Data-Engineer-Associate Test Score Report | Data-Engineer-Associate Valid Exam Prep

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Amazon Data-Engineer-Associate Exam Syllabus Topics:

Section	Weight	Objectives
Data Operations and Support	22%	- Monitor data pipelines 1. Amazon CloudWatch 2. AWS CloudTrail 3. Logging and metrics - Manage and troubleshoot data processes 1. Cost optimization 2. Debugging failed jobs 3. Performance tuning - Automate data pipelines 1. AWS Lambda triggers 2. Event-driven triggers 3. Scheduling jobs
Data Security and Governance	18%	- Apply authentication and authorization 1. AWS IAM policies and roles 2. Service control policies (SCPs) 3. Amazon S3 bucket policies - Ensure data encryption 1. AWS KMS 2. Encryption at rest and in transit - Implement data quality checks 1. AWS Glue DataBrew 2. Data validation - Manage data privacy and compliance 1. PII data handling 2. AWS Lake Formation permissions 3. Data masking and tokenization
Data Store Management	26%	- Manage data lifecycle 1. Data archiving 2. Data retention policies 3. Amazon S3 storage classes - Understand data cataloging 1. Data discovery and classification 2. AWS Glue Data Catalog 3. Schema evolution - Choose a data store 1. Data characteristics (structured, semi-structured, unstructured) 2. Amazon S3, Amazon RDS, Amazon DynamoDB, Amazon Redshift 3. Data lakes vs. data warehouses 4. Access and storage patterns - Design data models 1. Partitioning and indexing strategies 2. Normalization and denormalization 3. Schema design
Data Ingestion and Transformation	34%	- Perform data ingestion 1. Streaming data ingestion 2. Replayability of data 3. Batch data ingestion (scheduled ingestion, event-driven ingestion) 4. Data ingestion patterns (frequency and data history) 5. Throughput and latency characteristics for AWS services - Transform and process data 1. ETL/ELT patterns 2. Data transformation services (AWS Glue, Amazon EMR, AWS Lambda) 3. Data partitioning and compression 4. Batch and stream processing - Apply programming concepts 1. SQL, Python, Scala 2. Infrastructure as Code (IaC) 3. Version control - Orchestrate data pipelines 1. Amazon Managed Workflows for Apache Airflow (MWAA) 2. AWS Step Functions 3. AWS Glue Workflows 4. Event-driven architectures

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q29-Q34):

NEW QUESTION # 29
A company is migrating its database servers from Amazon EC2 instances that run Microsoft SQL Server to Amazon RDS for Microsoft SQL Server DB instances. The company's analytics team must export large data elements every day until the migration is complete. The data elements are the result of SQL joins across multiple tables. The data must be in Apache Parquet format. The analytics team must store the data in Amazon S3.
Which solution will meet these requirements in the MOST operationally efficient way?

A. Create a view in the EC2 instance-based SQL Server databases that contains the required data elements.
Create an AWS Glue job that selects the data directly from the view and transfers the data in Parquet format to an S3 bucket. Schedule the AWS Glue job to run every day.
B. Use a SQL query to create a view in the EC2 instance-based SQL Server databases that contains the required data elements. Create and run an AWS Glue crawler to read the view. Create an AWS Glue job that retrieves the data and transfers the data in Parquet format to an S3 bucket. Schedule the AWS Glue job to run every day.
C. Create an AWS Lambda function that queries the EC2 instance-based databases by using Java Database Connectivity (JDBC). Configure the Lambda function to retrieve the required data, transform the data into Parquet format, and transfer the data into an S3 bucket. Use Amazon EventBridge to schedule the Lambda function to run every day.
D. Schedule SQL Server Agent to run a daily SQL query that selects the desired data elements from the EC2 instance-based SQL Server databases. Configure the query to direct the output .csv objects to an S3 bucket. Create an S3 event that invokes an AWS Lambda function to transform the output format from .csv to Parquet.

Answer: A

Explanation:
Option A is the most operationally efficient way to meet the requirements because it minimizes the number of steps and services involved in the data export process. AWS Glue is a fully managed service that can extract, transform, and load (ETL) data from various sources to various destinations, including Amazon S3. AWS Glue can also convert data to different formats, such as Parquet, which is a columnar storage format that is optimized for analytics. By creating a view in the SQL Server databases that contains the required data elements, the AWS Glue job can select the data directly from the view without having to perform any joins or transformations on the source data. The AWS Glue job can then transfer the data in Parquet format to an S3 bucket and run on a daily schedule.
Option B is not operationally efficient because it involves multiple steps and services to export the data. SQL Server Agent is a tool that can run scheduled tasks on SQL Server databases, such as executing SQL queries.
However, SQL Server Agent cannot directly export data to S3, so the query output must be saved as .csv objects on the EC2 instance. Then, an S3 event must be configured to trigger an AWS Lambda function that can transform the .csv objects to Parquet format and upload them to S3. This option adds complexity and latency to the data export process and requires additional resources and configuration.
Option C is not operationally efficient because it introduces an unnecessary step of running an AWS Glue crawler to read the view. An AWS Glue crawler is a service that can scan data sources and create metadata tables in the AWS Glue Data Catalog. The Data Catalog is a central repository that stores information about the data sources, such as schema, format, and location. However, in this scenario, the schema and format of the data elements are already known and fixed, so there is no need to run a crawler to discover them. The AWS Glue job can directly select the data from the view without using the Data Catalog. Running a crawler adds extra time and cost to the data export process.
Option D is not operationally efficient because it requires custom code and configuration to query the databases and transform the data. An AWS Lambda function is a service that can run code in response to events or triggers, such as Amazon EventBridge. Amazon EventBridge is a service that can connect applications and services with event sources, such as schedules, and route them to targets, such as Lambda functions. However, in this scenario, using a Lambda function to query the databases and transform the data is not the best option because it requires writing and maintaining code that uses JDBC to connect to the SQL Server databases, retrieve the required data, convert the data to Parquet format, and transfer the data to S3.
This option also has limitations on the execution time, memory, and concurrency of the Lambda function, which may affect the performance and reliability of the data export process.
References:
* AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
* AWS Glue Documentation
* Working with Views in AWS Glue
* Converting to Columnar Formats

NEW QUESTION # 30
A company uses Amazon S3 buckets, AWS Glue tables, and Amazon Athena as components of a data lake. Recently, the company expanded its sales range to multiple new states. The company wants to introduce state names as a new partition to the existing S3 bucket, which is currently partitioned by date.
The company needs to ensure that additional partitions will not disrupt daily synchronization between the AWS Glue Data Catalog and the S3 buckets.
Which solution will meet these requirements with the LEAST operational overhead?

A. Run a REFRESH TABLE command in Athena.
B. Schedule an AWS Glue crawler to periodically update the Data Catalog.
C. Use the AWS Glue API to manually update the Data Catalog.
D. Run an MSCK REPAIR TABLE command in Athena.

Answer: B

Explanation:
Scheduling an AWS Glue crawler to periodically update the Data Catalog automates the process of detecting new partitions and updating the catalog, which minimizes manual maintenance and operational overhead.

NEW QUESTION # 31
A company has a data warehouse that contains a table that is named Sales. The company stores the table in Amazon Redshift The table includes a column that is named city_name. The company wants to query the table to find all rows that have a city_name that starts with "San" or "El." Which SQL query will meet this requirement?

A. Select * from Sales where city_name -,
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