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| Section | Weight | Objectives |
|---|---|---|
| Data Security and Governance | 18% | - Apply governance and compliance best practices - Implement data security controls |
| Data Operations and Support | 22% | - Troubleshoot data workflow issues - Monitor and maintain data pipelines |
| Data Store Management | 26% | - Optimize storage performance and cost - Select appropriate data storage solutions |
| Data Ingestion and Transformation | 34% | - Build and manage data pipelines - Ingest and transform data using AWS services |
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NEW QUESTION # 283
A company uses Amazon Redshift as its data warehouse. Data encoding is applied to the existing tables of the data warehouse. A data engineer discovers that the compression encoding applied to some of the tables is not the best fit for the data.
The data engineer needs to improve the data encoding for the tables that have sub-optimal encoding.
Which solution will meet this requirement?
Answer: C
Explanation:
To improve data encoding for Amazon Redshift tables where sub-optimal encoding has been applied, the correct approach is to analyze the table to determine the optimal encoding based on the data distribution and characteristics.
Option B: Run the ANALYZE COMPRESSION command against the identified tables. Manually update the compression encoding of columns based on the output of the command.The ANALYZE COMPRESSION command in Amazon Redshift analyzes the columnar data and suggests the best compression encoding for each column. The output provides recommendations for changing the current encoding to improve storage efficiency and query performance. After analyzing, you can manually apply the recommended encoding to the columns.
Option A (ANALYZE command) is incorrect because it is primarily used to update statistics on tables, not to analyze or suggest compression encoding.
Options C and D (VACUUM commands) deal with reclaiming disk space and reorganizing data, not optimizing compression encoding.
References:
Amazon Redshift ANALYZE COMPRESSION Command
NEW QUESTION # 284
A company receives call logs as Amazon S3 objects that contain sensitive customer information. The company must protect the S3 objects by using encryption. The company must also use encryption keys that only specific employees can access.
Which solution will meet these requirements with the LEAST effort?
Answer: C
Explanation:
Option C is the best solution to meet the requirements with the least effort because server-side encryption with AWS KMS keys (SSE-KMS) is a feature that allows you toencrypt data at rest in Amazon S3 using keys managed by AWS Key Management Service (AWS KMS). AWS KMS is a fully managed service that enables you to create and manage encryption keys for your AWS services and applications. AWS KMS also allows you to define granular access policies for your keys, such as who can use them to encrypt and decrypt data, and under what conditions. By using SSE-KMS, you can protect your S3 objects by using encryption keys that only specific employees can access, without having to manage the encryption and decryption process yourself.
Option A is not a good solution because it involves using AWS CloudHSM, which is a service that provides hardware security modules (HSMs) in the AWS Cloud. AWS CloudHSM allows you to generate and use your own encryption keys on dedicated hardware that is compliant with various standards and regulations.
However, AWS CloudHSM is not a fully managed service and requires more effort to set up and maintain than AWS KMS. Moreover, AWS CloudHSM does not integrate with Amazon S3, so you have to configure the process that writes to S3 to make calls to CloudHSM to encrypt and decrypt the objects, which adds complexity and latency to the data protection process.
Option B is not a good solution because it involves using server-side encryption with customer-provided keys (SSE-C), which is a feature that allows you to encrypt data at rest in Amazon S3 using keys that you provide and manage yourself. SSE-C requires you to send your encryption key along with each request to upload or retrieve an object. However, SSE-C does not provide any mechanism to restrict access to the keys that encrypt the objects, so you have to implement your own key management and access control system, which adds more effort and risk to the data protection process.
Option D is not a good solution because it involves using server-side encryption with Amazon S3 managed keys (SSE-S3), which is a feature that allows you to encrypt data at rest in Amazon S3 using keys that are managed by Amazon S3. SSE-S3 automatically encrypts and decrypts your objects as they are uploaded and downloaded from S3. However, SSE-S3 does not allow you to control who can access the encryption keys or under what conditions. SSE-S3 uses a single encryption key for each S3 bucket, which is shared by all users who have access to the bucket. This means that you cannot restrict access to the keys that encrypt the objects by specific employees, which does not meet the requirements.
:
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
Protecting Data Using Server-Side Encryption with AWS KMS-Managed Encryption Keys (SSE-KMS) - Amazon Simple Storage Service What is AWS Key Management Service? - AWS Key Management Service What is AWS CloudHSM? - AWS CloudHSM Protecting Data Using Server-Side Encryption with Customer-Provided Encryption Keys (SSE-C) - Amazon Simple Storage Service Protecting Data Using Server-Side Encryption with Amazon S3-Managed Encryption Keys (SSE-S3) - Amazon Simple Storage Service
NEW QUESTION # 285
A data engineer has two datasets that contain sales information for multiple cities and states. One dataset is named reference, and the other dataset is named primary.
The data engineer needs a solution to determine whether a specific set of values in the city and state columns of the primary dataset exactly match the same specific values in the reference dataset. The data engineer wants to useData Quality Definition Language (DQDL)rules in an AWS Glue Data Quality job.
Which rule will meet these requirements?
Answer: A
Explanation:
TheDatasetMatchrule in DQDL checks for full value equivalence between mapped fields. A value of1.0 indicates a100% match. The correct syntax and metric for an exact match scenario are:
"Use DatasetMatch when comparing mapped fields between two datasets. The comparison score of 1.0 confirms a perfect match."
-Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf Options with "100" use incorrect syntax since DQDL usesfloating-point scores(e.g., 1.0, 0.95), not percentages.
NEW QUESTION # 286
A company uses Amazon Redshift as its data warehouse. Data encoding is applied to the existing tables of the data warehouse. A data engineer discovers that the compression encoding applied to some of the tables is not the best fit for the data.
The data engineer needs to improve the data encoding for the tables that have sub-optimal encoding.
Which solution will meet this requirement?
Answer: C
Explanation:
To improve data encoding for Amazon Redshift tables where sub-optimal encoding has been applied, the correct approach is to analyze the table to determine the optimal encoding based on the data distribution and characteristics.
Option B: Run the ANALYZE COMPRESSION command against the identified tables. Manually update the compression encoding of columns based on the output of the command.
The ANALYZE COMPRESSION command in Amazon Redshift analyzes the columnar data and suggests the best compression encoding for each column. The output provides recommendations for changing the current encoding to improve storage efficiency and query performance. After analyzing, you can manually apply the recommended encoding to the columns.
Option A (ANALYZE command) is incorrect because it is primarily used to update statistics on tables, not to analyze or suggest compression encoding.
Options C and D (VACUUM commands) deal with reclaiming disk space and reorganizing data, not optimizing compression encoding.
Reference:
Amazon Redshift ANALYZE COMPRESSION Command
NEW QUESTION # 287
A company is designing a serverless data processing workflow in AWS Step Functions that involves multiple steps. The processing workflow ingests data from an external API, transforms the data by using multiple AWS Lambda functions, and loads the transformed data into Amazon DynamoDB.
The company needs the workflow to perform specific steps based on the content of the incoming data.
Which Step Functions state type should the company use to meet this requirement?
Answer: D
Explanation:
TheChoicestate type in AWS Step Functions is designed to perform branching logic, i.e., routing execution to different paths based on conditions in the input data.
"The Step FunctionsChoicestate lets you branch the execution flow depending on values in the state's input.
This allows you to run different processing logic based on dynamic conditions like values in the input JSON."
-Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf This makesChoicethe correct answer for content-driven conditional workflows.
NEW QUESTION # 288
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