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DEA-C01のPDFで合格させるスゴ問題集でDEA-C01最新のリアル試験問題 [Q12-Q32]

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DEA-C01のPDFで合格させるスゴ問題集でDEA-C01最新のリアル試験問題

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質問 # 12
Which callback function is required within a JavaScript User-Defined Function (UDF) for it to execute successfully?

  • A. initialize ()
  • B. finalize ()
  • C. handler
  • D. processRow ()

正解：D

解説：
Explanation
The processRow () callback function is required within a JavaScript UDF for it to execute successfully. This function defines how each row of input data is processed and what output is returned. The other callback functions are optional and can be used for initialization, finalization, or error handling.


質問 # 13
A data engineer must build an extract, transform, and load (ETL) pipeline to process and load data from 10 source systems into 10 tables that are in an Amazon Redshift database. All the source systems generate .csv, JSON, or Apache Parquet files every 15 minutes. The source systems all deliver files into one Amazon S3 bucket. The file sizes range from 10 MB to 20 GB.
The ETL pipeline must function correctly despite changes to the data schema.
Which data pipeline solutions will meet these requirements? (Choose two.)

  • A. Configure an AWS Lambda function to invoke an AWS Glue workflow when a file is loaded into the S3 bucket. Configure the AWS Glue workflow to have an on-demand trigger that runs an AWS Glue crawler and then runs an AWS Glue job when the crawler finishes running successfully. Configure the AWS Glue job to process and load the data into the Amazon Redshift tables.
  • B. Use an Amazon EventBridge rule to run an AWS Glue job every 15 minutes. Configure the AWS Glue job to process and load the data into the Amazon Redshift tables.
  • C. Use an Amazon EventBridge rule to invoke an AWS Glue workflow job every 15 minutes.
    Configure the AWS Glue workflow to have an on-demand trigger that runs an AWS Glue crawler and then runs an AWS Glue job when the crawler finishes running successfully. Configure the AWS Glue job to process and load the data into the Amazon Redshift tables.
  • D. Configure an AWS Lambda function to invoke an AWS Glue crawler when a file is loaded into the S3 bucket. Configure an AWS Glue job to process and load the data into the Amazon Redshift tables. Create a second Lambda function to run the AWS Glue job. Create an Amazon EventBridge rule to invoke the second Lambda function when the AWS Glue crawler finishes running successfully.
  • E. Configure an AWS Lambda function to invoke an AWS Glue job when a file is loaded into the S3 bucket. Configure the AWS Glue job to read the files from the S3 bucket into an Apache Spark DataFrame. Configure the AWS Glue job to also put smaller partitions of the DataFrame into an Amazon Kinesis Data Firehose delivery stream. Configure the delivery stream to load data into the Amazon Redshift tables.

正解：A、C


質問 # 14
You as Data engineer might want to consider disabling auto-suspend for a warehouse if?

  • A. You require the warehouse to be available with no delay or lag time.
  • B. You require the warehouse to be available with delay.
  • C. You have a low, fluctuating workload for the warehouse.
  • D. You have a heavy, steady workload for the warehouse.

正解：A、D

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 # 15
Which are supported Programming Languages for Creating UDTFs?

  • A. Python
  • B. Java
  • C. Perl
  • D. Javascript
  • E. Node.javascript

正解：A、B、D


質問 # 16
Which column provides information when the stream became stale or may become stale if not con-sumed?

  • A. STALE_AFTER
  • B. STALE_STREAM_PERIOD
  • C. STALE_PERIOD_AFTER
  • D. STREAM_STALE_PERIOD

正解：A

解説：
Explanation
execute SHOW STREAMS command.
Column Name: STALE_AFTER
Timestamp when the stream became stale or may become stale if not consumed. The value is calcu-lated by adding the retention period for the source table (i.e. the larger of the DA-TA_RETENTION_TIME_IN_DAYS or MAX_DATA_EXTENSION_TIME_IN_DAYS parame-ter setting) to the last time the stream was read.


