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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q14-Q19):

NEW QUESTION # 14
A company saves customer data to an Amazon S3 bucket. The company uses server-side encryption with AWS KMS keys (SSE-KMS) to encrypt the bucket. The dataset includes personally identifiable information (PII) such as social security numbers and account details.
Data that is tagged as PII must be masked before the company uses customer data for analysis. Some users must have secure access to the PII data during the preprocessing phase. The company needs a low- maintenance solution to mask and secure the PII data throughout the entire engineering pipeline.
Which combination of solutions will meet these requirements? (Select TWO.)

  • A. Use AWS Identity and Access Management (IAM) to manage permissions and to control access to the PII data.
  • B. Use AWS Glue DataBrew to perform extract, transform, and load (ETL) tasks that mask the PII data before analysis.
  • C. Use Amazon GuardDuty to monitor access patterns for the PII data that is used in the engineering pipeline.
  • D. Write custom scripts in an application to mask the PII data and to control access.
  • E. Configure an Amazon Made discovery job for the S3 bucket.

Answer: A,B

Explanation:
To address the requirement ofmasking PII dataand ensuring secure access throughout the data pipeline, the combination ofAWS Glue DataBrewandIAMprovides a low-maintenance solution.
* A. AWS Glue DataBrew for Masking:
* AWS Glue DataBrew provides a visual tool to performdata transformations, includingmasking PII data. It allows for easy configuration of data transformation tasks without requiring manual coding, making it ideal for this use case.
Reference:AWS Glue DataBrew
D: AWS Identity and Access Management (IAM):
UsingIAM policiesallows fine-grained control over access to PII data, ensuring that only authorized users can view or process sensitive data during the pipeline stages.
Reference:AWS IAM Best Practices
Alternatives Considered:
B (Amazon GuardDuty): GuardDuty is for threat detection and does not handle data masking or access control for PII.
C (Amazon Macie): Macie can help discover sensitive data but does not handle the masking of PII or access control.
E (Custom scripts): Custom scripting increases the operational burden compared to a built-in solution like DataBrew.
References:
AWS Glue DataBrew for Data Masking
IAM Policies for PII Access Control


NEW QUESTION # 15
A hotel management company receives daily data files from each of its hotels. The company wants to upload its data to AWS. The company plans to use Amazon Athena to access the files. The company needs to protect the files from accidental deletion. The company will develop an application on its on-premises servers to automatically forward the files to a fully managed AWS ingestion service.
Which solution will meet these requirements with the LEAST operational overhead?

  • A. Use the Amazon Kinesis Agent on the on-premises servers to send data to Amazon Data Firehose. Store the data in an Amazon S3 bucket that has versioning enabled.
  • B. Use a self-managed Apache Kafka agent on the on-premises servers to stream data to Amazon Managed Streaming for Apache Kafka (Amazon MSK). Store the data in an Amazon S3 bucket with versioning enabled.
  • C. Use AWS Glue jobs to ingest data from the on-premises servers into Amazon RDS. Enable automated backups for data protection.
  • D. Use AWS DataSync to replicate data from the on-premises servers to Amazon Elastic File System (Amazon EFS). Configure automatic backups in AWS Backup.

Answer: A

Explanation:
Amazon Kinesis Data Firehose is a fully managed data ingestion service that enables reliable and scalable delivery of streaming and batch data into Amazon S3 with minimal operational overhead. This directly satisfies the requirement for a fully managed AWS ingestion service while avoiding the need to provision, scale, or manage infrastructure.
By using the Amazon Kinesis Agent on the on-premises servers, the company can automatically forward daily data files to Kinesis Data Firehose. Firehose handles buffering, retry logic, scaling, and delivery without requiring administrative effort. Delivering the data to Amazon S3 allows seamless integration with Amazon Athena, which natively queries data stored in S3 without requiring data movement or transformation.
Enabling Amazon S3 versioning protects files from accidental deletion by preserving previous versions of objects. This aligns with AWS best practices for data durability and governance, especially for analytics workloads and compliance requirements.
Other options introduce unnecessary operational complexity. AWS DataSync with Amazon EFS is not optimized for Athena-based analytics. AWS Glue jobs and Amazon RDS are unsuitable for file-based analytical access. A self-managed Apache Kafka solution with Amazon MSK significantly increases operational overhead.
Therefore, option B is the most efficient, scalable, and operationally optimal solution according to AWS Certified Data Engineer - Associate best practices.


