[2025年最新] 最高のDEA-C01試験問題集を使って- 実際の試験問題と解答を解こう
テストエンジンを練習してDEA-C01テスト問題
Snowflake DEA-C01 認定試験の出題範囲：
| トピック | 出題範囲 |
|---|---|
| トピック 1 |
|
| トピック 2 |
|
| トピック 3 |
|
| トピック 4 |
|
| トピック 5 |
|
質問 # 21
A company uses Amazon EMR as an extract, transform, and load (ETL) pipeline to transform data that comes from multiple sources. A data engineer must orchestrate the pipeline to maximize performance.
Which AWS service will meet this requirement MOST cost effectively?
- A. Amazon Managed Workflows for Apache Airflow (Amazon MWAA)
- B. AWS Step Functions
- C. Amazon EventBridge
- D. AWS Glue Workflows
正解：B
解説：
Glue Workflows is for Glue job orchestration. C is for orchestration with different AWS services.
質問 # 22
Which system role is recommended for a custom role hierarchy to be ultimately assigned to?
- A. USERADMIN
- B. ACCOUNTADMIN
- C. SECURITYADMIN
- D. SYSTEMADMIN
正解：C
解説：
Explanation
The system role that is recommended for a custom role hierarchy to be ultimately assigned to is SECURITYADMIN. This role has the manage grants privilege on all objects in an account, which allows it to grant access privileges to other roles or revoke them as needed. This role can also create or modify custom roles and assign them to users or other roles. By assigning custom roles to SECURITYADMIN, the role hierarchy can be managed centrally and securely. The other options are not recommended system roles for a custom role hierarchy to be ultimately assigned to. Option A is incorrect because ACCOUNTADMIN is the most powerful role in an account, which has full access to all objects and operations. Assigning custom roles to ACCOUNTADMIN can pose a security risk and should be avoided. Option C is incorrect because SYSTEMADMIN is a role that has full access to all objects in the public schema of the account, but not to other schemas or databases. Assigning custom roles to SYSTEMADMIN can limit the scope and flexibility of the role hierarchy. Option D is incorrect because USERADMIN is a role that can manage users and roles in an account, but not grant access privileges to other objects. Assigning custom roles to USERADMIN can prevent the role hierarchy from controlling access to data and resources.
質問 # 23
A company stores datasets in JSON format and .csv format in an Amazon S3 bucket. The company has Amazon RDS for Microsoft SQL Server databases, Amazon DynamoDB tables that are in provisioned capacity mode, and an Amazon Redshift cluster. A data engineering team must develop a solution that will give data scientists the ability to query all data sources by using syntax similar to SQL.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Use AWS Glue to crawl the data sources. Store metadata in the AWS Glue Data Catalog. Use Amazon Athena to query the data. Use SQL for structured data sources. Use PartiQL for data that is stored in JSON format.
- B. Use AWS Glue to crawl the data sources. Store metadata in the AWS Glue Data Catalog. Use Redshift Spectrum to query the data. Use SQL for structured data sources. Use PartiQL for data that is stored in JSON format.
- C. Use AWS Lake Formation to create a data lake. Use Lake Formation jobs to transform the data from all data sources to Apache Parquet format. Store the transformed data in an S3 bucket. Use Amazon Athena or Redshift Spectrum to query the data.
- D. Use AWS Glue to crawl the data sources. Store metadata in the AWS Glue Data Catalog. Use AWS Glue jobs to transform data that is in JSON format to Apache Parquet or .csv format. Store the transformed data in an S3 bucket. Use Amazon Athena to query the original and transformed data from the S3 bucket.
正解：A
質問 # 24
To help manage STAGE storage costs, Data engineer recommended to monitor stage files and re-move them from the stages once the data has been loaded and the files which are no longer needed. Which option he can choose to remove these files either during data loading or afterwards?
- A. He can choose to remove stage files during data loading (using the COPY INTO <table> command).
- B. Script can be used during data loading & post data loading with DELETE command.
