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You will need to pass the AWS Certified Data Engineer - Associate (DEA-C01) (Data-Engineer-Associate) exam to achieve the AWS Certified Data Engineer - Associate (DEA-C01) (Data-Engineer-Associate) certification. Due to extremely high competition, passing the Amazon Data-Engineer-Associate exam is not easy; however, possible. You can use PassReview products to pass the Data-Engineer-Associate Exam on the first attempt. The AWS Certified Data Engineer - Associate (DEA-C01) (Data-Engineer-Associate) practice exam gives you confidence and helps you understand the criteria of the testing authority and pass the Amazon Data-Engineer-Associate exam on the first attempt.
| Section | Weight | Objectives |
|---|---|---|
| Data Security and Governance | 18% | - Apply authentication and authorization
|
| Data Operations and Support | 22% | - Monitor data pipelines
|
| Data Store Management | 26% | - Manage data lifecycle
|
| Data Ingestion and Transformation | 34% | - Apply programming concepts
|
>> Relevant Data-Engineer-Associate Questions <<
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NEW QUESTION # 171
A company loads transaction data for each day into Amazon Redshift tables at the end of each day. The company wants to have the ability to track which tables have been loaded and which tables still need to be loaded.
A data engineer wants to store the load statuses of Redshift tables in an Amazon DynamoDB table. The data engineer creates an AWS Lambda function to publish the details of the load statuses to DynamoDB.
How should the data engineer invoke the Lambda function to write load statuses to the DynamoDB table?
Answer: C
Explanation:
The Amazon Redshift Data API enables you to interact with your Amazon Redshift data warehouse in an easy and secure way. You can use the Data API to run SQL commands, such as loading data into tables, without requiring a persistent connection to the cluster. The Data API also integrates with Amazon EventBridge, which allows you to monitor the execution status of your SQL commands and trigger actions based on events. By using the Data API to publish an event to EventBridge, the data engineer can invoke the Lambda function that writes the load statuses to the DynamoDB table. This solution is scalable, reliable, and cost-effective. The other options are either not possible or not optimal. You cannot use a second Lambda function to invoke the first Lambda function based on CloudWatch or CloudTrail events, as these services do not capture the load status of Redshift tables. You can use the Data API to publish a message to an SQS queue, but this would require additional configuration and polling logic to invoke the Lambda function from the queue. This would also introduce additional latency and cost. Reference:
Using the Amazon Redshift Data API
Using Amazon EventBridge with Amazon Redshift
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 2: Data Store Management, Section 2.2: Amazon Redshift
NEW QUESTION # 172
A transportation company wants to track vehicle movements by capturing geolocation records. The records are 10 bytes in size. The company receives up to 10,000 records every second. Data transmission delays of a few minutes are acceptable because of unreliable network conditions.
The transportation company wants to use Amazon Kinesis Data Streams to ingest the geolocation data. The company needs a reliable mechanism to send data to Kinesis Data Streams. The company needs to maximize the throughput efficiency of the Kinesis shards.
Which solution will meet these requirements in the MOST operationally efficient way?
Answer: C
Explanation:
Problem Analysis:
The company ingests geolocation records (10 bytes each) at 10,000 records per second into Kinesis Data Streams.
Data transmission delays are acceptable, but the solution must maximize throughput efficiency.
Key Considerations:
The Kinesis Producer Library (KPL) batches records and uses aggregation to optimize shard throughput.
Efficiently handles high-throughput scenarios with minimal operational overhead.
Solution Analysis:
Option A: Kinesis Agent
Designed for file-based ingestion; not optimized for geolocation records.
Option B: KPL
Aggregates records into larger payloads, significantly improving shard throughput.
Suitable for applications generating small, high-frequency records.
Option C: Kinesis Firehose
Firehose is for delivery to destinations like S3 or Redshift and is not optimized for direct ingestion to Kinesis Data Streams.
