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| Section | Weight | Objectives |
|---|---|---|
| Data Ingestion and Transformation | 34% | - Ingest and transform data using AWS services - Build and manage data pipelines |
| Data Operations and Support | 22% | - Monitor and maintain data pipelines - Troubleshoot data workflow issues |
| Data Store Management | 26% | - Select appropriate data storage solutions - Optimize storage performance and cost |
| Data Security and Governance | 18% | - Apply governance and compliance best practices - Implement data security controls |
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NEW QUESTION # 103
A company stores datasets in JSON format and .csv format in an Amazon S3 bucket. The company has Amazon RDS for Microsoft SQL Server databases, Amazon DynamoDB tables that are in provisioned capacity mode, and an Amazon Redshift cluster. A data engineering team must develop a solution that will give data scientists the ability to query all data sources by using syntax similar to SQL.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: B
Explanation:
The best solution to meet the requirements of giving data scientists the ability to query all data sources by using syntax similar to SQL with the least operational overhead is to use AWS Glue to crawl the data sources, store metadata in the AWS Glue Data Catalog, use Amazon Athena to query the data, use SQL for structured data sources, and use PartiQL for data that is stored in JSON format.
AWS Glue is a serverless data integration service that makes it easy to prepare, clean, enrich, and move data between data stores1. AWS Glue crawlers are processes that connect to a data store, progress through a prioritized list of classifiers to determine the schema for your data, and then create metadata tables in the Data Catalog2. The Data Catalog is a persistent metadata store that contains table definitions, job definitions, and other control information to help you manage your AWS Glue components3. You can use AWS Glue to crawl the data sources, such as Amazon S3, Amazon RDS for Microsoft SQL Server, and Amazon DynamoDB, and store the metadata in the Data Catalog.
Amazon Athena is a serverless, interactive query service that makes it easy to analyze data directly in Amazon S3 using standard SQL or Python4. Amazon Athena also supports PartiQL, a SQL-compatible query language that lets you query, insert, update, and delete data from semi-structured and nested data, such as JSON. You can use Amazon Athena to query the data from the Data Catalog using SQL for structured data sources, such as .csv files and relational databases, and PartiQL for data that is stored in JSON format. You can also use Athena to query data from other data sources, such as Amazon Redshift, using federated queries.
Using AWS Glue and Amazon Athena to query all data sources by using syntax similar to SQL is the least operational overhead solution, as you do not need to provision, manage, or scale any infrastructure, and you pay only for the resources you use. AWS Glue charges you based on the compute time and the data processed by your crawlers and ETL jobs1. Amazon Athena charges you based on the amount of data scanned by your queries. You can also reduce the cost and improve the performance of your queries by using compression, partitioning, and columnar formats for your data in Amazon S3.
Option B is not the best solution, as using AWS Glue to crawl the data sources, store metadata in the AWS Glue Data Catalog, and use Redshift Spectrum to query the data, would incur more costs and complexity than using Amazon Athena. Redshift Spectrum is a feature of Amazon Redshift, a fully managed data warehouse service, that allows you to query and join data across your data warehouse and your data lake using standard SQL. While Redshift Spectrum is powerful and useful for many data warehousing scenarios, it is not necessary or cost-effective for querying all data sources by using syntax similar to SQL. Redshift Spectrum charges you based on the amount of data scanned by your queries, which is similar to Amazon Athena, but it also requires you to have an Amazon Redshift cluster, which charges you based on the node type, the number of nodes, and the duration of the cluster5. These costs can add up quickly, especially if you have large volumes of data and complex queries. Moreover, using Redshift Spectrum would introduce additional latency and complexity, as you would have to provision and manage the cluster, and create an external schema and database for the data in the Data Catalog, instead of querying it directly from Amazon Athena.
Option C is not the best solution, as using AWS Glue to crawl the data sources, store metadata in the AWS Glue Data Catalog, use AWS Glue jobs to transform data that is in JSON format to Apache Parquet or .csv format, store the transformed data in an S3 bucket, and use Amazon Athena to query the original and transformed data from the S3 bucket, would incur more costs and complexity than using Amazon Athena with PartiQL. AWS Glue jobs are ETL scripts that you can write in Python or Scala to transform your data and load it to your target data store. Apache Parquet is a columnar storage format that can improve the performance of analytical queries by reducing the amount of data that needs to be scanned and providing efficient compression and encoding schemes6. While using AWS Glue jobs and Parquet can improve the performance and reduce the cost of your queries, they would also increase the complexity and the operational overhead of the data pipeline, as you would have to write, run, and monitor the ETL jobs, and store the transformed data in a separate location in Amazon S3. Moreover, using AWS Glue jobs and Parquet would introduce additional latency, as you would have to wait for the ETL jobs to finish before querying the transformed data.