- C. Files no longer needed, can be removed using the PURGE=TRUE command.
- D. Files no longer needed, can be removed using the REMOVE command.
正解：A、C
解説：
Explanation
Managing Data Files
Staged files can be deleted from a Snowflake stage (user stage, table stage, or named stage) using the following methods:
Files that were loaded successfully can be deleted from the stage during a load by specifying the PURGE copy option in the COPY INTO <table> command.
After the load completes, use the REMOVE command to remove the files in the stage.
Removing files ensures they aren't inadvertently loaded again. It also improves load performance, because it reduces the number of files that COPY commands must scan to verify whether existing files in a stage were loaded already.
質問 # 25
Regular views do not cache data, and therefore cannot improve performance by caching?
- A. FALSE
- B. TRUE
正解：B
解説：
Explanation
Regular views do not cache data, and therefore cannot improve performance by caching.
質問 # 26
A manufacturing company has many IoT devices in facilities around the world. The company uses Amazon Kinesis Data Streams to collect data from the devices. The data includes device ID, capture date, measurement type, measurement value, and facility ID. The company uses facility ID as the partition key.
The company's operations team recently observed many WriteThroughputExceeded exceptions.
The operations team found that some shards were heavily used but other shards were generally idle.
How should the company resolve the issues that the operations team observed?
- A. Archive the data on the producer's side.
- B. Change the partition key from facility ID to a randomly generated key.
- C. Increase the number of shards.
- D. Change the partition key from facility ID to capture date.
正解：B
解説：
The best solution to resolve the issue of uneven shard usage and WriteThroughputExceeded exceptions is to balance the load more evenly across the shards. This can be effectively achieved by changing the partition key to something that ensures a more uniform distribution of data across the shards.
質問 # 27
A Data Engineer is working on a Snowflake deployment in AWS eu-west-1 (Ireland). The Engineer is planning to load data from staged files into target tables using the copy into command Which sources are valid? (Select THREE)
- A. Internal stage on GCP us-central1 (Iowa)
- B. External stage in an Amazon S3 bucket on AWS eu-central 1 (Frankfurt)
- C. Internal stage on AWS eu-central-1 (Frankfurt)
- D. SSO attached to an Amazon EC2 instance on AWS eu-west-1 (Ireland)
- E. External stage in an Amazon S3 bucket on AWS eu-west-1 (Ireland)
- F. External stage on GCP us-central1 (Iowa)
正解：B、E、F
解説：
Explanation
The valid sources for loading data from staged files into target tables using the copy into command are:
External stage on GCP us-central1 (Iowa): This is a valid source because Snowflake supports cross-cloud data loading from external stages on different cloud platforms and regions than the Snowflake deployment.
External stage in an Amazon S3 bucket on AWS eu-west-1 (Ireland): This is a valid source because Snowflake supports data loading from external stages on the same cloud platform and region as the Snowflake deployment.
External stage in an Amazon S3 bucket on AWS eu-central 1 (Frankfurt): This is a valid source because Snowflake supports cross-region data loading from external stages on different regions than the Snowflake deployment within the same cloud platform. The invalid sources are:
Internal stage on GCP us-central1 (Iowa): This is an invalid source because internal stages are always located on the same cloud platform and region as the Snowflake deployment. Therefore, an internal stage on GCP us-central1 (Iowa) cannot be used for a Snowflake deployment on AWS eu-west-1 (Ireland).
Internal stage on AWS eu-central-1 (Frankfurt): This is an invalid source because internal stages are always located on the same region as the Snowflake deployment. Therefore, an internal stage on AWS eu-central-1 (Frankfurt) cannot be used for a Snowflake deployment on AWS eu-west-1 (Ireland).
SSO attached to an Amazon EC2 instance on AWS eu-west-1 (Ireland): This is an invalid source because SSO stands for Single Sign-On, which is a security integration feature in Snowflake, not a data staging option.
質問 # 28
Data Engineer is performing below steps in sequence while working on Stream s1 created on table t1.