NEW QUESTION # 16
A financial company wants to use Amazon Athena to run on-demand SQL queries on a petabyte-scale dataset to support a business intelligence (BI) application. An AWS Glue job that runs during non-business hours updates the dataset once every day. The BI application has a standard data refresh frequency of 1 hour to comply with company policies.
A data engineer wants to cost optimize the company's use of Amazon Athena without adding any additional infrastructure costs.
Which solution will meet these requirements with the LEAST operational overhead?

  • A. Use the query result reuse feature of Amazon Athena for the SQL queries.
  • B. Add an Amazon ElastiCache cluster between the Bl application and Athena.
  • C. Configure an Amazon S3 Lifecycle policy to move data to the S3 Glacier Deep Archive storage class after 1 day
  • D. Change the format of the files that are in the dataset to Apache Parquet.

Answer: A

Explanation:
The best solution to cost optimize the company's use of Amazon Athena without adding any additional infrastructure costs is to use the query result reuse feature of AmazonAthena for the SQL queries. This feature allows you to run the same query multiple times without incurring additional charges, as long as the underlying data has not changed and the query results are still in the query result location in Amazon S31. This feature is useful for scenarios where you have a petabyte-scale dataset that is updated infrequently, such as once a day, and you have a BI application that runs the same queries repeatedly, such as every hour. By using the query result reuse feature, you can reduce the amount of data scanned by your queries and save on the cost of running Athena. You can enable or disable this feature at the workgroup level or at the individual query level1.
Option A is not the best solution, as configuring an Amazon S3 Lifecycle policy to move data to the S3 Glacier Deep Archive storage class after 1 day would not cost optimize the company's use of Amazon Athena, but rather increase the cost and complexity. Amazon S3 Lifecycle policies are rules that you can define to automatically transition objects between different storage classes based on specified criteria, such as the age of the object2. S3 Glacier Deep Archive is the lowest-cost storage class in Amazon S3, designed for long-term data archiving that is accessed once or twice in a year3. While moving data to S3 Glacier Deep Archive can reduce the storage cost, it would also increase the retrieval cost and latency, as it takes up to 12 hours to restore the data from S3 Glacier Deep Archive3. Moreover, Athena does not support querying data that is in S3 Glacier or S3 Glacier Deep Archive storage classes4. Therefore, using this option would not meet the requirements of running on-demand SQL queries on the dataset.
Option C is not the best solution, as adding an Amazon ElastiCache cluster between the BI application and Athena would not cost optimize the company's use of Amazon Athena, but rather increase the cost and complexity. Amazon ElastiCache is a service that offers fully managed in-memory data stores, such as Redis and Memcached, that can improve the performance and scalability of web applications by caching frequently accessed data. While using ElastiCache can reduce the latency and load on the BI application, it would not reduce the amount of data scanned by Athena, which is the main factor that determines the cost of running Athena. Moreover, using ElastiCache would introduce additional infrastructure costs and operational overhead, as you would have to provision, manage, and scale the ElastiCache cluster, and integrate it with the BI application and Athena.
Option D is not the best solution, as changing the format of the files that are in the dataset to Apache Parquet would not cost optimize the company's use of Amazon Athena without adding any additional infrastructure costs, but rather increase the complexity. Apache Parquet is a columnar storage format that can improve the performance of analytical queries by reducing the amount of data that needs to be scanned and providing efficient compression and encoding schemes. However,changing the format of the files that are in the dataset to Apache Parquet would require additional processing and transformation steps, such as using AWS Glue or Amazon EMR to convert the files from their original format to Parquet, and storing the converted files in a separate location in Amazon S3. This would increase the complexity and the operational overhead of the data pipeline, and also incur additional costs for using AWS Glue or Amazon EMR. References:
Query result reuse
Amazon S3 Lifecycle
S3 Glacier Deep Archive
Storage classes supported by Athena
[What is Amazon ElastiCache?]
[Amazon Athena pricing]
[Columnar Storage Formats]
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide


NEW QUESTION # 17
A data engineer needs Amazon Athena queries to finish faster. The data engineer notices that all the files the Athena queries use are currently stored in uncompressed .csv format. The data engineer also notices that users perform most queries by selecting a specific column.
Which solution will MOST speed up the Athena query performance?