質問 # 17
A company is planning to migrate on-premises Apache Hadoop clusters to Amazon EMR. The company also needs to migrate a data catalog into a persistent storage solution.
The company currently stores the data catalog in an on-premises Apache Hive metastore on the Hadoop clusters. The company requires a serverless solution to migrate the data catalog.
Which solution will meet these requirements MOST cost-effectively?

  • A. Configure a new Hive metastore in Amazon EMR. Migrate the existing on-premises Hive metastore into Amazon EMR. Use the new metastore as the company's data catalog.
  • B. Configure an external Hive metastore in Amazon EMR. Migrate the existing on-premises Hive metastore into Amazon EMR. Use Amazon Aurora MySQL to store the company's data catalog.
  • C. Configure a Hive metastore in Amazon EMR. Migrate the existing on-premises Hive metastore into Amazon EMR. Use AWS Glue Data Catalog to store the company's data catalog as an external data catalog.
  • D. Use AWS Database Migration Service (AWS DMS) to migrate the Hive metastore into Amazon S3. Configure AWS Glue Data Catalog to scan Amazon S3 to produce the data catalog.

正解：C

解説：
https://aws.amazon.com/blogs/big-data/migrate-and-deploy-your-apache-hive-metastore-on- amazon-emr/ Migrating the Hive metastore into Amazon EMR and using AWS Glue Data Catalog as an external catalog provides a balance between leveraging the scalable and managed services of AWS (like EMR and Glue Data Catalog) and ensuring a smooth transition from the on-premises setup. This approach leverages the serverless nature of AWS Glue Data Catalog, minimizing operational overhead and potentially reducing costs compared to managing database servers.


質問 # 18
A stream called TRANSACTIONS_STM is created on top of a transactions table in a continuous pipeline running in Snowflake. After a couple of months, the TRANSACTIONS table is renamed transactiok3_raw to comply with new naming standards What will happen to the TRANSACTIONS _STM object?

  • A. TRANSACTIONS _STMwill be automatically renamedTRANSACTIONS _RAW_STM.
  • B. Reading from the traksactioks_3T>: stream will succeed for some time after the expected STALE_TIME.
  • C. TRANSACTIONS _STMwill be stale and will need to be re-created
  • D. TRANSACTIONS _STMwill keep working as expected

正解：C

解説：
Explanation
A stream is a Snowflake object that records the history of changes made to a table. A stream is associated with a specific table at the time of creation, and it cannot be altered to point to a different table later. Therefore, if the source table is renamed, the stream will become stale and will need to be re-created with the new table name. The other options are not correct because:
TRANSACTIONS _STM will not keep working as expected, as it will lose track of the changes made to the renamed table.
TRANSACTIONS _STM will not be automatically renamed TRANSACTIONS _RAW_STM, as streams do not inherit the name changes of their source tables.
Reading from the transactions_stm stream will not succeed for some time after the expected STALE_TIME, as streams do not have a STALE_TIME property.


質問 # 19
A Data Engineer wants to create a new development database (DEV) as a clone of the permanent production database (PROD) There is a requirement to disable Fail-safe for all tables.
Which command will meet these requirements?

  • A. CREATE DATABASE DEV
    CLONE PROD
    FAIL_SAFE=FALSE;
  • B. CREATE DATABASE DEV
    CLOSE PROD
    DATA_RETENTION_TIME_IN_DAYS =0L
  • C. CREATE DATABASE DEV
    CLONE PROD;
  • D. CREATE TRANSIENT DATABASE DEV
    CLONE RPOD

正解：D

解説：
Explanation
This option will meet the requirements of creating a new development database (DEV) as a clone of the permanent production database (PROD) and disabling Fail-safe for all tables. By using the CREATE TRANSIENT DATABASE command, the Data Engineer can create a transient database that does not have Fail-safe enabled by default. Fail-safe is a feature in Snowflake that provides additional protection against data loss by retaining historical data for seven days beyond the time travel retention period. Transient databases do not have Fail-safe enabled, which means that they do not incur additional storage costs for historical data beyond their time travel retention period. By using the CLONE option, the Data Engineer can create an exact copy of the PROD database, including its schemas, tables, views, and other objects.