Step 1: Begin transaction.
Step 2: Query stream s1 on table t1.
Step 3: Update rows in table t1.
Step 4: Query stream s1.
Step 5: Commit transaction.
Step 6: Begin transaction.
Step 7: Query stream s1.
Mark the Incorrect Operational statements:
- A. For Step 4, Returns the CDC data records by streams with updated rows happened in the Step 3 because Streams works in Repeated committed mode in which statements see any changes made by previous statements executed within the same transaction, even though those changes are not yet committed.
- B. For Step 2, The stream returns the change data capture records between the current position to the Transaction 1 start time. If the stream is used in a DML statement, the stream is then locked to avoid changes by concurrent transactions.
- C. For Step 7, Results do include table changes committed by Transaction 1.
- D. For Step 5, If the stream was consumed in DML statements within the transaction, the stream position advances to the transaction start time.
- E. if Transaction 2 had begun before Transaction 1 was committed, queries to the stream would have returned a snapshot of the stream from the position of the stream to the be-ginning time of Transaction 2 and would not see any changes committed by Transac-tion 1.
正解：A
解説：
Explanation
Streams support repeatable read isolation. In repeatable read mode, multiple SQL statements within a transaction see the same set of records in a stream. This differs from the read committed mode supported for tables, in which statements see any changes made by previous statements executed within the same transaction, even though those changes are not yet committed.
The delta records returned by streams in a transaction is the range from the current position of the stream until the transaction start time. The stream position advances to the transaction start time if the transaction commits; otherwise, it stays at the same position.
Within Transaction 1, all queries to stream s1 see the same set of records. DML changes to table t1 are recorded to the stream only when the transaction is committed.
In Transaction 2, queries to the stream see the changes recorded to the table in Transaction 1. Note that if Transaction 2 had begun before Transaction 1 was committed, queries to the stream would have returned a snapshot of the stream from the position of the stream to the beginning time of Transaction 2 and would not see any changes committed by Transaction 1.
質問 # 29
A data engineer notices that Amazon Athena queries are held in a queue before the queries run.
How can the data engineer prevent the queries from queueing?
- A. Configure provisioned capacity for an existing workgroup.
- B. Allow users who run the Athena queries to an existing workgroup.
- C. Increase the query result limit.
- D. Use federated queries.
正解：A
解説：
https://aws.amazon.com/blogs/aws/introducing-athena-provisioned-capacity/
質問 # 30
Snowflake does not treat the inner transaction as nested; instead, the inner transaction is a separate transaction.
What is term used to call these Transaction?
- A. Enclosed Transaction
- B. Inner Transaction
- C. Nested Scope Transaction
- D. Scoped transactions
- E. Atomic Transaction
正解：D
質問 # 31
A media company wants to improve a system that recommends media content to customer based on user behavior and preferences. To improve the recommendation system, the company needs to incorporate insights from third-party datasets into the company's existing analytics platform.
The company wants to minimize the effort and time required to incorporate third-party datasets.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Use API calls to access and integrate third-party datasets from AWS DataSync.
- B. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from Amazon Elastic Container Registry (Amazon ECR).
- C. Use API calls to access and integrate third-party datasets from AWS Data Exchange.
- D. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from AWS CodeCommit repositories.
正解：A
解説：
Data Exchange is the AWS official third-party datasets repository:
https://aws.amazon.com/data-exchange
質問 # 32
While running an external function, me following error message is received:
Error:function received the wrong number of rows
What iscausing this to occur?
- A. The JSON returned by the remote service is not constructed correctly
- B. Nested arrays are not supported in the JSON response
- C. The return message did not produce the same number of rows that it received
- D. External functions do not support multiple rows
正解：C
解説：
Explanation
The error message "function received the wrong number of rows" is caused by the return message not producing the same number of rows that it received. External functions require that the remote service returns exactly one row for each input row that it receives from Snowflake. If the remote service returns more or fewer rows than expected, Snowflake will raise an error and abort the function execution. The other options are not causes of this error message. Option A is incorrect because external functions do support multiple rows as long as they match the input rows. Option B is incorrect because nested arrays are supported in the JSON response as long as they conform to the return type definition of the external function. Option C is incorrect because the JSON returned by the remote service may be constructed correctly but still produce a different number of rows than expected.
