any processing. The compute resources will persist but go unused.
- B. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.
- C. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will be deployed for the update and terminated when the pipeline is stopped.
- D. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.
- E. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.
正解：C
解説：
1: In Production mode, the pipeline runs continuously and updates the output tables whenever new data is available in the input sources. The compute resources are allocated on demand and released when the pipeline is stopped. This mode is suitable for production workloads that require high availability and reliability. Reference: Configure pipeline settings for Delta Live Tables, Tutorial: Run your first Delta Live Tables pipeline, Building Reliable Data Pipelines Using DataBricks' Delta Live Tables
質問 # 33
Which of the following describes the relationship between Gold tables and Silver tables?
- A. Gold tables are more likely to contain more data than Silver tables.
- B. Gold tables are more likely to contain aggregations than Silver tables.
- C. Gold tables are more likely to contain valuable data than Silver tables.
- D. Gold tables are more likely to contain truthful data than Silver tables.
- E. Gold tables are more likely to contain a less refined view of data than Silver tables.
正解：E
質問 # 34
Which of the following must be specified when creating a new Delta Live Tables pipeline?
- A. A key-value pair configuration
- B. A location of a target database for the written data
- C. The preferred DBU/hour cost
- D. A path to cloud storage location for the written data
- E. At least one notebook library to be executed
正解：E
解説：
Explanation
https://docs.databricks.com/en/delta-live-tables/tutorial-pipelines.html
質問 # 35
Which of the following benefits of using the Databricks Lakehouse Platform is provided by Delta Lake?
- A. The ability to set up alerts for query failures
- B. The ability to distribute complex data operations
- C. The ability to manipulate the same data using a variety of languages
- D. The ability to collaborate in real time on a single notebook
- E. The ability to support batch and streaming workloads
正解：E
質問 # 36
A data engineer is designing a data pipeline. The source system generates files in a shared directory that is also used by other processes. As a result, the files should be kept as is and will accumulate in the directory. The data engineer needs to identify which files are new since the previous run in the pipeline, and set up the pipeline to only ingest those new files with each run.
Which of the following tools can the data engineer use to solve this problem?
- A. Auto Loader
- B. Unity Catalog
- C. Delta Lake
- D. Data Explorer
- E. Databricks SQL
正解：A
質問 # 37
A data engineer needs to process SQL queries on a large dataset with fluctuating workloads. The workload requires automatic scaling based on the volume of queries, without the need to manage or provision infrastructure. The solution should be cost-efficient and charge only for the compute resources used during query execution.
Which compute option should the data engineer use?
- A. Serverless SQL Warehouse
- B. Databricks Jobs
- C. Databricks Runtime for ML
- D. Databricks SQL Analytics
正解：A
質問 # 38
Which of the following SQL keywords can be used to convert a table from a long format to a wide format?
- A. WHERE
- B. TRANSFORM
- C. CONVERT
- D. PIVOT
- E. SUM
正解：D
解説：
The SQL keyword that can be used to convert a table from a long format to a wide format is PIVOT. The PIVOT clause is used to rotate the rows of a table into columns of a new table1. The PIVOT clause can aggregate the values of a column based on the distinct values of another column, and use those values as the column names of the new table1. The PIVOT clause can be useful for transforming data from a long format, where each row represents an observation with multiple attributes, to a wide format, where each row represents an observation with a single attribute and multiple values2. For example, the PIVOT clause can be used to convert a table that contains the sales of different products by different regions into a table that contains the sales of each product by each region as separate columns1.
The other options are not suitable for converting a table from a long format to a wide format. CONVERT is a function that can be used to change the data type of an expression3. WHERE is a clause that can be used to filter the rows of a table based on a condition4. TRANSFORM is a keyword that can be used to apply a user- defined function to a group of rows in a table5. SUM is a function that can be used to calculate the total of a numeric column.
:
1: PIVOT | Databricks on AWS
2: Reshaping Data - Long vs Wide Format | Databricks on AWS
3: CONVERT | Databricks on AWS
4: WHERE | Databricks on AWS
5: TRANSFORM | Databricks on AWS
6: [SUM | Databricks on AWS]
質問 # 39
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Databricks-Certified-Data-Engineer-Associate試験の準備には、Databricks及びその様々なコンポーネントに対する深い理解が必要です。候補者は、Spark SQL、Spark Streaming、Spark MLlibに関する経験を持ち、データモデリング、データ構造、アルゴリズムの確固たる理解を持っている必要があります。また、Hadoop、Kafka、AWS S3などの一般的なデータストレージや処理技術にも精通している必要があります。
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