DEA-C01無料試験学習ガイド！（更新された132問あります)
DEA-C01問題集にはSnowPro Advanced認証済み試験問題と解答
質問 # 18
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. The JSON returned by the remote service is not constructed correctly
- C. External functions do not support multiple rows
- 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 # 19
Which stages support external tables?
- A. External stages only from any region, and any cloud provider
- B. internal stages only from any Snowflake account in the organization
- C. External stages only, only on the same region and cloud provider as the Snowflake account
- D. Internal stages only; within a single Snowflake account
正解：A
解説：
Explanation
External stages only from any region, and any cloud provider support external tables. External tables are virtual tables that can query data from files stored in external stages without loading them into Snowflake tables. External stages are references to locations outside of Snowflake, such as Amazon S3 buckets, Azure Blob Storage containers, or Google Cloud Storage buckets. External stages can be created from any region and any cloud provider, as long as they have a valid URL and credentials. The other options are incorrect because internal stages do notsupport external tables. Internal stages are locations within Snowflake that can store files for loading or unloading data. Internal stages can be user stages, table stages, or named stages.
質問 # 20
The JSON below is stored in a variant column named v in a table named jCustRaw:
Which query will return one row per team member (stored in the teamMembers array) along all of the attributes of each team member?
- A.
- B.
- C.
- D.
正解：B
質問 # 21
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. SSO attached to an Amazon EC2 instance on AWS eu-west-1 (Ireland)
- C. External stage on GCP us-central1 (Iowa)
- D. External stage in an Amazon S3 bucket on AWS eu-west-1 (Ireland)
- E. Internal stage on AWS eu-central-1 (Frankfurt)
- F. External stage in an Amazon S3 bucket on AWS eu-central 1 (Frankfurt)
正解：C、D、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 # 22
Data Engineer, ran the below clustering depth analysis function:
select system$clustering_depth('TPCH_CUSTOMERS', '(C1, C6)', 'C9 = 30'); on TPCH_CUSTOMERS table, will return which of the following?
- A. Calculate the clustering depth for a table using the clustering key defined for the table.
- B. An error: this function does not accept lists of columns as a third parameter.
- C. Calculate the clustering depth for a table using mentioned columns in the table.
- D. An error: this function does not accept predicates ('C9 = 30') as parameter.
正解：C
質問 # 23
A data engineer must ingest a source of structured data that is in .csv format into an Amazon S3 data lake. The .csv files contain 15 columns. Data analysts need to run Amazon Athena queries on one or two columns of the dataset. The data analysts rarely query the entire file.
Which solution will meet these requirements MOST cost-effectively?
- A. Use an AWS Glue PySpark job to ingest the source data into the data lake in .csv format.
- B. Create an AWS Glue extract, transform, and load (ETL) job to read from the .csv structured data source. Configure the job to write the data into the data lake in Apache Parquet format.
- C. Create an AWS Glue extract, transform, and load (ETL) job to read from the .csv structured data source. Configure the job to ingest the data into the data lake in JSON format.
- D. Use an AWS Glue PySpark job to ingest the source data into the data lake in Apache Avro format.
正解：B
質問 # 24
A company loads transaction data for each day into Amazon Redshift tables at the end of each day. The company wants to have the ability to track which tables have been loaded and which tables still need to be loaded.
A data engineer wants to store the load statuses of Redshift tables in an Amazon DynamoDB table. The data engineer creates an AWS Lambda function to publish the details of the load statuses to DynamoDB.
How should the data engineer invoke the Lambda function to write load statuses to the DynamoDB table?
- A. Use a second Lambda function to invoke the first Lambda function based on AWS CloudTrail events.
- B. Use the Amazon Redshift Data API to publish an event to Amazon EventBridge. Configure an EventBridge rule to invoke the Lambda function.
- C. Use a second Lambda function to invoke the first Lambda function based on Amazon CloudWatch events.
- D. Use the Amazon Redshift Data API to publish a message to an Amazon Simple Queue Service (Amazon SQS) queue. Configure the SQS queue to invoke the Lambda function.
正解：B
解説：
https://docs.aws.amazon.com/redshift/latest/mgmt/data-api-monitoring-events.html
質問 # 25
Ira a Data Engineer with TESLA IT systems, looking out to Compare Traditional Partitioning vs Snowflake micro-partitions for one of the Snowflake Project implementations. Which one of the following is incorrect understanding of Ira about Micro Partitioning?
- A. All DML operations (e.g. DELETE, UPDATE, MERGE) take advantage of the under-lying micro-partition metadata to facilitate and simplify table maintenance.
- B. In Snowflake, as data is inserted/loaded into a table, clustering metadata is collected and recorded for each micro-partition created during the process.
- C. The micro-partition metadata maintained by Snowflake enables precise pruning of col-umns in micro-partitions at query run-time, including columns containing semi-structured data.
