2025年最新の有効なDEA-C01試験最新問題で2025年最新の学習ガイド
DEA-C01認定で究極のガイド [2025年更新]
Snowflake DEA-C01 認定試験の出題範囲：
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質問 # 68
A Data Engineer is trying to load the following rows from a CSV file into a table in Snowflake with the following structure:
....engineer is using the following COPY INTO statement:
However, the following error is received.
Which file format option should be used to resolve the error and successfully load all the data into the table?
- A. FIELD OPTIONALLY ENCLOSED BY = " "
- B. ERROR_ON_COLUMN_COUKT_MISMATCH = FALSE
- C. FIELD_DELIMITER = ","
- D. ESC&PE_UNENGLO9ED_FIELD = '\\'
正解：A
解説：
Explanation
The file format option that should be used to resolve the error and successfully load all the data into the table is FIELD_OPTIONALLY_ENCLOSED_BY = '"'. This option specifies that fields in the file may be enclosed by double quotes, which allows for fields that contain commas or newlines within them. For example, in row 3 of the file, there is a field that contains a comma within double quotes: "Smith Jr., John". Without specifying this option, Snowflake will treat this field as two separate fields and cause an error due to column count mismatch. By specifying this option, Snowflake will treat this field as one field and load it correctly into the table.
質問 # 69
Company DEF has a strict security policy that mandates that all data at rest in Amazon S3 must be encrypted. They want to ensure that the encryption keys are managed by AWS, but they also want the flexibility to change the encryption keys when required.
Which of the following encryption methods best meets Company DEF's requirements?
- A. Server-Side Encryption with Amazon S3 Managed Keys (SSE-S3).
- B. Server-Side Encryption with AWS Key Management Service (SSE-KMS).
- C. Server-Side Encryption with Customer-Provided Keys (SSE-C).
- D. Client-Side Encryption with a client-side master key.
正解：B
質問 # 70
A Data Engineer is implementing a near real-time ingestionpipeline to toad data into Snowflake using the Snowflake Kafka connector. There will be three Kafka topics created.
......snowflake objects are created automatically when the Kafka connector starts? (Select THREE)
- A. Materialized views
- B. Pipes
- C. Tasks
- D. External stages
- E. internal stages
- F. Tables
正解：B、E、F
解説：
Explanation
The Snowflake objects that are created automatically when the Kafka connector starts are tables, pipes, and internal stages. The Kafka connector will create one table, one pipe, and oneinternal stage for each Kafka topic that is configured in the connector properties. The table will store the data from the Kafka topic, the pipe will load the data from the stage to the table using COPY statements, and the internal stage will store the files that are produced by the Kafka connector using PUT commands. The other options are not Snowflake objects that are created automatically when the Kafka connector starts. Option B, tasks, are objects that can execute SQL statements on a schedule without requiring a warehouse. Option E, external stages, are objects that can reference locations outside of Snowflake, such as cloud storage services. Option F, materialized views, are objects that can store the precomputed results of a query and refresh them periodically.
質問 # 71
Pascal, a Data Engineer, have requirement to retrieve the 10 most recent executions of a specified task (completed, still running, or scheduled in the future) scheduled within the last hour, which of the following is the correct SQL Code ?
- A. 1.select *
2.from table(information_schema.task_history(
3.scheduled_time_range_start=>dateadd('hour',-1,current_timestamp()),
4.result_limit => 10,
5.task_name=>'MYTASK')); - B. 1.select *
2.from table(information_schema.task_history(
3.scheduled_time_range_start=>dateadd('hour',-1,current_timestamp()),
4.result_limit => 10,query_id IS NOT NULL
5.task_name=>'MYTASK')); - C. 1.select *
2.from table(information_schema.task_history(
3.scheduled_time_range_start=>dateadd('hour',-1,current_timestamp()),
4.result_limit => 11,
5.task_name=>'MYTASK') WHERE query_id IS NOT NULL); - D. 1.select *
2.from table(information_schema.task_history(
3.scheduled_time_range_start=>dateadd('hour',-1,current_timestamp()),
4.result_limit => 10,
5.task_name=>'MYTASK') WHERE query_id IS NOT NULL);
正解：A
解説：
Explanation
To retrieve only tasks that are completed or still running, filter the query using WHERE query_id IS NOT NULL.
質問 # 72
As a Data Engineer, you have requirement to query most recent data from the Large Dataset that reside in the external cloud storage, how would you design your data pipelines keeping in mind fastest time to delivery?
- A. Unload data into SnowFlake Internal data storage using PUT command.
- B. Data pipelines would be created to first load data into internal stages & then into Per-manent table with SCD Type 2 transformation.