質問 # 20
Data Engineer wants to analyze query performance & looking out for profiling information, He went to Query/Operator Details also called Profile Overview of Query Profile Interface & searching for statistics attributes around I/O. Which of the following information he can't get from there?

  • A. External bytes scanned - bytes read from an external object, e.g. a stage.
  • B. Bytes written - bytes written (e.g. when loading into a table).
  • C. Percentage scanned from cache - the percentage of data scanned from the local disk cache.
  • D. Bytes read from result - bytes read from the result object.
  • E. Bytes sent over the wireframe - amount of data sent over the wireframe

正解：E

解説：
Explanation
To help you analyze query performance, Query/Operator Details panel also called Profile overview panel provides two classes of profiling information:
Execution time, broken down into categories
Detailed statistics
Apart from Option a Bytes sent over the wireframe - amount of data sent over the wireframe , Rest of the Statistics Information provided by Query/Operator details in the Query Profile Inter-face.
To Know More about the Query/Operator Details options , please refer the link:
https://docs.snowflake.com/en/user-guide/ui-query-profile#query-operator-details


質問 # 21
If external software i.e. TIBCO, exports Data fields enclosed in quotes but inserts a leading space before the opening quotation character for each field, How Snowflake handle it? [Select 2]

  • A. field_optionally_enclosed_by option along with TRIM_IF function in COPY INTO statement can be used to handle this scenario successfully.
  • B. Snowflake reads the leading space rather than the opening quotation character as the beginning of the field and the quotation characters are interpreted as string data.
    (Correct)
  • C. Snowflake automatically handles leading spaces by trimming implicitly & removes the quotation marks enclosing each field.
  • D. COPY command trims the leading space and removes the quotation marks enclosing each field
    1.copy into SFtable
    2.from @%SFtable
    3.file_format = (type = csv trim_space=true field_optionally_enclosed_by = '0x22');

正解：D

解説：
Explanation
If your external software exports fields enclosed in quotes but inserts a leading space before the opening quotation character for each field, Snowflake reads the leading space rather than the open-ing quotation character as the beginning of the field. The quotation characters are interpreted as string data.
Use the TRIM_SPACE file format option to remove undesirable spaces during the data load.


質問 # 22
You can execute zero, one, or more transactions inside a stored procedure?

  • A. FALSE
  • B. TRUE

正解：B


質問 # 23
A company stores details about transactions in an Amazon S3 bucket. The company wants to log all writes to the S3 bucket into another S3 bucket that is in the same AWS Region.
Which solution will meet this requirement with the LEAST operational effort?

  • A. Create a trail of management events in AWS CloudTraiL. Configure the trail to receive data from the transactions S3 bucket. Specify an empty prefix and write-only events. Specify the logs S3 bucket as the destination bucket.
  • B. Configure an S3 Event Notifications rule for all activities on the transactions S3 bucket to invoke an AWS Lambda function. Program the Lambda function to write the event to Amazon Kinesis Data Firehose. Configure Kinesis Data Firehose to write the event to the logs S3 bucket.
  • C. Create a trail of data events in AWS CloudTraiL. Configure the trail to receive data from the transactions S3 bucket. Specify an empty prefix and write-only events. Specify the logs S3 bucket as the destination bucket.
  • D. Configure an S3 Event Notifications rule for all activities on the transactions S3 bucket to invoke an AWS Lambda function. Program the Lambda function to write the events to the logs S3 bucket.

