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| Section | Weight | Objectives |
|---|---|---|
| Data Store Management | 26% | - Optimize storage performance and cost - Select appropriate data storage solutions |
| Data Operations and Support | 22% | - Monitor and maintain data pipelines - Troubleshoot data workflow issues |
| Data Security and Governance | 18% | - Implement data security controls - Apply governance and compliance best practices |
| Data Ingestion and Transformation | 34% | - Ingest and transform data using AWS services - Build and manage data pipelines |
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NEW QUESTION # 231
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?
Answer: C
Explanation:
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. References:
* 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
NEW QUESTION # 232
A data engineer configured an AWS Glue Data Catalog for data that is stored in Amazon S3 buckets. The data engineer needs to configure the Data Catalog to receive incremental updates.
The data engineer sets up event notifications for the S3 bucket and creates an Amazon Simple Queue Service (Amazon SQS) queue to receive the S3 events.
Which combination of steps should the data engineer take to meet these requirements with LEAST operational overhead? (Select TWO.)
Answer: A,D
Explanation:
The requirement is to update the AWS Glue Data Catalog incrementally based on S3 events. Using an S3 event-based approach is the most automated and operationally efficient solution.
* A. Create an S3 event-based AWS Glue crawler:
* An event-based Glue crawler can automatically update the Data Catalog when new data arrives in the S3 bucket. This ensures incremental updates with minimal operational overhead.
NEW QUESTION # 233
A company receives test results from testing facilities that are located around the world. The company stores the test results in millions of 1 KB JSON files in an Amazon S3 bucket. A data engineer needs to process the files, convert them into Apache Parquet format, and load them into Amazon Redshift tables. The data engineer uses AWS Glue to process the files, AWS Step Functions to orchestrate the processes, and Amazon EventBridge to schedule jobs.
The company recently added more testing facilities. The time required to process files is increasing. The data engineer must reduce the data processing time.
Which solution will MOST reduce the data processing time?
Answer: C
Explanation:
* Problem Analysis:
* Millions of 1 KB JSON files in S3 are being processed and converted to Apache Parquet format using AWS Glue.
* Processing time is increasing due to the additional testing facilities.
* The goal is toreduce processing timewhile using the existing AWS Glue framework.
* Key Considerations:
* AWS Glue offers thedynamic frame file-groupingfeature, which consolidates small files into larger, more efficient datasets during processing.
* Grouping smaller files reduces overhead and speeds up processing.
* Solution Analysis:
* Option A: Lambda for File Grouping
* Using Lambda to group files would add complexity and operational overhead. Glue already offers built-in grouping functionality.
* Option B: AWS Glue Dynamic Frame File-Grouping
* This option directly addresses the issue by grouping small files during Glue job execution.
* Minimizes data processing time with no extra overhead.
* Option C: Redshift COPY Command
* COPY directly loads raw files but is not designed for pre-processing (conversion to Parquet).
* Option D: Amazon EMR
* While EMR is powerful, replacing Glue with EMR increases operational complexity.
* Final Recommendation:
* UseAWS Glue dynamic frame file-groupingfor optimized data ingestion and processing.
:
AWS Glue Dynamic Frames
Optimizing Glue Performance
NEW QUESTION # 234
A retail company is developing a data lake solution on Amazon S3 to analyze historical sales data. The solution needs to support frequent schema changes as new product attributes are added. The company must also be able to query point-in-time historical data snapshots for compliance reporting. The solution must provide atomicity, consistency, isolation, and durability (ACID) transaction guarantees for concurrent write operations.
Which solution will meet these requirements?
Answer: A
Explanation:
The correct answer is B because Apache Iceberg is designed for analytical data lake tables that need schema evolution, transactional consistency, and historical snapshot access. AWS Glue supports Apache Iceberg for data lakes and can read and write Iceberg tables in Amazon S3 through the AWS Glue Data Catalog. The AWS Glue documentation also shows Iceberg tables being created with TBLPROPERTIES ( " format-version " = " 2 " ), which is the correct table format direction for modern Iceberg capabilities. CSV with scheduled conversion does not provide ACID transactions or point-in-time table snapshots. S3 Versioning tracks object versions but does not provide table-level ACID semantics. DynamoDB is not the best fit for an S3-based analytical data lake requirement.
NEW QUESTION # 235
A security company stores IoT data that is in JSON format in an Amazon S3 bucket. The data structure can change when the company upgrades the IoT devices. The company wants to create a data catalog that includes the IoT data. The company's analytics department will use the data catalog to index the data.
Which solution will meet these requirements MOST cost-effectively?
Answer: C
Explanation:
The best solution to meet the requirements of creating a data catalog that includes the IoT data, and allowing the analytics department to index the data, most cost-effectively, is to create an Amazon Athena workgroup, explore the data that is in Amazon S3 by using Apache Spark through Athena, and provide the Athena workgroup schema and tables to the analytics department.
Amazon Athena is a serverless, interactive query service that makes it easy to analyze data directly in Amazon S3 using standard SQL or Python1. Amazon Athena also supports Apache Spark, an open-source distributed processing framework that can run large-scale data analytics applications across clusters of servers2. You can use Athena to run Spark code on data in Amazon S3 without having to set up, manage, or scale any infrastructure. You can also use Athena to create and manage external tables that point to your data in Amazon S3, and store them in an external data catalog, such as AWS Glue Data Catalog, Amazon Athena Data Catalog, or your own Apache Hive metastore3. You can create Athena workgroups to separate query execution and resource allocation based on different criteria, such as users, teams, or applications4. You can share the schemas and tables in your Athena workgroup with other users or applications, such as Amazon QuickSight, for data visualization and analysis5.