- C. Direct Querying External tables on top of existing data stored in external cloud storage for analysis without first loading it into Snowflake.
- D. External tables with Materialized views can be created in Snowflake.
- E. Snowpipe can be leveraged with streams to load data in micro batch fashion with CDC streams that capture most recent data only.
正解：D
解説：
Explanation
In a typical table, the data is stored in the database; however, in an external table, the data is stored in files in an external stage. External tables store file-level metadata about the data files, such as the filename, a version identifier and related properties. This enables querying data stored in files in an external stage as if it were inside a database. External tables can access data stored in any format supported by COPY INTO <table> statements.
External tables are read-only, therefore no DML operations can be performed on them; however, external tables can be used for query and join operations. Views can be created against external ta-bles.
Querying data stored external to the database is likely to be slower than querying native database tables; however, materialized views based on external tables can improve query performance.
Creating External tables enable user for querying existing data stored in external cloud storage for analysis without first loading it into Snowflake. The source of truth for the data remains in the ex-ternal cloud storage.
Data sets materialized in Snowflake via materialized views are read-only.
This solution is especially beneficial to accounts that have a large amount of data stored in external cloud storage and only want to query a portion of the data; for example, the most recent data. Users can create materialized views on subsets of this data for improved query performance.
質問 # 73
A data engineer needs to maintain a central metadata repository that users access through Amazon EMR and Amazon Athena queries. The repository needs to provide the schema and properties of many tables. Some of the metadata is stored in Apache Hive. The data engineer needs to import the metadata from Hive into the central metadata repository.
Which solution will meet these requirements with the LEAST development effort?
- A. Use Amazon EMR and Apache Ranger.
- B. Use a metastore on an Amazon RDS for MySQL DB instance.
- C. Use the AWS Glue Data Catalog.
- D. Use a Hive metastore on an EMR cluster.
正解：C
解説：
https://aws.amazon.com/blogs/big-data/metadata-classification-lineage-and-discovery-using- apache-atlas-on-amazon-emr/
質問 # 74
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. Files no longer needed, can be removed using the PURGE=TRUE command.
- B. Files no longer needed, can be removed using the REMOVE command.
- C. Script can be used during data loading & post data loading with DELETE command.
- D. He can choose to remove stage files during data loading (using the COPY INTO <table> command).
正解：A、D
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 # 75
Which methods will trigger an action that will evaluate a DataFrame? (Select TWO)
- A. DataFrame.show ()
- B. DataFrame.col ( )
- C. DateFrame.select ()
- D. DataFrame.random_split ( )
- E. DataFrame.collect ()
正解：A、E
解説：
Explanation
The methods that will trigger an action that will evaluate a DataFrame are DataFrame.collect() and DataFrame.show(). These methods will force the execution of any pending transformations on the DataFrame and return or display the results. The other options are not methods that will evaluate a DataFrame. Option A, DataFrame.random_split(), is a method that will split a DataFrame into two or more DataFrames based on random weights. Option C, DataFrame.select(), is a method that will project a set of expressions on a DataFrame and return a new DataFrame. Option D, DataFrame.col(), is a method that will return a Column object based on a column name in a DataFrame.
質問 # 76
Which methods can be used to create a DataFrame object in Snowpark? (Select THREE)
- A. session.sql()
- B. session.builder()
- C. session.read.json{)
- D. session,table()
- E. DataFraas.writeO
- F. session.jdbc_connection()
正解：A、C、D
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 # 77
Which connector creates the RECORD_CONTENT and RECORD_METADATA columns in the existing Snowflake table while connecting to Snowflake?
- A. Spark Connector
- B. Python Connector
- C. Kafka Connector
- D. Node.js connector
正解：C
解説：
Explanation
Apache Kafka software uses a publish and subscribe model to write and read streams of records, similar to a message queue or enterprise messaging system. Kafka allows processes to read and write messages asynchronously. A subscriber does not need to be connected directly to a publisher; a pub-lisher can queue a message in Kafka for the subscriber to receive later.
An application publishes messages to a topic, and an application subscribes to a topic to receive those messages. Kafka can process, as well as transmit, messages; however, that is outside the scope of this document. Topics can be divided into partitions to increase scalability.
Kafka Connect is a framework for connecting Kafka with external systems, including databases. A Kafka Connect cluster is a separate cluster from the Kafka cluster. The Kafka Connect cluster sup-ports running and scaling out connectors (components that support reading and/or writing between external systems).
The Kafka connector is designed to run in a Kafka Connect cluster to read data from Kafka topics and write the data into Snowflake tables.
