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| Section | Weight | Objectives |
|---|---|---|
| Data Store Management | 26% | - Choose a data store
|
| Data Operations and Support | 22% | - Automate data pipelines
|
| Data Security and Governance | 18% | - Implement data quality checks
|
| Data Ingestion and Transformation | 34% | - Perform data ingestion
|
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NEW QUESTION # 98
A company has an Amazon S3-based data lake. The data lake contains datasets that belong to multiple departments. The data lake ingests millions of customer records each day.
A data engineer needs to design an access and storage solution that allows departments to access only the subset of the company's dataset that each department requires. The solution must follow the principle of least privilege.
Which solution will meet these requirements with the LEAST operational effort?
Answer: B
Explanation:
Comprehensive and Detailed Explanation (150-250 words)
AWS Lake Formation is specifically designed to simplify fine-grained access control for data lakes stored in Amazon S3. By using Lake Formation tag-based access control (LF-TBAC), administrators can define access policies once and apply them dynamically based on tags rather than managing individual IAM policies.
LF-Tags can be assigned to databases, tables, and columns in the AWS Glue Data Catalog. Departments are granted permissions based on tags, ensuring that each department can access only the data it is authorized to view. This approach scales efficiently as datasets and departments grow, which is critical for data lakes ingesting millions of records daily.
Managing IAM policies per department is operationally complex and error-prone. Redshift-based access centralization limits flexibility and introduces unnecessary infrastructure. Syncing data into an RDS database adds cost, latency, and maintenance overhead.
Lake Formation provides centralized governance, auditing, and least-privilege enforcement with minimal administrative effort, making it the optimal solution.
NEW QUESTION # 99
A company stores customer data in an Amazon S3 bucket. Multiple teams in the company want to use the customer data for downstream analysis. The company needs to ensure that the teams do not have access to personally identifiable information (PII) about the customers.
Which solution will meet this requirement with LEAST operational overhead?
Answer: B
Explanation:
Step 1: Understanding the Data Use Case
The company has data stored in an Amazon S3 bucket and needs to provide teams access for analysis, ensuring that PII data is not included in the analysis. The solution should be simple to implement and maintain, ensuring minimal operational overhead.
Step 2: Why Option D is Correct
Option D (AWS Glue DataBrew) allows you to visually prepare and transform data without needing to write code. By using a DataBrew job, the company can:
Automatically detect and separate PII data from non-PII data.
Store PII data in a second S3 bucket for security, while keeping the original S3 bucket clean for analysis.
This approach keeps operational overhead low by utilizing DataBrew's pre-built transformations and the easy-to-use interface for non-technical users. It also ensures compliance by separating sensitive PII data from the main dataset.
Step 3: Why Other Options Are Not Ideal
Option A (Amazon Macie) is a powerful tool for detecting sensitive data, but Macie doesn't inherently remove or mask PII. You would still need additional steps to clean the data after Macie identifies PII.
Option B (S3 Object Lambda with Amazon Comprehend) introduces more complexity by requiring custom logic at the point of data access. Amazon Comprehend can detect PII, but using S3 Object Lambda to filter data would involve more overhead.
Option C (Kinesis Data Firehose and Comprehend) is more suitable for real-time streaming data use cases rather than batch analysis. Setting up and managing a streaming solution like Kinesis adds unnecessary complexity.
Conclusion:
Using AWS Glue DataBrew provides a low-overhead, no-code solution to detect and separate PII data, ensuring the analysis teams only have access to non-sensitive data. This approach is simple, compliant, and easy to manage compared to other options.
NEW QUESTION # 100
A banking company uses an application to collect large volumes of transactional dat a. The company uses Amazon Kinesis Data Streams for real-time analytics. The company's application uses the PutRecord action to send data to Kinesis Data Streams.
A data engineer has observed network outages during certain times of day. The data engineer wants to configure exactly-once delivery for the entire processing pipeline.
Which solution will meet this requirement?
Answer: D
Explanation:
For exactly-once delivery and processing in Amazon Kinesis Data Streams, the best approach is to design the application so that it handles idempotency. By embedding a unique ID in each record, the application can identify and remove duplicate records during processing.
