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| Section | Weight | Objectives |
|---|---|---|
| Data Security and Governance | 18% | - Protect sensitive data - Enforce compliance and data governance
|
| Data Ingestion and Transformation | 34% | - Transform and enrich data
|
| Data Operations and Support | 22% | - Automate operational tasks - Backup, restore, and disaster recovery - Ensure reliability and scalability - Monitor and troubleshoot data pipelines
|
| Data Store Management | 26% | - Optimize storage performance and cost - Design and implement data storage solutions
|
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NEW QUESTION # 194
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?
Answer: C
Explanation:
The NEST TO MAP transformation allows you to combine multiple columns into a single column that contains a JSON object with key-value pairs. This is the easiest way to achieve the desired format for the physical address data, as you can simply select the columns to nest and specify the keys for each column. The NEST TO ARRAY transformation creates a single column that contains an array of values, which is not the same as the JSON object format. The PIVOT transformation reshapes the data by creating new columns from unique values in a selected column, which is not applicable for this use case. Writing a Lambda function in Python requires more coding effort than using AWS Glue DataBrew, which provides a visual and interactive interface for data transformations. References:
7 most common data preparation transformations in AWS Glue DataBrew (Section: Nesting and unnesting columns) NEST TO MAP - AWS Glue DataBrew (Section: Syntax)
NEW QUESTION # 195
A data engineer is using AWS Glue to build an extract, transform, and load (ETL) pipeline that processes streaming data from sensors. The pipeline sends the data to an Amazon S3 bucket in near real-time. The data engineer also needs to perform transformations and join the incoming data with metadata that is stored in an Amazon RDS for PostgreSQL database. The data engineer must write the results back to a second S3 bucket in Apache Parquet format.
Which solution will meet these requirements?
Answer: D
Explanation:
* AWS Glue streaming jobs are purpose-built for low-latency, near real-time ETL pipelines.
* Glue Studio allows visual job authoring and supports complex transformations, including joins with Amazon RDS databases, through JDBC connectors.
* Data can be written directly to S3 in Apache Parquet format using built-in AWS Glue support.
Options B and C are more suitable for batch or development/testing. Option D is for custom batch jobs, not real-time processing.
"You can use AWS Glue streaming jobs with Glue Studio to process streaming data, perform transformations, join with RDS data sources, and write results in formats like Parquet to S3." Reference: AWS Glue Studio and Streaming Jobs - AWS Documentation
NEW QUESTION # 196
A company uses a variety of AWS and third-party data stores. The company wants to consolidate all the data into a central data warehouse to perform analytics. Users need fast response times for analytics queries.
The company uses Amazon QuickSight in direct query mode to visualize the data. Users normally run queries during a few hours each day with unpredictable spikes.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: A
Explanation:
Problem Analysis:
The company requires a centralized data warehouse for consolidating data from various sources.
They use Amazon QuickSight in direct query mode, necessitating fast response times for analytical queries.
Users query the data intermittently, with unpredictable spikes during the day.
Operational overhead should be minimal.
Key Considerations:
The solution must support fast, SQL-based analytics.
It must handle unpredictable spikes efficiently.
Must integrate seamlessly with QuickSight for direct querying.
Minimize operational complexity and scaling concerns.
Solution Analysis:
Option A: Amazon Redshift Serverless
Redshift Serverless eliminates the need for provisioning and managing clusters.
Automatically scales compute capacity up or down based on query demand.
Reduces operational overhead by handling performance optimization.
Fully integrates with Amazon QuickSight, ensuring low-latency analytics.
Reduces costs as it charges only for usage, making it ideal for workloads with intermittent spikes.
Option B: Amazon Athena with S3 (Apache Parquet)
Athena supports querying data directly from S3 in Parquet format.
While it's cost-effective, performance depends on the size and complexity of the data.
It is not optimized for high-speed analytics needed by QuickSight in direct query mode.
Option C: Amazon Redshift Provisioned Clusters
Requires manual cluster provisioning, scaling, and maintenance.
Higher operational overhead compared to Redshift Serverless.
Option D: Amazon Aurora PostgreSQL
Aurora is optimized for transactional databases, not data warehousing or analytics.
Does not meet the requirement for fast analytics queries.
Final Recommendation:
Amazon Redshift Serverless is the best choice for this use case because it provides fast analytics, integrates natively with QuickSight, and minimizes operational complexity while efficiently handling unpredictable spikes.
Amazon Redshift Serverless Overview
Amazon QuickSight and Redshift Integration
Athena vs. Redshift
NEW QUESTION # 197
A data engineer needs to join data from multiple sources to perform a one-time analysis job. The data is stored in Amazon DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3.
Which solution will meet this requirement MOST cost-effectively?
Answer: C
Explanation:
Amazon Athena Federated Query is a feature that allows you to query data from multiple sources using standard SQL. You can use Athena Federated Query to join data from Amazon DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3, as well as other data sources such as MongoDB, Apache HBase, and Apache Kafka1. Athena Federated Query is a serverless and interactive service, meaning you do not need to provision or manage any infrastructure, and you only pay for the amount of data scanned by your queries. Athena Federated Query is the most cost-effective solution for performing a one-time analysis job on data from multiple sources, as it eliminates the need to copy or move data, and allows you to query data directly from the source.
The other options are not as cost-effective as Athena Federated Query, as they involve additional steps or costs. Option A requires you to provision and pay for an Amazon EMR cluster, which can be expensive and time-consuming for a one-time job. Option B requires you to copy or move data from DynamoDB, RDS, and Redshift to S3, which can incur additional costs for data transfer and storage, and also introduce latency and complexity. Option D requires you to have an existing Redshift cluster, which can be costly and may not be necessary for a one-time job. Option D also does not support querying data from RDS directly, so you would need to use Redshift Federated Query to access RDS data, which adds another layer of complexity2. Reference:
Amazon Athena Federated Query
Redshift Spectrum vs Federated Query
NEW QUESTION # 198
A data engineer configures a large number of AWS Glue jobs that all start up around the same time. All the jobs run for less than 1 hour in the same subnet of the same VPC. All the AWS Glue jobs run on a G.1X worker type.
Some of the jobs occasionally fail with the following error: "The specified subnet does not have enough free addresses to satisfy the request." What is the likely root cause of the error?
Answer: B
NEW QUESTION # 199
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