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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Ingestion and Transformation | 34% | - Build and manage data pipelines - Ingest and transform data using AWS services |
| Topic 2: Data Security and Governance | 18% | - Implement data security controls - Apply governance and compliance best practices |
| Topic 3: Data Operations and Support | 22% | - Troubleshoot data workflow issues - Monitor and maintain data pipelines |
| Topic 4: Data Store Management | 26% | - Select appropriate data storage solutions - Optimize storage performance and cost |
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NEW QUESTION # 209
A company runs a data pipeline that uses AWS Step Functions to orchestrate AWS Lambda functions and AWS Glue jobs. The Lambda functions and AWS Glue jobs require access to multiple Amazon RDS databases. The Lambda functions and AWS Glue jobs already have access to the VPC that hosts the RDS databases.
Which solution will meet these requirements in the MOST secure way?
Answer: D
Explanation:
AWS security best practices require avoiding long-term credentials and enforcing the principle of least privilege. Using IAM roles with role assumption is the most secure approach for granting temporary, scoped access to AWS resources.
In this solution, an IAM role is created with permissions to access the Amazon RDS databases. A second IAM role is assigned to the AWS Lambda functions and AWS Glue jobs with permission to assume the RDS- access role. This approach eliminates hard-coded credentials, enables automatic credential rotation through AWS Security Token Service (AWS STS), and provides clear separation of duties.
Using the root user or long-term access keys is explicitly discouraged by AWS. Creating IAM users for services violates best practices, as AWS services should use IAM roles, not users. Embedding credentials in JDBC connection strings exposes sensitive information and increases the risk of credential leakage.
Therefore, using IAM role assumption provides the strongest security posture, auditability, and compliance with AWS Well-Architected Framework guidance.
NEW QUESTION # 210
A global company currently uses Amazon Redshift to store data and Amazon Quick Suite (previously known as Amazon QuickSight) to generate reports.
A team of business analysts have varying levels of technical expertise. Some analysts lack SQL knowledge.
All the analysts need to create new reports frequently. The company wants to use natural program language queries to create dashboards and reports more efficiently.
Which solution will meet these requirements with the LEAST operational effort?
Answer: A
Explanation:
Option B is correct because Amazon Q in QuickSight is the AWS feature built specifically for natural language interaction with business data. AWS documentation explains that Amazon Q in QuickSight lets users ask questions in natural language, generate summaries and data stories, and create visuals from author prompts. AWS Prescriptive Guidance also notes that users can generate insights with natural language prompts and that they do not have to write SQL queries or learn complex BI tooling. That matches the requirement that some analysts lack SQL knowledge and need to create reports frequently with minimal effort.
Option A is not the best answer because zero-ETL access addresses data movement and integration, not natural-language dashboard creation. Option C introduces another analytics platform and therefore more operational effort. Option D provides access to data, but federated querying still does not address the key requirement for natural-language dashboard and report generation for nontechnical analysts. The AWS-native feature that directly solves this problem with the least additional management is Amazon Q in QuickSight.
NEW QUESTION # 211
A company uploads .csv files to an Amazon S3 bucket. The company's data platform team has set up an AWS Glue crawler to perform data discovery and to create the tables and schemas.
An AWS Glue job writes processed data from the tables to an Amazon Redshift database. The AWS Glue job handles column mapping and creates the Amazon Redshift tables in the Redshift database appropriately.
If the company reruns the AWS Glue job for any reason, duplicate records are introduced into the Amazon Redshift tables. The company needs a solution that will update the Redshift tables without duplicates.
Which solution will meet these requirements?
Answer: B
Explanation:
To avoid duplicate records in Amazon Redshift, the most effective solution is to perform the ETL in a way that first loads the data into a staging table and then uses SQL commands like MERGE or UPDATE to insert new records and update existing records without introducing duplicates.
Using Staging Tables in Redshift:
The AWS Glue job can write data to a staging table in Redshift. Once the data is loaded, SQL commands can be executed to compare the staging data with the target table and update or insert records appropriately. This ensures no duplicates are introduced during re-runs of the Glue job.