Option D is not the best solution, as using AWS Lake Formation to create a data lake, use Lake Formation jobs to transform the data from all data sources to Apache Parquet format, store the transformed data in an S3 bucket, and use Amazon Athena or Redshift Spectrum to query the data, would incur more costs and complexity than using Amazon Athena with PartiQL. AWS Lake Formation is a service that helps you centrally govern, secure, and globally share data for analytics and machine learning7. Lake Formation jobs are ETL jobs that you can create and run using the Lake Formation console or API. While using Lake Formation and Parquet can improve the performance and reduce the cost of your queries, they would also increase the complexity and the operational overhead of the data pipeline, as you would have to create, run, and monitor the Lake Formation jobs, and store the transformed data in a separate location in Amazon S3. Moreover, using Lake Formation and Parquet would introduce additional latency, as you would have to wait for the Lake Formation jobs to finish before querying the transformed data.
Furthermore, using Redshift Spectrum to query the data would also incur the same costs and complexity as mentioned in option B.
References:
What is Amazon Athena?
Data Catalog and crawlers in AWS Glue
AWS Glue Data Catalog
Columnar Storage Formats
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
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?]
[PartiQL for Amazon Athena]
[Federated queries in Amazon Athena]
[Amazon Athena pricing]
[Top 10 performance tuning tips for Amazon Athena]
[AWS Glue ETL jobs]
[AWS Lake Formation jobs]
NEW QUESTION # 104
A company uses AWS Glue Data Catalog to index data that is uploaded to an Amazon S3 bucket every day.
The company uses a daily batch processes in an extract, transform, and load (ETL) pipeline to upload data from external sources into the S3 bucket.
The company runs a daily report on the S3 data. Some days, the company runs the report before all the daily data has been uploaded to the S3 bucket. A data engineer must be able to send a message that identifies any incomplete data to an existing Amazon Simple Notification Service (Amazon SNS) topic.
Which solution will meet this requirement with the LEAST operational overhead?
Answer: C
Explanation:
AWS Glue workflows are designed to orchestrate the ETL pipeline, and you can createdata quality checksto ensure the uploaded datasets are complete before running reports. If there is an issue with the data, AWS Glue workflows can trigger anAmazon EventBridge eventthat sends a message to anSNS topic.
* AWS Glue Workflows:
* AWS Glue workflows allow users to automate and monitor complex ETL processes. You can includedata quality actionsto check for null values, data types, and other consistency checks.
* In the event of incomplete data, anEventBridgeevent can be generated to notify via SNS.
Reference:AWS Glue Workflows
Alternatives Considered:
A (Airflow cluster): Managed Airflow introduces more operational overhead and complexity compared to Glue workflows.
B (EMR cluster): Setting up an EMR cluster is also more complex compared to the Glue-centric solution.
D (Lambda functions): While Lambda functions can work, using Glue workflows offers a more integrated and lower operational overhead solution.
References:
AWS Glue Workflow Documentation
NEW QUESTION # 105
A company is building an inventory management system and an inventory reordering system to automatically reorder products. Both systems use Amazon Kinesis Data Streams. The inventory management system uses the Amazon Kinesis Producer Library (KPL) to publish data to a stream. The inventory reordering system uses the Amazon Kinesis Client Library (KCL) to consume data from the stream. The company configures the stream to scale up and down as needed.
Before the company deploys the systems to production, the company discovers that the inventory reordering system received duplicated data.
Which factors could have caused the reordering system to receive duplicated data? (Select TWO.)
Answer: C,E
Explanation:
Problem Analysis:
The company uses Kinesis Data Streams for both inventory management and reordering.
The Kinesis Producer Library (KPL) publishes data, and the Kinesis Client Library (KCL) consumes data.
Duplicate records were observed in the inventory reordering system.
Key Considerations:
Kinesis streams are designed for durability but may produce duplicates under certain conditions.
Factors such as network timeouts, shard splits, or changes in record processors can cause duplication.
Solution Analysis:
Option A: Network-Related Timeouts
If the producer (KPL) experiences network timeouts, it retries data submission, potentially causing duplicates.
Option B: High IteratorAgeMilliseconds
High iterator age suggests delays in processing but does not directly cause duplication.
Option C: Changes in Shards or Processors
Changes in the number of shards or record processors can lead to re-processing of records, causing duplication.
Option D: AggregationEnabled Set to True
AggregationEnabled controls the aggregation of multiple records into one, but it does not cause duplication.
Option E: High max_records Value
A high max_records value increases batch size but does not lead to duplication.
Final Recommendation:
Network-related timeouts and changes in shards or processors are the most likely causes of duplicate data in this scenario.
Amazon Kinesis Data Streams Best Practices
Kinesis Producer Library (KPL) Overview
Kinesis Client Library (KCL) Overview
NEW QUESTION # 106
A data engineer needs to use Amazon Neptune to develop graph applications.
Which programming languages should the engineer use to develop the graph applications? (Select TWO.)