Every Snowflake table loaded by the Kafka connector has a schema consisting of two VARIANT columns:
RECORD_CONTENT. This contains the Kafka message.
RECORD_METADATA. This contains metadata about the message, for example, the topic from which the message was read.
質問 # 78
A data engineer is building a data orchestration workflow. The data engineer plans to use a hybrid model that includes some on-premises resources and some resources that are in the cloud. The data engineer wants to prioritize portability and open source resources.
Which service should the data engineer use in both the on-premises environment and the cloud- based environment?
- A. Amazon Simple Workflow Service (Amazon SWF)
- B. Amazon Managed Workflows for Apache Airflow (Amazon MWAA)
- C. AWS Glue
- D. AWS Data Exchange
正解：B
解説：
Amazon MWAA is a managed service for Apache Airflow, which is an open-source workflow automation tool. Apache Airflow can be used both on-premises and in the cloud, making it ideal for hybrid environments. Using Amazon MWAA allows the data engineer to leverage the managed service in the cloud while maintaining the ability to use the same open-source Airflow setup on-premises, ensuring portability and consistency across environments.
質問 # 79
A retail company stores data from a product lifecycle management (PLM) application in an on- premises MySQL database. The PLM application frequently updates the database when transactions occur.
The company wants to gather insights from the PLM application in near real time. The company wants to integrate the insights with other business datasets and to analyze the combined dataset by using an Amazon Redshift data warehouse.
The company has already established an AWS Direct Connect connection between the on- premises infrastructure and AWS.
Which solution will meet these requirements with the LEAST development effort?
- A. Run a scheduled AWS Glue extract, transform, and load (ETL) job to get the MySQL database updates by using a Java Database Connectivity (JDBC) connection. Set Amazon Redshift as the destination for the ETL job.
- B. Run a full load plus CDC task in AWS Database Migration Service (AWS DMS) to continuously replicate the MySQL database changes. Set Amazon Redshift as the destination for the task.
- C. Use the Amazon AppFlow SDK to build a custom connector for the MySQL database to continuously replicate the database changes. Set Amazon Redshift as the destination for the connector.
- D. Run scheduled AWS DataSync tasks to synchronize data from the MySQL database. Set Amazon Redshift as the destination for the tasks.
正解：B
解説：
https://aws.amazon.com/ko/blogs/apn/change-data-capture-from-on-premises-sql-server-to- amazon-redshift-target/
質問 # 80
An ecommerce company wants to use AWS to migrate data pipelines from an on-premises environment into the AWS Cloud. The company currently uses a third-party tool in the on- premises environment to orchestrate data ingestion processes.
The company wants a migration solution that does not require the company to manage servers.
The solution must be able to orchestrate Python and Bash scripts. The solution must not require the company to refactor any code.
Which solution will meet these requirements with the LEAST operational overhead?
- A. AWS Lambda
- B. AWS Glue
- C. Amazon Managed Workflows for Apache Airflow (Amazon MVVAA)
- D. AWS Step Functions
正解：C
解説：
All of the components contained in the outer box (in the image below) appear as a single Amazon MWAA environment in your account. The Apache Airflow Scheduler and Workers are AWS Fargate (Fargate) containers that connect to the private subnets in the Amazon VPC for your environment. Each environment has its own Apache Airflow metadatabase managed by AWS that is accessible to the Scheduler and Workers Fargate containers via a privately-secured VPC endpoint.
https://docs.aws.amazon.com/mwaa/latest/userguide/what-is-mwaa.html
質問 # 81
Partition columns optimize query performance by pruning out the data files that do not need to be scanned (i.e.
partitioning the external table). Which pseudocolumn of External table evaluate as an expression that parses the path and/or filename information.
- A. METADATA$FILEPATH
- B. METADATA$ROW_NUMBER
- C. METADATA$COLUMNNAME
- D. METADATA$FILENAME
正解：D
解説：
Explanation
METADATA$FILENAME
A pseudocolumn that identifies the name of each staged data file included in the external table, in-cluding its path in the stage.
An external table creator defines partition columns in a new external table as expressions that parse the path and/or filename information stored in the METADATA$FILENAME pseudocolumn. A partition consists of all data files that match the path and/or filename in the expression for the parti-tion column.
質問 # 82
For the most efficient and cost-effective Data load experience, Data Engineer needs to inconsider-ate which of the following considerations?
- A. if the "null" values in your files indicate missing values and have no other special mean-ing, Snowflake recommend setting the file format option STRIP_NULL_VALUES to TRUE when loading the semi-structured data file.
- B. Enabling the STRIP_OUTER_ARRAY file format option for the COPY INTO <ta-ble> command to remove the outer array structure and load the records into separate table rows.
