Amazon Data-Engineer-Associate Q&A - in .pdf

  • Exam Code: Data-Engineer-Associate
  • Exam Name: AWS Certified Data Engineer - Associate (DEA-C01)
  • Q & A: 305 Questions and Answers
  • PDF Price: $59.99
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  • Exam Code: Data-Engineer-Associate
  • Exam Name: AWS Certified Data Engineer - Associate (DEA-C01)
  • Q & A: 305 Questions and Answers
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Amazon Data-Engineer-Associate Exam Syllabus Topics:

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

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

1. A data engineer needs to maintain a central metadata repository that users access through Amazon EMR and Amazon Athena queries. The repository needs to provide the schema and properties of many tables. Some of the metadata is stored in Apache Hive. The data engineer needs to import the metadata from Hive into the central metadata repository.
Which solution will meet these requirements with the LEAST development effort?

A) Use Amazon EMR and Apache Ranger.
B) Use the AWS Glue Data Catalog.
C) Use a Hive metastore on an EMR cluster.
D) Use a metastore on an Amazon RDS for MySQL DB instance.


2. 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?

A) Write a custom extract, transform, and load (ETL) job in Python. Use the DataFramedrop duplicatesf) function by importing the Pandas library to perform data deduplication.
B) Write an AWS Glue extract, transform, and load (ETL) job. Import the Python dedupe library. Use the dedupe library to perform data deduplication.
C) Write a custom extract, transform, and load (ETL) job in Python. Import the Python dedupe library. Use the dedupe library to perform data deduplication.
D) Write an AWS Glue extract, transform, and load (ETL) job. Use the FindMatches machine learning (ML) transform to transform the data to perform data deduplication.


3. A data engineer must implement a data cataloging solution to track schema changes in an Amazon Redshift table.
Which solution will meet these requirements?

A) Use AWS DataSync to log the table metadata to an AWS Glue Data Catalog. Use an AWS Glue crawler to update the Data Catalog every day.
B) Schedule an AWS Glue crawler to run every day on the table. Configure the crawler to update an Apache Hive metastore.
C) Use the AWS Schema Conversion Tool (AWS SCT) to log the table metadata to an Apache Hive metastore. Use Amazon EventBridge Scheduler to update the metastore every day.
D) Schedule an AWS Glue crawler to run every day on the table by using the Java Database Connectivity (JDBC) driver. Configure the crawler to update an AWS Glue Data Catalog.


4. A financial services company stores financial data in Amazon Redshift. A data engineer wants to run real- time queries on the financial data to support a web-based trading application. The data engineer wants to run the queries from within the trading application.
Which solution will meet these requirements with the LEAST operational overhead?

A) Use the Amazon Redshift Data API.
B) Set up Java Database Connectivity (JDBC) connections to Amazon Redshift.
C) Establish WebSocket connections to Amazon Redshift.
D) Store frequently accessed data in Amazon S3. Use Amazon S3 Select to run the queries.


5. A company uses Amazon Athena for one-time queries against data that is in Amazon S3. The company has several use cases. The company must implement permission controls to separate query processes and access to query history among users, teams, and applications that are in the same AWS account.
Which solution will meet these requirements?

A) Create an JAM role for each use case. Assign appropriate permissions to the role for each use case.
Associate the role with Athena.
B) Create an Athena workgroup for each use case. Apply tags to the workgroup. Create an 1AM policy that uses the tags to apply appropriate permissions to the workgroup.
C) Create an S3 bucket for each use case. Create an S3 bucket policy that grants permissions to appropriate individual IAM users. Apply the S3 bucket policy to the S3 bucket.
D) Create an AWS Glue Data Catalog resource policy that grants permissions to appropriate individual IAM users for each use case. Apply the resource policy to the specific tables that Athena uses.


Solutions:

Question # 1
Answer: B
Question # 2
Answer: D
Question # 3
Answer: D
Question # 4
Answer: A
Question # 5
Answer: B

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