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Amazon Data-Engineer-Associate Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Security and Governance	18%	- Implement data security controls - Apply governance and compliance best practices
Topic 2: Data Operations and Support	22%	- Troubleshoot data workflow issues - Monitor and maintain data pipelines
Topic 3: Data Ingestion and Transformation	34%	- Ingest and transform data using AWS services - Build and manage data pipelines
Topic 4: Data Store Management	26%	- Select appropriate data storage solutions - Optimize storage performance and cost

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q36-Q41):

NEW QUESTION # 36
A retail company is developing a data lake solution on Amazon S3 to analyze historical sales data. The solution needs to support frequent schema changes as new product attributes are added. The company must also be able to query point-in-time historical data snapshots for compliance reporting. The solution must provide atomicity, consistency, isolation, and durability (ACID) transaction guarantees for concurrent write operations.
Which solution will meet these requirements?

A. Store the data in Amazon DynamoDB with a composite primary key that includes a timestamp. Use Amazon DynamoDB Streams to capture changes and replicate to Amazon S3 in Parquet format.
B. Enable Amazon S3 Versioning on the company ' s S3 bucket. Create an AWS Glue crawler to catalog the data. Use AWS Glue extract, transform, and load (ETL) jobs to read specific S3 version IDs.
C. Create an AWS Glue Data Catalog table that uses CSV format. Schedule AWS Glue extract, transform, and load (ETL) jobs to transform the data into Parquet format and partition by date.
D. Create an AWS Glue Data Catalog table that uses Apache Iceberg table format. Set the format version to 2. Configure time travel retention policies in the table properties.

Answer: D

Explanation:
The correct answer is B because Apache Iceberg is designed for analytical data lake tables that need schema evolution, transactional consistency, and historical snapshot access. AWS Glue supports Apache Iceberg for data lakes and can read and write Iceberg tables in Amazon S3 through the AWS Glue Data Catalog. The AWS Glue documentation also shows Iceberg tables being created with TBLPROPERTIES ( " format-version " = " 2 " ), which is the correct table format direction for modern Iceberg capabilities. CSV with scheduled conversion does not provide ACID transactions or point-in-time table snapshots. S3 Versioning tracks object versions but does not provide table-level ACID semantics. DynamoDB is not the best fit for an S3-based analytical data lake requirement.

NEW QUESTION # 37
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?

A. Add 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.
B. Add a policy to the data engineer's IAM user that allows the sts:AddAssociation action for the AWS Glue and SageMaker service principals in the trust policy.
C. Add the AmazonSageMakerFullAccess managed policy to the data engineer's IAM user.
D. Add the AWSGlueServiceRole managed policy to the data engineer's IAM user.

Answer: A

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 # 38
A company is using an AWS Transfer Family server to migrate data from an on-premises environment to AWS. Company policy mandates the use of TLS 1.2 or above to encrypt the data in transit.
Which solution will meet these requirements?

A. Generate new SSH keys for the Transfer Family server. Make the old keys and the new keys available for use.
B. Update the security group rules for the on-premises network to allow only connections that use TLS 1.2 or above.
C. Install an SSL certificate on the Transfer Family server to encrypt data transfers by using TLS 1.2.
D. Update the security policy of the Transfer Family server to specify a minimum protocol version of TLS
1.2.

Answer: C

NEW QUESTION # 39
An ecommerce company stores sales data in an AWS Glue table named sales_data. The company stores the sales_data table in an Amazon S3 Standard bucket. The table contains columns named order_id, customer_id, product_id, order_date, shipping_date, and order_amount.
The company wants to improve query performance by partitioning the sales_data table by order_date. The company needs to add the partition to the existing sales_data table in AWS Glue.
Which solution will meet these requirements?

A. Update the AWS Glue table's schema to include the new partition.
B. Use the AWS Glue Data Catalog API to add the new partition to the table.
C. Edit the AWS Glue table's metadata file directly in Amazon S3.
D. Manually modify the S3 bucket to use the new partition.

Answer: B

Explanation:
In AWS Glue, table partitions are managed as metadata objects within the AWS Glue Data Catalog. To add a new partition to an existing table, the correct and supported approach is to use the AWS Glue Data Catalog API, such as the CreatePartition operation, or equivalent console or SDK actions.
Updating the table schema alone does not create partitions or inform Glue about new partition values. Editing metadata files directly in Amazon S3 is unsupported and can corrupt the Data Catalog. Manually modifying the S3 bucket structure without registering partitions in Glue will result in Athena and other query engines being unable to recognize the partitions.
By adding partitions through the Glue Data Catalog API, query engines such as Amazon Athena and Amazon Redshift Spectrum can perform partition pruning, which significantly improves query performance by scanning only relevant data.
This method aligns with AWS best practices, ensures metadata consistency, and avoids unnecessary operational risk. Therefore, Option C is the correct solution.

NEW QUESTION # 40
A data engineer notices slow query performance on a highly partitioned table that is in Amazon Athena. The table contains daily data for the previous 5 years, partitioned by date. The data engineer wants to improve query performance and to automate partition management. Which solution will meet these requirements?

A. Increase the processing capacity of Athena queries by allocating more compute resources.
B. Reduce the number of partitions by changing the partitioning schema from dairy to monthly granularity.
C. Use an AWS Lambda function that runs daily. Configure the function to manually create new partitions in AW5 Glue for each day's data.
D. Use partition projection in Athena. Configure the table properties by using a date range from 5 years ago to the present.

Answer: D

NEW QUESTION # 41
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