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Amazon Data-Engineer-Associate Exam Syllabus Topics:

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

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q261-Q266):

NEW QUESTION # 261
A company has a data lake in Amazon S3. The company collects AWS CloudTrail logs for multiple applications. The company stores the logs in the data lake, catalogs the logs in AWS Glue, and partitions the logs based on the year. The company uses Amazon Athena to analyze the logs.
Recently, customers reported that a query on one of the Athena tables did not return any dat a. A data engineer must resolve the issue.
Which combination of troubleshooting steps should the data engineer take? (Select TWO.)

Answer: C,D

Explanation:
The problem likely arises from Athena not being able to read from the correct S3 location or missing partitions. The two most relevant troubleshooting steps involve checking the S3 location and repairing the table metadata.
A . Confirm that Athena is pointing to the correct Amazon S3 location:
One of the most common issues with missing data in Athena queries is that the query is pointed to an incorrect or outdated S3 location. Checking the S3 path ensures Athena is querying the correct data.
Reference:
C . Use the MSCK REPAIR TABLE command:
When new partitions are added to the S3 bucket without being reflected in the Glue Data Catalog, Athena queries will not return data from those partitions. The MSCK REPAIR TABLE command updates the Glue Data Catalog with the latest partitions.
Alternatives Considered:
B (Increase query timeout): Timeout issues are unrelated to missing data.
D (Restart Athena): Athena does not require restarting.
E (Delete and recreate table): This introduces unnecessary overhead when the issue can be resolved by repairing the table and confirming the S3 location.
Athena Query Fails to Return Data


NEW QUESTION # 262
A company is building data processing pipelines by using AWS Glue. The pipelines access data stored in Amazon S3. The company has organized the data into folders with prefixes that represent different classification levels. The company needs to restrict AWS Glue jobs to access only specific prefixes based on the data classification. The company must also restrict access to business hours (9 AM to 5 PM).
Which elements must the company include in a custom IAM policy to meet these requirements?

Answer: B

Explanation:
Option B is correct because access to specific S3 folders for AWS Glue jobs should be restricted by scoping permissions to the correct S3 object ARNs in the IAM policy Resource element, such as bucket paths with wildcard patterns for the allowed prefixes. Time-based access is then enforced by adding a Condition that uses the global IAM condition key aws:CurrentTime with date operators such as DateGreaterThan and DateLessThan. AWS IAM documentation explicitly states that date condition operators are used with aws:
CurrentTime or aws:EpochTime in the Condition element.
Option C is not the best answer because s3:prefix is generally used with bucket listing operations rather than as the main mechanism to control object-level access for Glue reads and writes. For object access, the cleanest least-privilege design is to scope the allowed object ARNs directly. Option A is incorrect because $util.time is not an IAM policy condition key. Option D uses the wrong S3 condition key for the stated requirement. The uploaded study guide also emphasizes granting AWS Glue access through an IAM service role with the right scoped permissions, which fits this pattern.


NEW QUESTION # 263
A company is building a data lake for a new analytics team. The company is using Amazon S3 for storage and Amazon Athena for query analysis. All data that is in Amazon S3 is in Apache Parquet format.
The company is running a new Oracle database as a source system in the company's data center. The company has 70 tables in the Oracle database. All the tables have primary keys. Data can occasionally change in the source system. The company wants to ingest the tables every day into the data lake.
Which solution will meet this requirement with the LEAST effort?