正解：C

解説：
https://docs.aws.amazon.com/AmazonS3/latest/userguide/logging-with-S3.html


質問 # 24
Select the correct usage statements with regards to SQL UDF?

  • A. When using a query expression in a SQL UDF, do not include a semicolon within the UDF body to terminate the query expression.
  • B. All of above are correct.
  • C. The body of a UDF cannot contain DDL statements or any DML statement other than SELECT.
  • D. You can include only one query expression.
  • E. Scalar functions (UDFs) have a limit of 500 input arguments.

正解：B


質問 # 25
Mark the Incorrect Statements with respect to types of streams supported by Snowflake?

  • A. Insert-only Stream supported on external tables only.
  • B. An append-only stream returns the appended rows only and therefore can be much more performant than a standard stream for extract, load, transform (ELT).
  • C. An insert-only stream tracks row inserts & Delete ops only
  • D. Standard streams cannot retrieve update data for geospatial data.

正解：C

解説：
Explanation
Standard Stream:
Supported for streams on tables, directory tables, or views. A standard (i.e. delta) stream tracks all DML changes to the source object, including inserts, updates, and deletes (including table trun-cates). This stream type performs a join on inserted and deleted rows in the change set to provide the row level delta. As a net effect, for example, a row that is inserted and then deleted between two transactional points of time in a table is removed in the delta (i.e. is not returned when the stream is queried).
Append-only Stream:
Supported for streams on standard tables, directory tables, or views. An append-only stream tracks row inserts only. Update and delete operations (including table truncates) are not recorded. For ex-ample, if 10 rows are inserted into a table and then 5 of those rows are deleted before the offset for an append-only stream is advanced, the stream records 10 rows.
An append-only stream returns the appended rows only and therefore can be much more performant than a standard stream for extract, load, transform (ELT) and similar scenarios that depend exclu-sively on row inserts. For example, a source table can be truncated immediately after the rows in an append-only stream are consumed, and the record deletions do not contribute to the overhead the next time the stream is queried or consumed.
Insert-only Stream:
Supported for streams on external tables only. An insert-only stream tracks row inserts only; they do not record delete operations that remove rows from an inserted set (i.e. no-ops). For example, in-between any two offsets, if File1 is removed from the cloud storage location referenced by the ex-ternal table, and File2 is added, the stream returns records for the rows in File2 only. Unlike when tracking CDC data for standard tables, Snowflake cannot access the historical records for files in cloud storage.


質問 # 26
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. Create indexes to ensure sorted access to data
  • B. Use a multi-cluster virtual warehouse with the scaling policy set to standard
  • C. Increase the max cluster count
  • D. Move the query to a larger virtual warehouse
  • E. Add a LIMIT to the ORDER BY If possible

正解：D、E

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 # 27
An airline company is collecting metrics about flight activities for analytics. The company is conducting a proof of concept (POC) test to show how analytics can provide insights that the company can use to increase on-time departures.
The POC test uses objects in Amazon S3 that contain the metrics in .csv format. The POC test uses Amazon Athena to query the data. The data is partitioned in the S3 bucket by date.
As the amount of data increases, the company wants to optimize the storage solution to improve query performance.
Which combination of solutions will meet these requirements? (Choose two.)

  • A. Use an S3 bucket that is in the same account that uses Athena to query the data.
  • B. Use an S3 bucket that is in the same AWS Region where the company runs Athena queries.
  • C. Preprocess the .csv data to JSON format by fetching only the document keys that the query requires.
  • D. Preprocess the .csv data to Apache Parquet format by fetching only the data blocks that are needed for predicates.
  • E. Add a randomized string to the beginning of the keys in Amazon S3 to get more throughput across partitions.

正解：B、D

解説：
https://docs.aws.amazon.com/athena/latest/ug/performance-tuning.html


質問 # 28
Given the table sales which has a clustering key of column CLOSED_DATE which table function will return the average clustering depth for the SALES_REPRESENTATIVEcolumn for the North American region?