Reference:
Alternatives Considered:
B (MySQL upsert): This introduces unnecessary complexity by involving another database (MySQL).
C (Spark dropDuplicates): While Spark can eliminate duplicates, handling duplicates at the Redshift level with a staging table is a more reliable and Redshift-native solution.
D (AWS Glue ResolveChoice): The ResolveChoice transform in Glue helps with column conflicts but does not handle record-level duplicates effectively.
Amazon Redshift MERGE Statements
Staging Tables in Amazon Redshift
NEW QUESTION # 212
A company currently stores all of its data in Amazon S3 by using the S3 Standard storage class.
A data engineer examined data access patterns to identify trends. During the first 6 months, most data files are accessed several times each day. Between 6 months and 2 years, most data files are accessed once or twice each month. After 2 years, data files are accessed only once or twice each year.
The data engineer needs to use an S3 Lifecycle policy to develop new data storage rules. The new storage solution must continue to provide high availability.
Which solution will meet these requirements in the MOST cost-effective way?
Answer: B
Explanation:
To achieve the most cost-effective storage solution, the data engineer needs to use an S3 Lifecycle policy that transitions objects to lower-cost storage classes based on their access patterns, and deletes them when they are no longer needed. The storage classes should also provide high availability, which means they should be resilient to the loss of data in a single Availability Zone1. Therefore, the solution must include the following steps:
* Transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 months. S3 Standard-IA is designed for data that is accessed less frequently, but requires rapid access when needed. It offers the same high durability, throughput, and low latency as S3 Standard, but with a lower storage cost and a retrieval fee2. Therefore, it is suitable for data files that are accessed once or twice each month. S3 Standard-IA also provides high availability, as it stores data redundantly across multiple Availability Zones1.
* Transfer objects to S3 Glacier Deep Archive after 2 years. S3 Glacier Deep Archive is the lowest-cost storage class that offers secure and durable storage for data that is rarely accessed and can tolerate a 12- hour retrieval time. It is ideal for long-term archiving and digital preservation3. Therefore, it is suitable for data files that are accessed only once or twice each year. S3 Glacier Deep Archive also provides high availability, as it stores data across at least three geographically dispersed Availability Zones1.
* Delete objects when they are no longer needed. The data engineer can specify an expiration action in the S3 Lifecycle policy to delete objects after a certain period of time. This will reduce the storage cost and comply with any data retention policies.
Option C is the only solution that includes all these steps. Therefore, option C is the correct answer.
Option A is incorrect because it transitions objects to S3 One Zone-Infrequent Access (S3 One Zone-IA) after
6 months. S3 One Zone-IA is similar to S3 Standard-IA, but it stores data in a single Availability Zone. This means it has a lower availability and durability than S3 Standard-IA, and it is not resilient to the loss of data in a single Availability Zone1. Therefore, it does not provide high availability as required.
Option B is incorrect because it transfers objects to S3 Glacier Flexible Retrieval after 2 years. S3 Glacier Flexible Retrieval is a storage class that offers secure and durable storage for data that is accessed infrequently and can tolerate a retrieval time of minutes to hours. It is more expensive than S3 Glacier Deep Archive, and it is not suitable for data that is accessed only once or twice each year3. Therefore, it is not the most cost-effective option.
Option D is incorrect because it combines the errors of option A and B. It transitions objects to S3 One Zone- IA after 6 months, which does not provide high availability, and it transfers objects to S3 Glacier Flexible Retrieval after 2 years, which is not the most cost-effective option.
References:
* 1: Amazon S3 storage classes - Amazon Simple Storage Service
* 2: Amazon S3 Standard-Infrequent Access (S3 Standard-IA) - Amazon Simple Storage Service
* 3: Amazon S3 Glacier and S3 Glacier Deep Archive - Amazon Simple Storage Service
* [4]: Expiring objects - Amazon Simple Storage Service
* [5]: Managing your storage lifecycle - Amazon Simple Storage Service
* [6]: Examples of S3 Lifecycle configuration - Amazon Simple Storage Service
* [7]: Amazon S3 Lifecycle further optimizes storage cost savings with new features - What's New with AWS
NEW QUESTION # 213
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 # 214
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