- D. Snowflake stores metadata about all rows stored in a micro-partition, including number of distinct columns.
- E. All data in Snowflake tables is automatically divided into micro-partitions, which are contiguous units of storage compared to traditional partitioning where specialized DDL required.
正解：D
解説：
Explanation
What are Micro-partitions?
All data in Snowflake tables is automatically divided into micro-partitions, which are contiguous units of storage. Each micro-partition contains between 50 MB and 500 MB of uncompressed data (note that the actual size in Snowflake is smaller because data is always stored compressed). Groups of rows in tables are mapped into individual micro-partitions, organized in a columnar fashion. This size and structure allow for extremely granular pruning of very large tables, which can be comprised of millions, or even hundreds of millions, of micro-partitions.
Snowflake stores metadata about all rows stored in a micro-partition, including:
The range of values for each of the columns in the micro-partition.
The number of distinct values.
Additional properties used for both optimization and efficient query processing.
It Never stores number of columns as part of Metadata.
Rest of the statements are correct.
質問 # 26
Within a Snowflake account permissions have been defined with custom roles and role hierarchies.
To set up column-level masking using a role in the hierarchy of the current user, what command would be used?
- A. IS_GRANTED_TO_INVOKER_ROLE
- B. IS_RCLE_IN_SESSION
- C. CORRECT_ROLE
- D. IKVOKER_ROLE
正解：B
解説：
Explanation
The IS_ROLE_IN_SESSION function is used to set up column-level masking using a role in the hierarchy of the current user. Column-level masking is a feature in Snowflake that allows users to apply dynamic data masking policies to specific columns based on the roles of the users who access them. The IS_ROLE_IN_SESSION function takes a role name as an argument and returns true if the role is in the current user's session, or false otherwise. The function can be used in a masking policy expression to determine whether to mask or unmask a column value based on the role of the user. For example:
CREATE OR REPLACE MASKING POLICY email_mask AS (val string) RETURNS string -> CASE WHEN IS_ROLE_IN_SESSION('HR') THEN val ELSE REGEXP_REPLACE(val, '(.).(.@.)', '\1****\2') END; In this example, the IS_ROLE_IN_SESSION function is used to create a masking policy for an email column.
The masking policy returns the original email value if the user has the HR role in their session, or returns a masked email value with asterisks if not.
質問 # 27
To view/monitor the clustering metadata for a table, Snowflake provides which of the following system functions?
- A. SYSTEM$CLUSTERING_DEPTH_KEY
- B. SYSTEM$CLUSTERING_DEPTH
- C. SYSTEM$CLUSTERING_KEY_INFORMATION (including clustering depth)
- D. SYSTEM$CLUSTERING_INFORMATION (including clustering depth)
正解：B、D
解説：
Explanation
SYSTEM$CLUSTERING_DEPTH:
Computes the average depth of the table according to the specified columns (or the clustering key defined for the table). The average depth of a populated table (i.e. a table containing data) is always 1 or more. The smaller the average depth, the better clustered the table is with regards to the speci-fied columns.
Calculate the clustering depth for a table using two columns in the table:
SELECT SYSTEM$CLUSTERING_DEPTH('TPCH_PRODUCT', '(C2, C9)');
SYSTEM$CLUSTERING_INFORMATION:
Returns clustering information, including average clustering depth, for a table based on one or more columns in the table.
SELECT SYSTEM$CLUSTERING_INFORMATION('SAMPLE_TABLE', '(col1, col3)');
質問 # 28
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. Client-Side Encryption with a client-side master key.
- C. Server-Side Encryption with Customer-Provided Keys (SSE-C).
- D. Server-Side Encryption with AWS Key Management Service (SSE-KMS).
正解：D
質問 # 29
Which Role inherits the privileges of the USERADMIN role via the system role hierarchy?
- A. SECURITYADMIN
- B. CUSTOM ROLE
- C. SYSADMIN
- D. PUBLIC
正解：A
質問 # 30
A company has a production AWS account that runs company workloads. The company's security team created a security AWS account to store and analyze security logs from the production AWS account. The security logs in the production AWS account are stored in Amazon CloudWatch Logs.
The company needs to use Amazon Kinesis Data Streams to deliver the security logs to the security AWS account.
Which solution will meet these requirements?
- A. Create a destination data stream in the production AWS account. In the security AWS account, create an IAM role that has cross-account permissions to Kinesis Data Streams in the production AWS account.
- B. Create a destination data stream in the security AWS account. Create an IAM role and a trust policy to grant CloudWatch Logs the permission to put data into the stream. Create a subscription filter in the security AWS account.
- C. Create a destination data stream in the security AWS account. Create an IAM role and a trust policy to grant CloudWatch Logs the permission to put data into the stream. Create a subscription filter in the production AWS account.