Answer: A

Explanation:
The company needs to ingest tables from an on-premises Oracle database into a data lake on Amazon S3 in Apache Parquet format. The most efficient solution, requiring the least manual effort, would be to use AWS Database Migration Service (DMS) for continuous data replication.
Option C: Create an AWS Database Migration Service (AWS DMS) task for ongoing replication. Set the Oracle database as the source. Set Amazon S3 as the target. Configure the task to write the data in Parquet format.
AWS DMS can continuously replicate data from the Oracle database into Amazon S3, transforming it into Parquet format as it ingests the data. DMS simplifies the process by providing ongoing replication with minimal setup, and it automatically handles the conversion to Parquet format without requiring manual transformations or separate jobs. This option is the least effort solution since it automates both the ingestion and transformation processes.
Other options:
Option A (Apache Sqoop on EMR) involves more manual configuration and management, including setting up EMR clusters and writing Sqoop jobs.
Option B (AWS Glue bookmark job) involves configuring Glue jobs, which adds complexity. While Glue supports data transformations, DMS offers a more seamless solution for database replication.
Option D (RDS and Lambda triggers) introduces unnecessary complexity by involving RDS and Lambda for a task that DMS can handle more efficiently.
Reference:
AWS Database Migration Service (DMS)
DMS S3 Target Documentation


NEW QUESTION # 264
A company maintains an Amazon Redshift provisioned cluster that the company uses for extract, transform, and load (ETL) operations to support critical analysis tasks. A sales team within the company maintains a Redshift cluster that the sales team uses for business intelligence (BI) tasks.
The sales team recently requested access to the data that is in the ETL Redshift cluster so the team can perform weekly summary analysis tasks. The sales team needs to join data from the ETL cluster with data that is in the sales team's BI cluster.
The company needs a solution that will share the ETL cluster data with the sales team without interrupting the critical analysis tasks. The solution must minimize usage of the computing resources of the ETL cluster.
Which solution will meet these requirements?

Answer: C

Explanation:
Redshift data sharing is a feature that enables you to share live data across different Redshift clusters without the need to copy or move data. Data sharing provides secure and governed access to data, while preserving the performance and concurrency benefits of Redshift. By setting up the sales team BI cluster as a consumer of the ETL cluster, the company can share the ETL cluster data with the sales team without interrupting the critical analysis tasks. The solution also minimizes the usage of the computing resources of the ETL cluster, as the data sharing does not consume any storage space or compute resources from the producer cluster. The other options are either not feasible or not efficient. Creating materialized views or database views would require the sales team to have direct access to the ETL cluster, which could interfere with the critical analysis tasks.
Unloading a copy of the data from the ETL cluster to anAmazon S3 bucket every week would introduce additional latency and cost, as well as create data inconsistency issues. References:
Sharing data across Amazon Redshift clusters
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 2: Data Store Management, Section 2.2: Amazon Redshift


NEW QUESTION # 265
A data engineer needs to create an AWS Lambda function that converts the format of data from .csv to Apache Parquet. The Lambda function must run only if a user uploads a .csv file to an Amazon S3 bucket.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: C

Explanation:
Option A is the correct answer because it meets the requirements with the least operational overhead. Creating an S3 event notification that has an event type of s3:ObjectCreated:* will trigger the Lambda function whenever a new object is created in the S3 bucket. Using a filter rule to generate notifications only when the suffix includes .csv will ensure that the Lambda function only runs for .csv files. Setting the ARN of the Lambda function as the destination for the event notification will directly invoke the Lambda function without any additional steps.
Option B is incorrect because it requires the user to tag the objects with .csv, which adds an extra step and increases the operational overhead.
Option C is incorrect because it uses an event type of s3:*, which will trigger the Lambda function for any S3 event, not just object creation. This could result in unnecessary invocations and increased costs.
Option D is incorrect because it involves creating and subscribing to an SNS topic, which adds an extra layer of complexity and operational overhead.
:
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 3: Data Ingestion and Transformation, Section 3.2: S3 Event Notifications and Lambda Functions, Pages 67-69 Building Batch Data Analytics Solutions on AWS, Module 4: Data Transformation, Lesson 4.2: AWS Lambda, Pages 4-8 AWS Documentation Overview, AWS Lambda Developer Guide, Working with AWS Lambda Functions, Configuring Function Triggers, Using AWS Lambda with Amazon S3, Pages 1-5


NEW QUESTION # 266
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