Option D: Kinesis SDK
The SDK lacks advanced features like aggregation, resulting in lower throughput efficiency.
Final Recommendation:
Use Kinesis Producer Library (KPL) for its built-in aggregation and batching capabilities.
Reference:
Kinesis Producer Library (KPL) Overview
NEW QUESTION # 173
A company is developing a product recommendation system that uses Amazon OpenSearch Service. The system needs to perform k-nearest neighbors (k-NN) vector searches on 10 million product embeddings with
768-dimensional vectors. The system must maintain high recall accuracy and support incremental updates without reindexing as new products are added each day. The system must also accommodate complex filtering based on product categories and inventory status.
Which vector index type will meet these requirements?
Answer: B
Explanation:
The correct answer is B because the scenario requires scalable approximate vector search, high recall, incremental updates, and complex filtering. Amazon OpenSearch Service supports k-NN vector search for recommendation use cases, and OpenSearch supports k-NN vector fields for high-dimensional vector search. The Lucene HNSW option is strongest here because OpenSearch documentation specifically states that Lucene supports k-NN searches using HNSW graphs and supports Lucene filters for k-NN searches. That directly matches the need for category and inventory filtering. Exact k-NN with Painless script scoring is accurate but too slow for 10 million vectors. IVF can be efficient but is less ideal for frequent incremental updates and complex filtering. Binary quantization reduces memory but sacrifices accuracy.
NEW QUESTION # 174
A company uses an organization in AWS Organizations to manage multiple AWS accounts. The company uses an enhanced fanout data stream in Amazon Kinesis Data Streams to receive streaming data from multiple producers.
The data stream runs in Account A.
The company wants to use an AWS Lambda function in Account B to process the data from the stream. The company creates a Lambda execution role in Account B that has permissions to access data from the stream in Account A.
What additional step must the company take to meet this requirement?
Answer: A
Explanation:
To allow cross-account access to a Kinesis Data Stream, you must add a resource-based policy to the Kinesis stream in Account A, explicitly granting the Lambda execution role in Account B the required permissions.
* SCPs (A & C) set permissions boundaries, but do not grant access.
* Option D incorrectly refers to the Lambda function - but the Kinesis resource must allow access.
"You must add a resource-based policy to the Kinesis Data Stream in Account A to allow a Lambda function in Account B to consume from the stream." Reference: AWS Documentation - Cross-account Lambda access to Kinesis
NEW QUESTION # 175
A company saves customer data to an Amazon S3 bucket. The company uses server-side encryption with AWS KMS keys (SSE-KMS) to encrypt the bucket. The dataset includes personally identifiable information (PII) such as social security numbers and account details.
Data that is tagged as PII must be masked before the company uses customer data for analysis. Some users must have secure access to the PII data during the preprocessing phase. The company needs a low-maintenance solution to mask and secure the PII data throughout the entire engineering pipeline.
Which combination of solutions will meet these requirements? (Select TWO.)
Answer: D,E
Explanation:
To address the requirement of masking PII data and ensuring secure access throughout the data pipeline, the combination of AWS Glue DataBrew and IAM provides a low-maintenance solution.
A . AWS Glue DataBrew for Masking:
AWS Glue DataBrew provides a visual tool to perform data transformations, including masking PII data. It allows for easy configuration of data transformation tasks without requiring manual coding, making it ideal for this use case.
Reference:
D . AWS Identity and Access Management (IAM):
Using IAM policies allows fine-grained control over access to PII data, ensuring that only authorized users can view or process sensitive data during the pipeline stages.
Alternatives Considered:
B (Amazon GuardDuty): GuardDuty is for threat detection and does not handle data masking or access control for PII.
C (Amazon Macie): Macie can help discover sensitive data but does not handle the masking of PII or access control.
E (Custom scripts): Custom scripting increases the operational burden compared to a built-in solution like DataBrew.
AWS Glue DataBrew for Data Masking
IAM Policies for PII Access Control
NEW QUESTION # 176
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