  • A. Change the data format from .csvto Apache Parquet. Apply Snappy compression.
  • B. Compress the .csv files by using gzjg compression.
  • C. Change the data format from .csvto JSON format. Apply Snappy compression.
  • D. Compress the .csv files by using Snappy compression.

Answer: A

Explanation:
Amazon Athena is a serverless interactive query service that allows you to analyze data in Amazon S3 using standard SQL. Athena supports various data formats, such as CSV, JSON, ORC, Avro, and Parquet.
However, not all data formats are equally efficient for querying. Some data formats, such as CSV and JSON, are row-oriented, meaning that they store data as a sequence of records, each with the same fields. Row- oriented formats are suitable for loading and exporting data, but they are not optimal for analytical queries that often access only a subset of columns. Row-oriented formats also do not support compression or encoding techniques that can reduce the data size and improve the query performance.
On the other hand, some data formats, such as ORC and Parquet, are column-oriented, meaning that they store data as a collection of columns, each with a specific data type. Column-oriented formats are ideal for analytical queries that often filter, aggregate, or join data by columns. Column-oriented formats also support compression and encoding techniques that can reduce the data size and improve the query performance. For example, Parquet supports dictionary encoding, which replaces repeated values with numeric codes, and run- length encoding, which replaces consecutive identical values with a single value and a count. Parquet also supports various compression algorithms, such as Snappy, GZIP, and ZSTD, that can further reduce the data size and improve the query performance.
Therefore, changing the data format from CSV to Parquet and applying Snappy compression will most speed up the Athena query performance. Parquet is a column-oriented format that allows Athena to scan only the relevant columns and skip the rest, reducing the amount of data read from S3. Snappy is a compression algorithm that reduces the data size without compromising the query speed, as it is splittable and does not require decompression before reading. This solution will also reduce the cost of Athena queries, as Athena charges based on the amount of data scanned from S3.
The other options are not as effective as changing the data format to Parquet and applying Snappy compression. Changing the data format from CSV to JSON and applying Snappy compression will not improve the query performance significantly, as JSON is also a row-oriented format that does not support columnar access or encoding techniques. Compressing the CSV files by using Snappy compression will reduce the data size, but it will not improve the query performance significantly, as CSV is still a row-oriented format that does not support columnar access or encoding techniques. Compressing the CSV files by using gzjg compression will reduce the data size, but it will degrade the query performance, as gzjg is not a splittable compression algorithm and requires decompression before reading. References:
* Amazon Athena
* Choosing the Right Data Format
* AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 5: Data Analysis and Visualization, Section 5.1: Amazon Athena


NEW QUESTION # 18
A data engineer needs to use Amazon Neptune to develop graph applications.
Which programming languages should the engineer use to develop the graph applications? (Select TWO.)

  • A. Spark SQL
  • B. Gremlin
  • C. SQL
  • D. SPARQL
  • E. ANSI SQL

Answer: B,D

Explanation:
Amazon Neptune supports graph applications using Gremlin and SPARQL as query languages. Neptune is a fully managed graph database service that supports both property graph and RDF graph models.
* Option A: GremlinGremlin is a query language for property graph databases, which is supported by Amazon Neptune. It allows the traversal and manipulation of graph data in the property graph model.
* Option D: SPARQLSPARQL is a query language for querying RDF graph data in Neptune. It is used to query, manipulate, and retrieve information stored in RDF format.
Other options:
* SQL (Option B) and ANSI SQL (Option C) are traditional relational database query languages and are not used for graph databases.
* Spark SQL (Option E) is related to Apache Spark for big data processing, not for querying graph databases.
References:
* Amazon Neptune Documentation
* Gremlin Documentation
* SPARQL Documentation


NEW QUESTION # 19
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