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| Section | Weight | Objectives |
|---|---|---|
| Data Operations and Support | 22% | - Monitor data pipelines
|
| Data Store Management | 26% | - Choose a data store
|
| Data Ingestion and Transformation | 34% | - Apply programming concepts
|
| Data Security and Governance | 18% | - Ensure data encryption
|
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NEW QUESTION # 11
A company runs an extract, transform, and load (ETL) job in AWS Glue. The job processes personally identifiable information (PII) data and writes logs to an Amazon CloudWatch Logs log group. A data engineer needs to mask PII data in the CloudWatch Logs log group.
Which solution will meet these requirements?
Answer: D
Explanation:
Option B is the right approach because the requirement is to mask PII in the log destination (the CloudWatch Logs log group). The exam guide explicitly calls out security responsibilities that include "data encryption and masking" and also emphasizes enabling and preparing logs for audit and governance needs. A log-group-level masking mechanism is therefore the most direct control point to prevent sensitive values from being exposed to anyone who can view logs.
Option A (Glue security configuration) is primarily used to apply protections such as encryption settings for Glue jobs and related outputs; it does not inherently solve the problem of masking PII that has already been emitted into application logs. Option C (Macie) is intended to discover and classify sensitive data-most commonly in Amazon S3-and to produce findings; it is not a log-masking control for CloudWatch Logs.
The material reinforces Macie as a discovery/classification service for PII rather than a masking mechanism.
Option D adds custom code paths in the ETL job and still may miss PII that appears in framework/system logs; it also violates the "least operational effort" spirit compared to applying a centralized log policy.
Therefore, attaching a data protection policy directly to the CloudWatch Logs log group best meets the masking requirement.
NEW QUESTION # 12
A company needs to load customer data that comes from a third party into an Amazon Redshift data warehouse. The company stores order data and product data in the same data warehouse. The company wants to use the combined dataset to identify potential new customers.
A data engineer notices that one of the fields in the source data includes values that are in JSON format.
How should the data engineer load the JSON data into the data warehouse with the LEAST effort?
Answer: D
Explanation:
In Amazon Redshift, the SUPER data type is designed specifically to handle semi-structured data like JSON, Parquet, ORC, and others. By using the SUPER data type, Redshift can ingest and query JSON data without requiring complex data flattening processes, thus reducing the amount of preprocessing required before loading the data. The SUPER data type also works seamlessly with Redshift Spectrum, enabling complex queries that can combine both structured and semi-structured datasets, which aligns with the company's need to use combined datasets to identify potential new customers.
Using the SUPER data type also allows for automatic parsing and query processing of nested data structures through Amazon Redshift's PARTITION BY and JSONPATH expressions, which makes this option the most efficient approach with the least effort involved. This reduces the overhead associated with using tools like AWS Glue or Lambda for data transformation.
Reference:
Amazon Redshift Documentation - SUPER Data Type
AWS Certified Data Engineer - Associate Training: Building Batch Data Analytics Solutions on AWS AWS Certified Data Engineer - Associate Study Guide By directly leveraging the capabilities of Redshift with the SUPER data type, the data engineer ensures streamlined JSON ingestion with minimal effort while maintaining query efficiency.
NEW QUESTION # 13
A company uses Amazon Redshift to store order transactions from the current day. The company has an orders table that contains the previous order data. The company also has a staging table that contains new or updated order records. The company needs to remove stale records from the orders table and insert the most recent data in the orders table from the staging table. Several downstream applications need the orders table to display up-to-date information.
Which solution will meet these requirements?
Answer: C
Explanation:
Option D is correct because Amazon Redshift stored procedures are designed to encapsulate a sequence of SQL statements and business logic inside the database. AWS documentation states that stored procedures are commonly used for data transformation, data validation, and business-specific logic, and that they can combine multiple SQL steps into one procedure. AWS also documents the standard Redshift pattern for deleting stale rows and inserting fresh rows from a staging table, which is exactly the requirement here.
Keeping the operation inside Redshift is the most direct way to maintain an up-to-date orders table for downstream consumers.
Option A is incorrect because Redshift Spectrum is for querying external data in S3, not for performing this in- place Redshift table-maintenance pattern. Option B adds unnecessary unload and reload steps, creating delay and operational complexity. Option C is also unsuitable because Athena federated queries are not the right mechanism for transactional maintenance of Redshift tables. The correct DEA-C01-style answer is to use Redshift-native procedural SQL to delete stale rows and insert current rows from staging.
NEW QUESTION # 14
A data engineer wants to orchestrate a set of extract, transform, and load (ETL) jobs that run on AWS. The ETL jobs contain tasks that must run Apache Spark jobs on Amazon EMR, make API calls to Salesforce, and load data into Amazon Redshift.
The ETL jobs need to handle failures and retries automatically. The data engineer needs to use Python to orchestrate the jobs.
Which service will meet these requirements?
Answer: B
Explanation:
The data engineer needs to orchestrate ETL jobs that include Spark jobs on Amazon EMR, API calls to Salesforce, and loading data into Redshift. They also need automatic failure handling and retries. Amazon Managed Workflows for Apache Airflow (Amazon MWAA) is the best solution for this requirement.
* Option A: Amazon Managed Workflows for Apache Airflow (Amazon MWAA)Apache Airflow is designed for complex job orchestration, allowing users to define workflows (DAGs) in Python. MWAA manages Airflow and its integrations with other AWS services, including Amazon EMR, Redshift, and external APIs like Salesforce. It provides automatic retry handling, failure detection, and detailed monitoring, which fits the use case perfectly.
* Option B (AWS Step Functions) can orchestrate tasks but doesn't natively support complex workflow definitions with Python like Airflow does.
* Option C (AWS Glue) is more focused on ETL and doesn't handle the orchestration of external systems like Salesforce as well as Airflow.
* Option D (Amazon EventBridge) is more suited for event-driven architectures rather than complex workflow orchestration.
References:
* Amazon Managed Workflows for Apache Airflow
* Apache Airflow on AWS
NEW QUESTION # 15
A company needs to store semi-structured transactional data for an application in a database. The database must be serverless. The application writes the data infrequently, but it reads the data frequently. The application must retrieve the data within milliseconds.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: C
Explanation:
Option D is correct because Amazon DynamoDB is a serverless NoSQL database that is well suited for semi- structured data, and DynamoDB Accelerator (DAX) is designed to improve read performance for read-heavy applications. AWS documentation states that DAX can improve DynamoDB performance from milliseconds to microseconds, and the DynamoDB guide says DAX is most effective for applications with a high read-to- write ratio, which exactly matches this workload of infrequent writes and frequent reads.
Option A and B are incorrect because Amazon S3 and Iceberg are object storage and table-format solutions, not low-latency transactional databases for millisecond retrieval. Option C is not serverless and requires managing a database cluster, even if optimized reads improve performance. By contrast, DynamoDB is fully managed and serverless, and DAX adds the lowest-latency read path for hot data. The uploaded study guide also identifies DynamoDB as the right fit for dynamic or semi-structured data that requires low-latency access at scale.
NEW QUESTION # 16
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