- D. Create a destination data stream in the production AWS account. In the production AWS account, create an IAM role that has cross-account permissions to Kinesis Data Streams in the security AWS account.
正解：C
質問 # 31
Streams cannot be created to query change data on which of the following objects? [Select All that Apply]
- A. Directory tables
- B. External tables
- C. Query Log Tables
- D. Standard tables, including shared tables.
- E. Views, including secure views
正解：C
解説：
Explanation
Streams supports all the listed objects except Query Log tables.
質問 # 32
A data engineer wants to improve the performance of SQL queries in Amazon Athena that run against a sales data table.
The data engineer wants to understand the execution plan of a specific SQL statement. The data engineer also wants to see the computational cost of each operation in a SQL query.
Which statement does the data engineer need to run to meet these requirements?
- A. EXPLAIN FROM sales;
- B. EXPLAIN ANALYZE FROM sales;
- C. EXPLAIN ANALYZE SELECT * FROM sales;
- D. EXPLAIN SELECT * FROM sales;
正解：C
解説：
https://docs.aws.amazon.com/athena/latest/ug/athena-explain-statement.html
質問 # 33
A company wants to use machine learning (ML) to perform analytics on data that is in an Amazon S3 data lake. The company has two data transformation requirements that will give consumers within the company the ability to create reports.
The company must perform daily transformations on 300 GB of data that is in a variety format that must arrive in Amazon S3 at a scheduled time. The company must perform one-time transformations of terabytes of archived data that is in the S3 data lake. The company uses Amazon Managed Workflows for Apache Airflow (Amazon MWAA) Directed Acyclic Graphs (DAGs) to orchestrate processing.
Which combination of tasks should the company schedule in the Amazon MWAA DAGs to meet these requirements MOST cost-effectively? (Choose two.)
- A. For daily and archived data, use Amazon EMR to perform data transformations.
- B. For daily incoming data, use Amazon Redshift to perform transformations.
- C. For daily incoming data, use AWS Glue crawlers to scan and identify the schema.
- D. For daily incoming data, use Amazon Athena to scan and identify the schema.
- E. For archived data, use Amazon SageMaker to perform data transformations.
正解：A、C
質問 # 34
A data engineer is configuring Amazon SageMaker Studio to use AWS Glue interactive sessions to prepare data for machine learning (ML) models.
The data engineer receives an access denied error when the data engineer tries to prepare the data by using SageMaker Studio.
Which change should the engineer make to gain access to SageMaker Studio?
- A. Add a policy to the data engineer's IAM user that includes the sts:AssumeRole action for the AWS Glue and SageMaker service principals in the trust policy.
- B. Add the AWSGlueServiceRole managed policy to the data engineer's IAM user.
- C. Add a policy to the data engineer's IAM user that allows the sts:AddAssociation action for the AWS Glue and SageMaker service principals in the trust policy.
- D. Add the AmazonSageMakerFullAccess managed policy to the data engineer's IAM user.
正解：D
解説：
Amazon SageMaker requires permissions to perform actions on your behalf. By attaching the AmazonSageMakerFullAccess managed policy to the data engineer's IAM user, you grant the necessary permissions for SageMaker Studio to access AWS Glue and other related services.
質問 # 35
For SQL UDFs, The invoker of the function need not have access to the objects referenced in the function definition, but only needs the privilege to use the function?
- A. FALSE
- B. TRUE
正解：B
質問 # 36
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') WHERE query_id IS NOT NULL); - B. 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); - C. 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')); - D. 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'));
正解：C
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 # 37
A company receives .csv files that contain physical address data. The data is in columns that have the following names: Door_No, Street_Name, City, and Zip_Code. The company wants to create a single column to store these values in the following format:
Which solution will meet this requirement with the LEAST coding effort?
- A. Use AWS Glue DataBrew to read the files. Use the NEST_TO_ARRAY transformation to create the new column.
- B. Write a Lambda function in Python to read the files. Use the Python data dictionary type to create the new column.
- C. Use AWS Glue DataBrew to read the files. Use the PIVOT transformation to create the new column.
- D. Use AWS Glue DataBrew to read the files. Use the NEST_TO_MAP transformation to create the new column.
正解：D
質問 # 38
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. ESC&PE_UNENGLO9ED_FIELD = '\\'
- B. FIELD OPTIONALLY ENCLOSED BY = " "
- C. FIELD_DELIMITER = ","
- D. ERROR_ON_COLUMN_COUKT_MISMATCH = FALSE
正解：B
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 # 39
Pivoting in SQL is mainly used to transform data from:
- A. Multiple columns into multiple rows
- B. One row to one column
- C. Multiple rows into multiple columns
- D. Only one column to one row
正解：C
質問 # 40
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Snowflake DEA-C01 認定試験の出題範囲：
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