Exactly-Once Processing:
Kinesis Data Streams does not natively support exactly-once processing. Therefore, idempotency should be designed into the application, ensuring that each record has a unique identifier so that the same event is processed only once, even if it is ingested multiple times.
This pattern is widely used for achieving exactly-once semantics in distributed systems.
Reference:
Alternatives Considered:
B (Checkpoint configuration): While updating the checkpoint configuration can help with some aspects of duplicate processing, it is not a full solution for exactly-once delivery.
C (Design data source): Ensuring events are not ingested multiple times is ideal, but network outages can make this difficult, and it doesn't guarantee exactly-once delivery.
D (Using EMR): While using EMR with Flink or Spark could work, it introduces unnecessary complexity compared to handling idempotency at the application level.
Amazon Kinesis Best Practices for Exactly-Once Processing
Achieving Idempotency with Amazon Kinesis
NEW QUESTION # 101
A data engineer has a one-time task to read data from objects that are in Apache Parquet format in an Amazon S3 bucket. The data engineer needs to query only one column of the data.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: A
Explanation:
Option B is the best solution to meet the requirements with the least operational overhead because S3 Select is a feature that allows you to retrieve only a subset of data from an S3 object by using simple SQL expressions.
S3 Select works on objects stored in CSV, JSON, or Parquet format. By using S3 Select, you can avoid the need to download and process the entire S3 object, which reduces the amount of data transferred and the computation time. S3 Select is also easy to use and does not require any additional services or resources.
Option A is not a good solution because it involves writing custom code and configuring an AWS Lambda function to load data from the S3 bucket into a pandas dataframe and query the required column. This option adds complexity and latency to the data retrieval process and requires additional resources and configuration.
Moreover, AWS Lambda has limitations on the execution time, memory, and concurrency, which may affect the performance and reliability of the data retrieval process.
Option C is not a good solution because it involves creating and running an AWS Glue DataBrew project to consume the S3 objects and query the required column. AWS Glue DataBrew is a visual data preparation tool that allows you to clean, normalize, and transform data without writing code. However, in this scenario, the data is already in Parquet format, which is a columnar storage format that is optimized for analytics.
Therefore, there is no need to use AWS Glue DataBrew to prepare the data. Moreover, AWS Glue DataBrew adds extra time and cost to the data retrieval process and requires additional resources and configuration.
Option D is not a good solution because it involves running an AWS Glue crawler on the S3 objects and using a SQL SELECT statement in Amazon Athena to query the required column. An AWS Glue crawler is a service that can scan data sources and create metadata tables in the AWS Glue Data Catalog. The Data Catalog is a central repository that stores information about the data sources, such as schema, format, and location. Amazon Athena is a serverless interactive query service that allows you to analyze data in S3 using standard SQL. However, in this scenario, the schema and format of the data are already known and fixed, so there is no need to run a crawler to discover them. Moreover, running a crawler and using Amazon Athena adds extra time and cost to the data retrieval process and requires additional services and configuration.
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
S3 Select and Glacier Select - Amazon Simple Storage Service
AWS Lambda - FAQs
What Is AWS Glue DataBrew? - AWS Glue DataBrew
Populating the AWS Glue Data Catalog - AWS Glue
What is Amazon Athena? - Amazon Athena
NEW QUESTION # 102
A company uses an Amazon Redshift cluster that runs on RA3 nodes. The company wants to scale read and write capacity to meet demand. A data engineer needs to identify a solution that will turn on concurrency scaling.
Which solution will meet this requirement?
Answer: C
Explanation:
Concurrency scaling is a feature that allows you to support thousands of concurrent users and queries, with consistently fast query performance. When you turn on concurrency scaling, Amazon Redshift automatically adds query processing power in seconds to process queries without any delays. You can manage which queries are sent to the concurrency-scaling cluster by configuring WLM queues. To turn on concurrency scaling for a queue, set the Concurrency Scaling mode value to auto. The other options are either incorrect or irrelevant, as they do not enable concurrency scaling for the existing Redshift cluster on RA3 nodes. Reference:
Working with concurrency scaling - Amazon Redshift
Amazon Redshift Concurrency Scaling - Amazon Web Services
Configuring concurrency scaling queues - Amazon Redshift
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 6, page 163)
NEW QUESTION # 103
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