Using Athena and Spark to create a data catalog and explore the IoT data in Amazon S3 is the most cost- effective solution, as you pay only for the queries you run or the compute you use, and you pay nothing when the service is idle1. You also save on the operational overhead and complexity of managing data warehouse infrastructure, as Athena and Spark are serverless and scalable. You can also benefit from the flexibility and performance of Athena and Spark, as they support various data formats, including JSON, and can handle schema changes and complex queries efficiently.
Option A is not the best solution, as creating an AWS Glue Data Catalog, configuring an AWS Glue Schema Registry, creating a new AWS Glue workload to orchestrate the ingestion of the data that the analytics department will use into Amazon Redshift Serverless, would incur more costs and complexity than using Athena and Spark. AWS Glue Data Catalog is a persistent metadata store that contains table definitions, job definitions, and other control information to help you manage your AWS Glue components6. AWS Glue Schema Registry is a service that allows you to centrally store and manage the schemas of your streaming data in AWS Glue Data Catalog7. AWS Glue is a serverless data integration service that makes it easy to prepare, clean, enrich, and move data between data stores8. Amazon Redshift Serverless is a feature of Amazon Redshift, a fully managed data warehouse service, that allows you to run and scale analytics without having to manage data warehouse infrastructure9. While these services are powerful and useful for many data engineering scenarios, they are not necessary or cost-effective for creating a data catalog and indexing the IoT data in Amazon S3. AWS Glue Data Catalog and Schema Registry charge you based on the number of objects stored and the number of requests made67. AWS Glue charges you based on the compute time and the data processed by your ETL jobs8. Amazon Redshift Serverless charges you based on the amount of data scanned by your queries and the compute time used by your workloads9. These costs can add up quickly, especially if you have large volumes of IoT data and frequent schema changes. Moreover, using AWS Glue and Amazon Redshift Serverless would introduce additional latency and complexity, as you would have to ingest the data from Amazon S3 to Amazon Redshift Serverless, and then query it from there, instead of querying it directly from Amazon S3 using Athena and Spark.
Option B is not the best solution, as creating an Amazon Redshift provisioned cluster, creating an Amazon Redshift Spectrum database for the analytics department to explore the data that is in Amazon S3, and creating Redshift stored procedures to load the data into Amazon Redshift, would incur more costs and complexity than using Athena and Spark. Amazon Redshift provisioned clusters are clusters that you create and manage by specifying the number and type of nodes, and the amount of storage and compute capacity10. Amazon Redshift Spectrum is a feature of Amazon Redshift that allows you to query and join data across your data warehouse and your data lake using standard SQL11. Redshift stored procedures are SQL statements that you can define and store in Amazon Redshift, and then call them by using the CALL command12. While these features are powerful and useful for many data warehousing scenarios, they are not necessary or cost-effective for creating a data catalog and indexing the IoT data in Amazon S3. Amazon Redshift provisioned clusters charge you based on the node type, the number of nodes, and the duration of the cluster10. Amazon Redshift Spectrum charges you based on the amount of data scanned by your queries11.
These costs can add up quickly, especially if you have large volumes of IoT data and frequent schema changes. Moreover, using Amazon Redshift provisioned clusters and Spectrum would introduce additional latency and complexity, as you would have to provision and manage the cluster, create an external schema and database for the data in Amazon S3, and load the data into the cluster using stored procedures, instead of querying it directly from Amazon S3 using Athena and Spark.
Option D is not the best solution, as creating an AWS Glue Data Catalog, configuring an AWS Glue Schema Registry, creating AWS Lambda user defined functions (UDFs) by using the Amazon Redshift Data API, and creating an AWS Step Functions job to orchestrate the ingestion of the data that the analytics department will use into Amazon Redshift Serverless, would incur more costs and complexity than using Athena and Spark. AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers13. AWS Lambda UDFs are Lambda functions that you can invoke from within an Amazon Redshift query. Amazon Redshift Data API is a service that allows you to run SQL statements on Amazon Redshift clusters using HTTP requests, without needing a persistent connection. AWS Step Functions is a service that lets you coordinate multiple AWS services into serverless workflows. While these services are powerful and useful for many data engineering scenarios, they are not necessary or cost-effective for creating a data catalog and indexing the IoT data in Amazon S3. AWS Glue Data Catalog and Schema Registry charge you based on the number of objects stored and the number of requests made67. AWS Lambda charges you based on the number of requests and the duration of your functions13. Amazon Redshift Serverless charges you based on the amount of data scanned by your queries and the compute time used by your workloads9. AWS Step Functions charges you based on the number of state transitions in your workflows. These costs can add up quickly, especially if you have large volumes of IoT data and frequent schema changes. Moreover, using AWS Glue, AWS Lambda, Amazon Redshift Data API, and AWS Step Functions would introduce additional latency and complexity, as you would have to create and invoke Lambda functions to ingest the data from Amazon S3 to Amazon Redshift Serverless using the Data API, and coordinate the ingestion process using Step Functions, instead of querying it directly from Amazon S3 using Athena and Spark. References:
What is Amazon Athena?
Apache Spark on Amazon Athena
Creating tables, updating the schema, and adding new partitions in the Data Catalog from AWS Glue ETL jobs Managing Athena workgroups Using Amazon QuickSight to visualize data in Amazon Athena AWS Glue Data Catalog AWS Glue Schema Registry What is AWS Glue?
Amazon Redshift Serverless
Amazon Redshift provisioned clusters
Querying external data using Amazon Redshift Spectrum
Using stored procedures in Amazon Redshift
What is AWS Lambda?
[Creating and using AWS Lambda UDFs]
[Using the Amazon Redshift Data API]
[What is AWS Step Functions?]
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
NEW QUESTION # 236
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