質問 # 33
Data Engineer Loading File named snowdata.tsv in the /datadir directory from his local machine to Snowflake stage and try to prefix the file with a folder named tablestage, please mark the correct command which helps him to load the files data into snowflake internal Table stage?
- A. put file:///datadir/snowdata.tsv @%tablestage;
- B. put file://c:\datadir\snowdata.tsv @~/tablestage;
- C. put file://c:\datadir\snowdata.tsv @tablestage;
- D. put file://c:\datadir\snowdata.tsv @%tablestage;
正解：D
解説：
Explanation
Execute PUT to upload (stage) local data files into an internal stage.
@% character combination identifies a table stage.
質問 # 34
What is the primary purpose of data lineage in data engineering?
- A. To transform data formats.
- B. To create visualizations.
- C. To trace the source and flow of data.
- D. To optimize query performance.
正解：C
質問 # 35
When processing large datasets using distributed computing frameworks, uneven distribution of data can lead to processing delays. What is this phenomenon commonly known as?
- A. Data fragmentation
- B. Data shuffling
- C. Data partitioning
- D. Data skew
正解：D
質問 # 36
A data engineering team is using an Amazon Redshift data warehouse for operational reporting.
The team wants to prevent performance issues that might result from long- running queries. A data engineer must choose a system table in Amazon Redshift to record anomalies when a query optimizer identifies conditions that might indicate performance issues.
Which table views should the data engineer use to meet this requirement?
- A. STL_QUERY_METRICS
- B. STL_PLAN_INFO
- C. STL_USAGE_CONTROL
- D. STL_ALERT_EVENT_LOG
正解：D
解説：
https://docs.aws.amazon.com/redshift/latest/dg/cm_chap_system-tables.html STL_ALERT_EVENT_LOG table view to meet this requirement. This system table in Amazon Redshift is designed to record anomalies when a query optimizer identifies conditions that might indicate performance issues.
質問 # 37
Which methods can be used to create a DataFrame object in Snowpark? (Select THREE)
- A. DataFraas.writeO
- B. session.builder()
- C. session.sql()
- D. session.jdbc_connection()
- E. session,table()
- F. session.read.json{)
正解：C、E、F
解説：
Explanation
The methods that can be used to create a DataFrame object in Snowpark are session.read.json(), session.table(), and session.sql(). These methods can create a DataFrame from different sources, such as JSON files, Snowflake tables, or SQL queries. The other options are not methods that can create a DataFrame object in Snowpark. Option A, session.jdbc_connection(), is a method that can create a JDBC connection object to connect to a database. Option D, DataFrame.write(), is a method that can write a DataFrame to a destination, such as a file or a table. Option E, session.builder(), is a method that can create a SessionBuilder object to configure and build a Snowpark session.
質問 # 38
A Data Engineer is evaluating the performance of a query in a development environment.
Based on the Query Profile what are some performance tuning options the Engineer can use? (Select TWO)
- A. Use a multi-cluster virtual warehouse with the scaling policy set to standard
- B. Move the query to a larger virtual warehouse
- C. Add a LIMIT to the ORDER BY If possible
- D. Increase the max cluster count
- E. Create indexes to ensure sorted access to data
正解：B、C
解説：
Explanation
The performance tuning options that the Engineer can use based on the Query Profile are:
Add a LIMIT to the ORDER BY If possible: This option will improve performance by reducing the amount of data that needs to be sorted and returned by the query. The ORDER BY clause requires sorting all rows in the input before returning them, which can be expensive and time-consuming. By adding a LIMIT clause, the query can return only a subset of rows that satisfy the order criteria, which can reduce sorting time and network transfer time.