  • A.
  • B.
  • C.
  • D.

正解：B

解説：
Explanation
The table function SYSTEM$CLUSTERING_DEPTH returns the average clustering depth for a specified column or set of columns in a table. The function takes two arguments: the table name and the column name(s). In this case, the table name is sales and the column name is SALES_REPRESENTATIVE. The function also supports a WHERE clause to filter the rows for which the clustering depth is calculated. In this case, the WHERE clause is REGION = 'North America'. Therefore, the function call in Option B will return the desired result.


質問 # 29
A company is migrating its database servers from Amazon EC2 instances that run Microsoft SQL Server to Amazon RDS for Microsoft SQL Server DB instances. The company's analytics team must export large data elements every day until the migration is complete. The data elements are the result of SQL joins across multiple tables. The data must be in Apache Parquet format. The analytics team must store the data in Amazon S3.
Which solution will meet these requirements in the MOST operationally efficient way?

  • A. Use a SQL query to create a view in the EC2 instance-based SQL Server databases that contains the required data elements. Create and run an AWS Glue crawler to read the view.
    Create an AWS Glue job that retrieves the data and transfers the data in Parquet format to an S3 bucket. Schedule the AWS Glue job to run every day.
  • B. Create a view in the EC2 instance-based SQL Server databases that contains the required data elements. Create an AWS Glue job that selects the data directly from the view and transfers the data in Parquet format to an S3 bucket. Schedule the AWS Glue job to run every day.
  • C. Create an AWS Lambda function that queries the EC2 instance-based databases by using Java Database Connectivity (JDBC). Configure the Lambda function to retrieve the required data, transform the data into Parquet format, and transfer the data into an S3 bucket. Use Amazon EventBridge to schedule the Lambda function to run every day.
  • D. Schedule SQL Server Agent to run a daily SQL query that selects the desired data elements from the EC2 instance-based SQL Server databases. Configure the query to direct the output .csv objects to an S3 bucket. Create an S3 event that invokes an AWS Lambda function to transform the output format from .csv to Parquet.

正解：B


質問 # 30
Streams cannot be created to query change data on which of the following objects? [Select All that Apply]

  • A. External tables
  • B. Directory tables
  • C. Views, including secure views
  • D. Standard tables, including shared tables.
  • E. Query Log Tables

正解：E

解説：
Explanation
Streams supports all the listed objects except Query Log tables.


質問 # 31
A company uses Apache Airflow to orchestrate the company's current on-premises data pipelines. The company runs SQL data quality check tasks as part of the pipelines. The company wants to migrate the pipelines to AWS and to use AWS managed services.
Which solution will meet these requirements with the LEAST amount of refactoring?

  • A. Convert the pipelines to AWS Step Functions workflows. Recreate the data quality checks in SQL as Python based AWS Lambda functions.
  • B. Migrate the existing Airflow orchestration configuration into Amazon Managed Workflows for Apache Airflow (Amazon MWAA). Create the data quality checks during the ingestion to validate the data quality by using SQL tasks in Airflow.
  • C. Create a custom Amazon Machine Image (AMI) that contains the Airflow application and the code that the company needs to migrate. Use the custom AMI to deploy Amazon EC2 instances.
    Update the network connections to interact with the newly deployed EC2 instances.
  • D. Setup AWS Outposts in the AWS Region that is nearest to the location where the company uses Airflow. Migrate the servers into Outposts hosted Amazon EC2 instances. Update the pipelines to interact with the Outposts hosted EC2 instances instead of the on-premises pipelines.

正解：B

解説：
Amazon MWAA is a managed service for running Apache Airflow. It allows migrating existing Airflow configurations with minimal changes. Data quality checks can continue to be implemented as SQL tasks in Airflow, similar to the current setup.


質問 # 32
......

DEA-C01問題集はあなたの合格を必ず保証します：https://www.passtest.jp/Snowflake/DEA-C01-shiken.html

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