Data-Engineer-Associate Valid Test Discount - Data-Engineer-Associate Certified Questions

What's more, part of that Itcertking Data-Engineer-Associate dumps now are free: https://drive.google.com/open?id=1-HnBq4hp8OjZ_FgCV5lhvF8bQ-C1S4Jq

As everybody knows, the most crucial matter is the quality of AWS Certified Data Engineer - Associate (DEA-C01) study question for learners. We have been doing this professional thing for many years. Let the professionals handle professional issues. So as for us, we have enough confidence to provide you with the best Data-Engineer-Associate Exam Questions for your study to pass it. Only with strict study, we write the latest and the specialized study materials. We can say that our Data-Engineer-Associate exam questions are the most suitable for examinee to pass the exam.

Amazon Data-Engineer-Associate Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Security and Governance18%- Manage data privacy and compliance
  • 1. PII data handling
  • 2. Data masking and tokenization
  • 3. AWS Lake Formation permissions
- Ensure data encryption
  • 1. AWS KMS
  • 2. Encryption at rest and in transit
- Apply authentication and authorization
  • 1. Service control policies (SCPs)
  • 2. Amazon S3 bucket policies
  • 3. AWS IAM policies and roles
- Implement data quality checks
  • 1. AWS Glue DataBrew
  • 2. Data validation
Topic 2: Data Operations and Support22%- Manage and troubleshoot data processes
  • 1. Debugging failed jobs
  • 2. Performance tuning
  • 3. Cost optimization
- Automate data pipelines
  • 1. Scheduling jobs
  • 2. Event-driven triggers
  • 3. AWS Lambda triggers
- Monitor data pipelines
  • 1. AWS CloudTrail
  • 2. Logging and metrics
  • 3. Amazon CloudWatch
Topic 3: Data Ingestion and Transformation34%- Transform and process data
  • 1. Data transformation services (AWS Glue, Amazon EMR, AWS Lambda)
  • 2. ETL/ELT patterns
  • 3. Batch and stream processing
  • 4. Data partitioning and compression
- Orchestrate data pipelines
  • 1. AWS Step Functions
  • 2. Amazon Managed Workflows for Apache Airflow (MWAA)
  • 3. AWS Glue Workflows
  • 4. Event-driven architectures
- Perform data ingestion
  • 1. Throughput and latency characteristics for AWS services
  • 2. Batch data ingestion (scheduled ingestion, event-driven ingestion)
  • 3. Data ingestion patterns (frequency and data history)
  • 4. Replayability of data
  • 5. Streaming data ingestion
- Apply programming concepts
  • 1. Infrastructure as Code (IaC)
  • 2. SQL, Python, Scala
  • 3. Version control
Topic 4: Data Store Management26%- Manage data lifecycle
  • 1. Data retention policies
  • 2. Data archiving
  • 3. Amazon S3 storage classes
- Design data models
  • 1. Partitioning and indexing strategies
  • 2. Schema design
  • 3. Normalization and denormalization
- Understand data cataloging
  • 1. Data discovery and classification
  • 2. Schema evolution
  • 3. AWS Glue Data Catalog
- Choose a data store
  • 1. Data lakes vs. data warehouses
  • 2. Data characteristics (structured, semi-structured, unstructured)
  • 3. Amazon S3, Amazon RDS, Amazon DynamoDB, Amazon Redshift
  • 4. Access and storage patterns

>> Data-Engineer-Associate Valid Test Discount <<

Desktop Practice Amazon Data-Engineer-Associate Exam Software No Internet Required

If you plan to apply for the AWS Certified Data Engineer - Associate (DEA-C01) (Data-Engineer-Associate) certification exam, you need the best Data-Engineer-Associate practice test material that can help you maximize your chances of success. You cannot rely on invalid Data-Engineer-Associate Materials and then expect the results to be great. So, you must prepare from the updated Amazon Data-Engineer-Associate Exam Dumps to crack the Data-Engineer-Associate exam.

Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q141-Q146):

NEW QUESTION # 141
A company has three subsidiaries. Each subsidiary uses a different data warehousing solution. The first subsidiary hosts its data warehouse in Amazon Redshift. The second subsidiary uses Teradata Vantage on AWS. The third subsidiary uses Google BigQuery.
The company wants to aggregate all the data into a central Amazon S3 data lake. The company wants to use Apache Iceberg as the table format.
A data engineer needs to build a new pipeline to connect to all the data sources, run transformations by using each source engine, join the data, and write the data to Iceberg.
Which solution will meet these requirements with the LEAST operational effort?

Answer: D

Explanation:
Amazon Athena provides federated query connectors that allow querying multiple data sources, such as Amazon Redshift, Teradata, and Google BigQuery, without needing to extract the data from the original source. This solution is optimal because it offers theleast operational effortby avoiding complex data movement and transformation processes.
* Amazon Athena Federated Queries:
* Athena's federated queries allow direct querying of data stored across multiple sources, including Amazon Redshift, Teradata, and BigQuery. With Athena's support for Apache Iceberg, the company can easily run a Merge operation on the Iceberg table.
* The solution reduces complexity by centralizing the query execution and transformation process in Athena using SQL queries.
Reference:Amazon Athena Federated Query
Alternatives Considered:
A (AWS Glue pipeline): This would work but requires more operational effort to manage and transform the data in AWS Glue.
C (Amazon EMR): Using EMR and writing PySpark code introduces more operational overhead and complexity compared to a SQL-based solution in Athena.
D (Amazon AppFlow): AppFlow is more suitable for transferring data between services but is not as efficient for transformations and joins as Athena federated queries.
References:
Amazon Athena Documentation
Federated Queries in Amazon Athena


NEW QUESTION # 142
A retail company is using an Amazon Redshift cluster to support real-time inventory management. The company has deployed an ML model on a real-time endpoint in Amazon SageMaker.
The company wants to make real-time inventory recommendations. The company also wants to make predictions about future inventory needs.
Which solutions will meet these requirements? (Select TWO.)