Create indexes to ensure sorted access to data: This option will improve performance by reducing the amount of data that needs to be scanned and filtered by the query. The query contains several predicates on different columns, such as o_orderdate, o_orderpriority, l_shipmode, etc. By creating indexes on these columns, the query can leverage sorted access to data and prune unnecessary micro-partitions or rows that do not match the predicates. This can reduce IO time and processing time.
The other options are not optimal because:
Use a multi-cluster virtual warehouse with the scaling policy set to standard: This option will not improve performance, as the query is already using a multi-cluster virtual warehouse with the scaling policy set to standard. The Query Profile shows that the query is using a 2XL warehouse with 4 clusters and a standard scaling policy, which means that the warehouse can automatically scale up or down based on the load. Changing the warehouse size or the number of clusters will not affect the performance of this query, as it is already using the optimal resources.
Increase the max cluster count: This option will not improve performance, as the query is not limited by the max cluster count. The max cluster count is a parameter that specifies the maximum number of clusters that a multi-cluster virtual warehouse can scale up to. The Query Profile shows that the query is using a 2XL warehouse with 4 clusters and a standard scaling policy, which means that the warehouse can automatically scale up or down based on theload. The default max cluster count for a 2XL warehouse is 10, which means that the warehouse can scale up to 10 clusters if needed. However, the query does not need more than 4 clusters, as it is not CPU-bound or memory-bound. Increasing the max cluster count will not affect the performance of this query, as it will not use more clusters than necessary.
質問 # 39
The following code is executed ina Snowflake environment with the default settings:
What will be the result of the select statement?
- A. 0
- B. SQL compilation error object CUSTOMER' does not exist or is not authorized.
- C. John
- D. 1John
正解：A
質問 # 40
Can the same column be specified in both a Dynamic data masking policy signature and a row ac-cess policy signature at the same time?
- A. YES
- B. NO
正解：B
質問 # 41
A retail company uses Amazon Aurora PostgreSQL to process and store live transactional data.
The company uses an Amazon Redshift cluster for a data warehouse.
An extract, transform, and load (ETL) job runs every morning to update the Redshift cluster with new data from the PostgreSQL database. The company has grown rapidly and needs to cost optimize the Redshift cluster.
A data engineer needs to create a solution to archive historical data. The data engineer must be able to run analytics queries that effectively combine data from live transactional data in PostgreSQL, current data in Redshift, and archived historical data. The solution must keep only the most recent 15 months of data in Amazon Redshift to reduce costs.
Which combination of steps will meet these requirements? (Choose two.)
- A. Configure Amazon Redshift Spectrum to query live transactional data that is in the PostgreSQL database.
- B. Schedule a monthly job to copy data that is older than 15 months to Amazon S3 by using the UNLOAD command. Delete the old data from the Redshift cluster. Configure Amazon Redshift Spectrum to access historical data in Amazon S3.
- C. Schedule a monthly job to copy data that is older than 15 months to Amazon S3 Glacier Flexible Retrieval by using the UNLOAD command. Delete the old data from the Redshift cluster. Configure Redshift Spectrum to access historical data from S3 Glacier Flexible Retrieval.
- D. Create a materialized view in Amazon Redshift that combines live, current, and historical data from different sources.
- E. Configure the Amazon Redshift Federated Query feature to query live transactional data that is in the PostgreSQL database.
正解：B、E
解説：
Choice A ensures that live transactional data from PostgreSQL can be accessed directly within Redshift queries.
Choice C archives historical data in Amazon S3, reducing storage costs in Redshift while still making the data accessible via Redshift Spectrum.
質問 # 42
......
DEA-C01実際の問題アンサーPDFには100%カバー率リアルな試験問題：https://www.goshiken.com/Snowflake/DEA-C01-mondaishu.html
DEA-C01リアルな試験問題テストエンジン問題集トレーニング132問題：https://drive.google.com/open?id=1enXBRgYUh81EYblvX5aeYKOXRKckKQEr