Answer: A,C
Explanation:
Amazon Neptune supports graph applications using Gremlin and SPARQL as query languages. Neptune is a fully managed graph database service that supports both property graph and RDF graph models.
Option A: Gremlin
Gremlin is a query language for property graph databases, which is supported by Amazon Neptune. It allows the traversal and manipulation of graph data in the property graph model.
Option D: SPARQL
SPARQL is a query language for querying RDF graph data in Neptune. It is used to query, manipulate, and retrieve information stored in RDF format.
Other options:
SQL (Option B) and ANSI SQL (Option C) are traditional relational database query languages and are not used for graph databases.
Spark SQL (Option E) is related to Apache Spark for big data processing, not for querying graph databases.
Reference:
Amazon Neptune Documentation
Gremlin Documentation
SPARQL Documentation
NEW QUESTION # 107
A company has a frontend ReactJS website that uses Amazon API Gateway to invoke REST APIs. The APIs perform the functionality of the website. A data engineer needs to write a Python script that can be occasionally invoked through API Gateway. The code must return results to API Gateway.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: D
Explanation:
AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers. You can use Lambda to create functions that perform custom logic and integrate with other AWS services, such as API Gateway. Lambda automatically scales your application by running code in response to each trigger. You pay only for the compute time you consume1.
Amazon ECS is a fully managed container orchestration service that allows you to run and scale containerized applications on AWS. You can use ECS to deploy, manage, and scale Docker containers using either Amazon EC2 instances or AWS Fargate, a serverless compute engine for containers2.
Amazon EKS is a fully managed Kubernetes service that allows you to run Kubernetes clusters on AWS without needing to install, operate, or maintain your own Kubernetes control plane. You can use EKS to deploy, manage, and scale containerized applications using Kubernetes on AWS3.
The solution that meets the requirements with the least operational overhead is to create an AWS Lambda Python function with provisioned concurrency. This solution has the following advantages:
* It does not require you to provision, manage, or scale any servers or clusters, as Lambda handles all the infrastructure for you. This reduces the operational complexity and cost of running your code.
* It allows you to write your Python script as a Lambda function and integrate it with API Gateway using a simple configuration. API Gateway can invoke your Lambda function synchronously or asynchronously, and return the results to the frontend website.
* It ensures that your Lambda function is ready to respond to API requests without any cold start delays, by using provisioned concurrency. Provisioned concurrency is a feature that keeps your function initialized and hyper-ready to respond in double-digit milliseconds. You can specify the number of concurrent executions that you want to provision for your function.
Option A is incorrect because it requires you to deploy a custom Python script on an Amazon ECS cluster.
This solution has the following disadvantages:
* It requires you to provision, manage, and scale your own ECS cluster, either using EC2 instances or Fargate. This increases the operational complexity and cost of running your code.
* It requires you to package your Python script as a Docker container image and store it in a container registry, such as Amazon ECR or Docker Hub. This adds an extra step to your deployment process.
* It requires you to configure your ECS cluster to integrate with API Gateway, either using an Application Load Balancer or a Network Load Balancer. This adds another layer of complexity to your architecture.
Option C is incorrect because it requires you to deploy a custom Python script that can integrate with API Gateway on Amazon EKS. This solution has the following disadvantages:
* It requires you to provision, manage, and scale your own EKS cluster, either using EC2 instances or Fargate. This increases the operational complexity and cost of running your code.
* It requires you to package your Python script as a Docker container image and store it in a container registry, such as Amazon ECR or Docker Hub. This adds an extra step to your deployment process.
* It requires you to configure your EKS cluster to integrate with API Gateway, either using an Application Load Balancer, a Network Load Balancer, or a service of type LoadBalancer. This adds another layer of complexity to your architecture.
Option D is incorrect because it requires you to create an AWS Lambda function and ensure that the function is warm by scheduling an Amazon EventBridge rule to invoke the Lambda function every 5 minutes by using mock events. This solution has the following disadvantages:
* It does not guarantee that your Lambda function will always be warm, as Lambda may scale down your function if it does not receive any requests for a long period of time. This may cause cold start delays when your function is invoked by API Gateway.
* It incurs unnecessary costs, as you pay for the compute time of your Lambda function every time it is invoked by the EventBridge rule, even if it does not perform any useful work1.
References:
* 1: AWS Lambda - Features
* 2: Amazon Elastic Container Service - Features
* 3: Amazon Elastic Kubernetes Service - Features
* [4]: Building API Gateway REST API with Lambda integration - Amazon API Gateway
* [5]: Improving latency with Provisioned Concurrency - AWS Lambda
* [6]: Integrating Amazon ECS with Amazon API Gateway - Amazon Elastic Container Service
* [7]: Integrating Amazon EKS with Amazon API Gateway - Amazon Elastic Kubernetes Service
* [8]: Managing concurrency for a Lambda function - AWS Lambda
NEW QUESTION # 108
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