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최신 AWS Certified Data Engineer Data-Engineer-Associate 무료샘플문제 (Q136-Q141):

질문 # 136
A company implements a data mesh that has a central governance account. The company needs to catalog all data in the governance account. The governance account uses AWS Lake Formation to centrally share data and grant access permissions.
The company has created a new data product that includes a group of Amazon Redshift Serverless tables. A data engineer needs to share the data product with a marketing team. The marketing team must have access to only a subset of columns. The data engineer needs to share the same data product with a compliance team.
The compliance team must have access to a different subset of columns than the marketing team needs access to.
Which combination of steps should the data engineer take to meet these requirements? (Select TWO.)

A. Share the Amazon Redshift data share to the Amazon Redshift Serverless workgroup in the marketing team's account.
B. Share the Amazon Redshift data share to the Lake Formation catalog in the governance account.
C. Create an Amazon Redshift data than that includes the tables that need to be shared.
D. Create an Amazon Redshift managed VPC endpoint in the marketing team's account. Grant the marketing team access to the views.
E. Create views of the tables that need to be shared. Include only the required columns.

정답：A,E

설명：
The company is using a data mesh architecture with AWS Lake Formation for governance and needs to share specific subsets of data with different teams (marketing and compliance) using Amazon Redshift Serverless.
* Option A: Create views of the tables that need to be shared. Include only the required columns.
Creating views in Amazon Redshift that include only the necessary columns allows for fine-grained access control. This method ensures that each team has access to only the data they are authorized to view.
* Option E: Share the Amazon Redshift data share to the Amazon Redshift Serverless workgroup in the marketing team's account.Amazon Redshift data sharing enables live access to data across Redshift clusters or Serverless workgroups. By sharing data with specific workgroups, you can ensure that the marketing team and compliance team each access the relevant subset of data based on the views created.
* Option B (creating a Redshift data share) is close but does not address the fine-grained column-level access.
* Option C (creating a managed VPC endpoint) is unnecessary for sharing data with specific teams.
* Option D (sharing with the Lake Formation catalog) is incorrect because Redshift data shares do not integrate directly with Lake Formation catalogs; they are specific to Redshift workgroups.
References:
* Amazon Redshift Data Sharing
* AWS Lake Formation Documentation

질문 # 137
A data engineer has a one-time task to read data from objects that are in Apache Parquet format in an Amazon S3 bucket. The data engineer needs to query only one column of the data.
Which solution will meet these requirements with the LEAST operational overhead?

A. Prepare an AWS Glue DataBrew project to consume the S3 objects and to query the required column.
B. Confiqure an AWS Lambda function to load data from the S3 bucket into a pandas dataframe- Write a SQL SELECT statement on the dataframe to query the required column.
C. Run an AWS Glue crawler on the S3 objects. Use a SQL SELECT statement in Amazon Athena to query the required column.
D. Use S3 Select to write a SQL SELECT statement to retrieve the required column from the S3 objects.

정답：D

설명：
Option B is the best solution to meet the requirements with the least operational overhead because S3 Select is a feature that allows you to retrieve only a subset of data from an S3 object by using simple SQL expressions.
S3 Select works on objects stored in CSV, JSON, or Parquet format. By using S3 Select, you can avoid the need to download and process the entire S3 object, which reduces the amount of data transferred and the computation time. S3 Select is also easy to use and does not require any additional services or resources.
Option A is not a good solution because it involves writing custom code and configuring an AWS Lambda function to load data from the S3 bucket into a pandas dataframe and query the required column. This option adds complexity and latency to the data retrieval process and requires additional resources and configuration.
Moreover, AWS Lambda has limitations on the execution time, memory, and concurrency, which may affect the performance and reliability of the data retrieval process.
Option C is not a good solution because it involves creating and running an AWS Glue DataBrew project to consume the S3 objects and query the required column. AWS Glue DataBrew is a visual data preparation tool that allows you to clean, normalize, and transform data without writing code. However, in this scenario, the data is already in Parquet format, which is a columnar storage format that is optimized for analytics.
Therefore, there is no need to use AWS Glue DataBrew to prepare the data. Moreover, AWS Glue DataBrew adds extra time and cost to the data retrieval process and requires additional resources and configuration.
Option D is not a good solution because it involves running an AWS Glue crawler on the S3 objects and using a SQL SELECT statement in Amazon Athena to query the required column. An AWS Glue crawler is a service that can scan data sources and create metadata tables in the AWS Glue Data Catalog. The Data Catalog is a central repository that stores information about the data sources, such as schema, format, and location. Amazon Athena is a serverless interactive query service that allows you to analyze data in S3 using standard SQL. However, in this scenario, the schema and format of the data are already known and fixed, so there is no need to run a crawler to discover them. Moreover, running a crawler and using Amazon Athena adds extra time and cost to the data retrieval process and requires additional services and configuration.
References:
* AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
* S3 Select and Glacier Select - Amazon Simple Storage Service
* AWS Lambda - FAQs
* What Is AWS Glue DataBrew? - AWS Glue DataBrew
* Populating the AWS Glue Data Catalog - AWS Glue
* What is Amazon Athena? - Amazon Athena

질문 # 138
A data engineer needs to create an Amazon Athena table based on a subset of data from an existing Athena table named cities_world. The cities_world table contains cities that are located around the world. The data engineer must create a new table named cities_us to contain only the cities from cities_world that are located in the US.
Which SQL statement should the data engineer use to meet this requirement?