Answer: A,B

Explanation:
The company needs to use machine learning models for real-time inventory recommendations and future inventory predictions while leveraging both Amazon Redshift and Amazon SageMaker.
Option A: Use Amazon Redshift ML to generate inventory recommendations.
Amazon Redshift ML allows you to build, train, and deploy machine learning models directly from Redshift using SQL statements. It integrates with SageMaker to train models and run inference. This feature is useful for generating inventory recommendations directly from the data stored in Redshift.
Option B: Use SQL to invoke a remote SageMaker endpoint for prediction.
You can use SQL in Redshift to call a SageMaker endpoint for real-time inference. By invoking a SageMaker endpoint from within Redshift, the company can get real-time predictions on inventory, allowing for integration between the data warehouse and the machine learning model hosted in SageMaker.
Option C (offline model training) and Option D (creating dashboards with SageMaker Autopilot) are not relevant to the real-time prediction and recommendation requirements.
Option E (archiving inventory reports in Redshift) is not related to making predictions or recommendations.
Reference:
Amazon Redshift ML Documentation
Invoking SageMaker Endpoints from SQL


NEW QUESTION # 143
A data engineer is using an Apache Iceberg framework to build a data lake that contains 100 TB of data. The data engineer wants to run AWS Glue Apache Spark Jobs that use the Iceberg framework.
What combination of steps will meet these requirements? (Select TWO.)

Answer: C,D


NEW QUESTION # 144
A company stores server logs in an Amazon 53 bucket. The company needs to keep the logs for 1 year. The logs are not required after 1 year.
A data engineer needs a solution to automatically delete logs that are older than 1 year.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: B

Explanation:
* Problem Analysis:
* The company usesAWS Gluefor ETL pipelines and requires automaticdata quality checks during pipeline execution.
* The solution must integrate with existing AWS Glue pipelines and evaluatedata quality rules based on predefined thresholds.
* Key Considerations:
* Ensure minimal implementation effort by leveraging built-in AWS Glue features.
* Use a standardized approach for defining and evaluating data quality rules.
* Avoid custom libraries or external frameworks unless absolutely necessary.
* Solution Analysis:
* Option A: SQL Transform
* Adding SQL transforms to define and evaluate data quality rules is possible but requires writing complex queries for each rule.
* Increases operational overhead and deviates from Glue's declarative approach.
* Option B: Evaluate Data Quality Transform with DQDL
* AWS Glue provides a built-inEvaluate Data Quality transform.
* Allows defining rules inData Quality Definition Language (DQDL), a concise and declarative way to define quality checks.
* Fully integrated with Glue Studio, making it the least effort solution.
* Option C: Custom Transform with PyDeequ
* PyDeequ is a powerful library for data quality checks but requires custom code and integration.
* Increases implementation effort compared to Glue's native capabilities.
* Option D: Custom Transform with Great Expectations
* Great Expectations is another powerful library for data quality but adds complexity and external dependencies.
* Final Recommendation:
* UseEvaluate Data Quality transformin AWS Glue.
* Define rules inDQDLfor checking thresholds, null values, or other quality criteria.
* This approach minimizes development effort and ensures seamless integration with AWS Glue.
:
AWS Glue Data Quality Overview
DQDL Syntax and Examples
Glue Studio Transformations


NEW QUESTION # 145
A data engineer is creating a product recommendation system that requires vector search across 5 million product embeddings with 768 dimensions. The system must prioritize search accuracy and maintain search latency under 100 milliseconds. The data engineer wants to implement a k-nearest neighbors (k-NN) vector index in Amazon OpenSearch Service.
Which vector index type should the data engineer use?

Answer: C

Explanation:
The correct answer is B because HNSW with the Faiss engine is the best fit when the system needs approximate k-NN search over millions of high-dimensional vectors while maintaining low latency and strong recall. OpenSearch approximate k-NN is designed for large, high-dimensional datasets where brute-force exact search becomes inefficient, and OpenSearch documentation shows HNSW configured with the Faiss engine for knn_vector fields. Exact search with a scoring script is accurate but does not scale well for 5 million vectors under a strict latency requirement. IVF can scale but usually requires training and tuning and may trade off recall. Binary quantization reduces memory but can reduce accuracy. Therefore, HNSW with Faiss is the strongest exam answer.


NEW QUESTION # 146
......

The Data-Engineer-Associate certification verifies that you are a skilled professional. Itcertking product is designed by keeping all the rules and regulations in focus that Amazon publishes. Our main goal is that you can memorize the actual Amazon Data-Engineer-Associate exam question to complete the AWS Certified Data Engineer - Associate (DEA-C01) (Data-Engineer-Associate) test in time with extraordinary grades. Amazon Data-Engineer-Associate Exam Dumps includes Amazon Data-Engineer-Associate dumps PDF format, desktop Data-Engineer-Associate practice exam software, and web-based Data-Engineer-Associate practice test software.

Data-Engineer-Associate Certified Questions: https://www.itcertking.com/Data-Engineer-Associate_exam.html

P.S. Free & New Data-Engineer-Associate dumps are available on Google Drive shared by Itcertking: https://drive.google.com/open?id=1-HnBq4hp8OjZ_FgCV5lhvF8bQ-C1S4Jq