Valid Data-Engineer-Associate Test Notes & Valid Data-Engineer-Associate Exam Question

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Amazon Data-Engineer-Associate Exam Syllabus Topics:

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

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q202-Q207):

NEW QUESTION # 202
A media company uses software as a service (SaaS) applications to gather data by using third-party tools. The company needs to store the data in an Amazon S3 bucket. The company will use Amazon Redshift to perform analytics based on the data.
Which AWS service or feature will meet these requirements with the LEAST operational overhead?

Answer: D

Explanation:
Amazon AppFlow is a fully managed integration service that enables you to securely transfer data between SaaS applications and AWS services like Amazon S3 and AmazonRedshift. Amazon AppFlow supports many SaaS applications as data sources and targets, and allows you to configure data flows with a few clicks.
Amazon AppFlow also provides features such as data transformation, filtering, validation, and encryption to prepare and protect your data. Amazon AppFlow meets the requirements of the media company with the least operational overhead, as it eliminates the need to write code, manage infrastructure, or monitor data pipelines.
References:
Amazon AppFlow
Amazon AppFlow | SaaS Integrations List
Get started with data integration from Amazon S3 to Amazon Redshift using AWS Glue interactive sessions


NEW QUESTION # 203
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.
Reference:
Amazon Managed Workflows for Apache Airflow
Apache Airflow on AWS


NEW QUESTION # 204
A company is migrating a legacy application to an Amazon S3 based data lake. A data engineer reviewed data that is associated with the legacy application. The data engineer found that the legacy data contained some duplicate information.
The data engineer must identify and remove duplicate information from the legacy application data.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: C

Explanation:
AWS Glue is a fully managed serverless ETL service that can handle data deduplication with minimal operational overhead. AWS Glue provides a built-in ML transform called FindMatches, which can automatically identify and group similar records in a dataset. FindMatches can also generate a primary key for each group of records and remove duplicates. FindMatches does not require any coding or prior ML experience, as it can learn from a sample of labeled data provided by the user. FindMatches can also scale to handle large datasets and optimize the cost and performance of the ETL job. References:
AWS Glue
FindMatches ML Transform
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide


NEW QUESTION # 205
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. References:
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 # 206
A data engineer is configuring Amazon SageMaker Studio to use AWS Glue interactive sessions to prepare data for machine learning (ML) models.
The data engineer receives an access denied error when the data engineer tries to prepare the data by using SageMaker Studio.
Which change should the engineer make to gain access to SageMaker Studio?

Answer: B

Explanation:
This solution meets the requirement of gaining access to SageMaker Studio to use AWS Glue interactive sessions. AWS Glue interactive sessions are a way to use AWS Glue DataBrew and AWS Glue Data Catalog from within SageMaker Studio. To use AWS Glue interactive sessions, the data engineer's IAM user needs to have permissions to assume the AWS Glue service role and the SageMaker execution role. By adding a policy to the data engineer's IAM user that includes the sts:AssumeRole action for the AWS Glue and SageMaker service principals in the trust policy, the data engineer can grant these permissions and avoid the access denied error. The other options are not sufficient or necessary to resolve the error. References:
Get started with data integration from Amazon S3 to Amazon Redshift using AWS Glue interactive sessions Troubleshoot Errors - Amazon SageMaker AccessDeniedException on sagemaker:CreateDomain in AWS SageMaker Studio, despite having SageMakerFullAccess


NEW QUESTION # 207
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