A. Option A
B. Option C
C. Option B
D. Option D

정답：A

설명：
To create a new table named cities_usa in Amazon Athena based on a subset of data from the existing cities_world table, you should use an INSERT INTO statement combined with a SELECT statement to filter only the records where the country is 'usa'. The correct SQL syntax would be:
* Option A: INSERT INTO cities_usa (city, state) SELECT city, state FROM cities_world WHERE country='usa';This statement inserts only the cities and states where the country column has a value of
'usa' from the cities_world table into the cities_usa table. This is a correct approach to create a new table with data filtered from an existing table in Athena.
Options B, C, and D are incorrect due to syntax errors or incorrect SQL usage (e.g., the MOVE command or the use of UPDATE in a non-relevant context).
References:
* Amazon Athena SQL Reference
* Creating Tables in Athena

질문 # 139
A company is migrating on-premises workloads to AWS. The company wants to reduce overall operational overhead. The company also wants to explore serverless options.
The company's current workloads use Apache Pig, Apache Oozie, Apache Spark, Apache Hbase, and Apache Flink. The on-premises workloads process petabytes of data in seconds. The company must maintain similar or better performance after the migration to AWS.
Which extract, transform, and load (ETL) service will meet these requirements?

A. Amazon Redshift
B. Amazon EMR
C. AWS Lambda
D. AWS Glue

정답：B

설명：
AWS Glue is a fully managed serverless ETL service that can handle petabytes of data in seconds. AWS Glue can run Apache Spark and Apache Flink jobs without requiring any infrastructure provisioning or management. AWS Glue can also integrate with Apache Pig, Apache Oozie, and Apache Hbase using AWS Glue Data Catalog and AWS Glue workflows. AWS Glue can reduce the overall operational overhead by automating the data discovery, data preparation, and data loading processes. AWS Glue can also optimize the cost and performance of ETL jobs by using AWS Glue Job Bookmarking, AWS Glue Crawlers, and AWS Glue Schema Registry. References:
* AWS Glue
* AWS Glue Data Catalog
* AWS Glue Workflows
* [AWS Glue Job Bookmarking]
* [AWS Glue Crawlers]
* [AWS Glue Schema Registry]
* [AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide]

질문 # 140
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 Apache Avro format.
B. 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.
C. Use an AWS Glue PySpark job to ingest the source data into the data lake in .csv format.
D. 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.

정답：D

설명：
Amazon Athena is a serverless interactive query service that allows you to analyze data in Amazon S3 using standard SQL. Athena supports various data formats, such as CSV, JSON, ORC, Avro, and Parquet. However, not all data formats are equally efficient for querying. Some data formats, such as CSV and JSON, are row-oriented, meaning that they store data as a sequence of records, each with the same fields. Row-oriented formats are suitable for loading and exporting data, but they are not optimal for analytical queries that often access only a subset of columns. Row-oriented formats also do not support compression or encoding techniques that can reduce the data size and improve the query performance.
On the other hand, some data formats, such as ORC and Parquet, are column-oriented, meaning that they store data as a collection of columns, each with a specific data type. Column-oriented formats are ideal for analytical queries that often filter, aggregate, or join data by columns. Column-oriented formats also support compression and encoding techniques that can reduce the data size and improve the query performance. For example, Parquet supports dictionary encoding, which replaces repeated values with numeric codes, and run-length encoding, which replaces consecutive identical values with a single value and a count. Parquet also supports various compression algorithms, such as Snappy, GZIP, and ZSTD, that can further reduce the data size and improve the query performance.
Therefore, creating an AWS Glue extract, transform, and load (ETL) job to read from the .csv structured data source and writing the data into the data lake in Apache Parquet format will meet the requirements most cost-effectively. AWS Glue is a fully managed service that provides a serverless data integration platform for data preparation, data cataloging, and data loading. AWS Glue ETL jobs allow you to transform and load data from various sources into various targets, using either a graphical interface (AWS Glue Studio) or a code-based interface (AWS Glue console or AWS Glue API). By using AWS Glue ETL jobs, you can easily convert the data from CSV to Parquet format, without having to write or manage any code. Parquet is a column-oriented format that allows Athena to scan only the relevant columns and skip the rest, reducing the amount of data read from S3. This solution will also reduce the cost of Athena queries, as Athena charges based on the amount of data scanned from S3.
The other options are not as cost-effective as creating an AWS Glue ETL job to write the data into the data lake in Parquet format. Using an AWS Glue PySpark job to ingest the source data into the data lake in .csv format will not improve the query performance or reduce the query cost, as .csv is a row-oriented format that does not support columnar access or compression. Creating an AWS Glue ETL job to ingest the data into the data lake in JSON format will not improve the query performance or reduce the query cost, as JSON is also a row-oriented format that does not support columnar access or compression. Using an AWS Glue PySpark job to ingest the source data into the data lake in Apache Avro format will improve the query performance, as Avro is a column-oriented format that supports compression and encoding, but it will require more operational effort, as you will need to write and maintain PySpark code to convert the data from CSV to Avro format. Reference:
Amazon Athena
Choosing the Right Data Format
AWS Glue
[AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide], Chapter 5: Data Analysis and Visualization, Section 5.1: Amazon Athena

질문 # 141
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적중율좋은Data-Engineer-Associate유효한최신덤프자료덤프문제

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최신 AWS Certified Data Engineer Data-Engineer-Associate 무료샘플문제 (Q136-Q141):

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