- C. Amazon Kinesis Firehose can be convenient way to aggregate and batch data files which also allows defining both the desired file size, called the buffer size, and the wait interval after which a new file is sent, called the buffer interval.
- D. When preparing your delimited text (CSV) files for loading, the number of columns in each row should be consistent.
- E. Split larger files into a greater number of smaller files, maximize the processing over-head for each file.
(Correct)
正解：E
解説：
Explanation
Split larger files into a greater number of smaller files to distribute the load among the compute re-sources in an active warehouse. This would minimize the processing overhead rather than maximize it.
Rest is recommended Data loading considerations.
質問 # 83
A data engineer needs to debug an AWS Glue job that reads from Amazon S3 and writes to Amazon Redshift. The data engineer enabled the bookmark feature for the AWS Glue job.
The data engineer has set the maximum concurrency for the AWS Glue job to 1.
The AWS Glue job is successfully writing the output to Amazon Redshift. However, the Amazon S3 files that were loaded during previous runs of the AWS Glue job are being reprocessed by subsequent runs.
What is the likely reason the AWS Glue job is reprocessing the files?
- A. The maximum concurrency for the AWS Glue job is set to 1.
- B. The AWS Glue job does not have the s3:GetObjectAcl permission that is required for bookmarks to work correctly.
- C. The AWS Glue job does not have a required commit statement.
- D. The data engineer incorrectly specified an older version of AWS Glue for the Glue job.
正解：C
解説：
https://docs.aws.amazon.com/glue/latest/dg/glue-troubleshooting-errors.html#error-job- bookmarks-reprocess-data
質問 # 84
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 return message did not produce the same number of rows that it received
- B. External functions do not support multiple rows
- C. The JSON returned by the remote service is not constructed correctly
- D. Nested arrays are not supported in the JSON response
正解：A
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 # 85
A data engineer needs Amazon Athena queries to finish faster. The data engineer notices that all the files the Athena queries use are currently stored in uncompressed .csv format. The data engineer also notices that users perform most queries by selecting a specific column.
Which solution will MOST speed up the Athena query performance?
- A. Change the data format from .csv to Apache Parquet. Apply Snappy compression.
- B. Change the data format from .csv to JSON format. Apply Snappy compression.
- C. Compress the .csv files by using Snappy compression.
- D. Compress the .csv files by using gzip compression.
正解：A
解説：
Apache Parquet is a columnar storage format optimized for analytical queries. It is highly efficient for query performance, especially when queries involve selecting specific columns, as it allows for column pruning and predicate pushdown optimizations.
質問 # 86
A company has five offices in different AWS Regions. Each office has its own human resources (HR) department that uses a unique IAM role. The company stores employee records in a data lake that is based on Amazon S3 storage.
A data engineering team needs to limit access to the records. Each HR department should be able to access records for only employees who are within the HR department's Region.
Which combination of steps should the data engineering team take to meet this requirement with the LEAST operational overhead? (Choose two.)
- A. Register the S3 path as an AWS Lake Formation location.
- B. Modify the IAM roles of the HR departments to add a data filter for each department's Region.
- C. Use data filters for each Region to register the S3 paths as data locations.
- D. Enable fine-grained access control in AWS Lake Formation. Add a data filter for each Region.
- E. Create a separate S3 bucket for each Region. Configure an IAM policy to allow S3 access.Restrict access based on Region.
正解：A、D
解説：
https://docs.aws.amazon.com/lake-formation/latest/dg/data-filters-about.html
https://docs.aws.amazon.com/lake-formation/latest/dg/access-control-fine-grained.html
質問 # 87
You as Data engineer might want to consider disabling auto-suspend for a warehouse if?
- A. You have a heavy, steady workload for the warehouse.
- B. You require the warehouse to be available with no delay or lag time.
- C. You have a low, fluctuating workload for the warehouse.
- D. You require the warehouse to be available with delay.
正解：A、B
解説：
Explanation
Automating Warehouse Suspension
Data Engineer might want to consider disabling auto-suspend for a warehouse if:
He/She have a heavy, steady workload for the warehouse.
He/She require the warehouse to be available with no delay or lag time. Warehouse provisioning is generally very fast (e.g. 1 or 2 seconds); however, depending on the size of the warehouse and the availability of compute resources to provision, it can take longer.
If he/she chose to disable auto-suspend, He/she must carefully consider the costs associated with running a warehouse continually, even when the warehouse is not processing queries. The costs can be significant, especially for larger warehouses (X-Large, 2X-Large, etc.).
To disable auto-suspend, Engineer must explicitly select Never in the web interface, or specify 0 or NULL in SQL.
質問 # 88
......
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