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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q160-Q165):

NEW QUESTION # 160
A company plans to use Amazon Kinesis Data Firehose to store data in Amazon S3. The source data consists of 2 MB csv files. The company must convert the .csv files to JSON format. The company must store the files in Apache Parquet format.
Which solution will meet these requirements with the LEAST development effort?

A. Use Kinesis Data Firehose to invoke an AWS Lambda function that transforms the .csv files to JSON.Use Kinesis Data Firehose to store the files in Parquet format.
B. Use Kinesis Data Firehose to convert the csv files to JSON and to store the files in Parquet format.
C. Use Kinesis Data Firehose to convert the csv files to JSON. Use an AWS Lambda function to store the files in Parquet format.
D. Use Kinesis Data Firehose to invoke an AWS Lambda function that transforms the .csv files to JSON and stores the files in Parquet format.

Answer: B

Explanation:
The company wants to use Amazon Kinesis Data Firehose to transform CSV files into JSON format and store the files in Apache Parquet format with the least development effort.
* Option B: Use Kinesis Data Firehose to convert the CSV files to JSON and to store the files in Parquet format.Kinesis Data Firehose supports data format conversion natively, including converting incoming CSV data to JSON format and storing the resulting files in Parquet format in Amazon S3.
This solution requires the least development effort because it uses built-in transformation features of Kinesis Data Firehose.
Other options (A, C, D) involve invoking AWS Lambda functions, which would introduce additional complexity and development effort compared to Kinesis Data Firehose's native format conversion capabilities.
References:
* Amazon Kinesis Data Firehose Documentation

NEW QUESTION # 161
A retail company uses an Amazon Redshift data warehouse and an Amazon S3 bucket. The company ingests retail order data into the S3 bucket every day.
The company stores all order data at a single path within the S3 bucket. The data has more than 100 columns.
The company ingests the order data from a third-party application that generates more than 30 files in CSV format every day. Each CSV file is between 50 and 70 MB in size.
The company uses Amazon Redshift Spectrum to run queries that select sets of columns. Users aggregate metrics based on daily orders. Recently, users have reported that the performance of the queries has degraded.
A data engineer must resolve the performance issues for the queries.
Which combination of steps will meet this requirement with LEAST developmental effort? (Select TWO.)

A. Partition the order data in the S3 bucket based on order date.
B. Develop an AWS Glue ETL job to convert the multiple daily CSV files to one file for each day.
C. Configure the third-party application to create the files in a columnar format.
D. Configure the third-party application to create the files in JSON format.
E. Load the JSON data into the Amazon Redshift table in a SUPER type column.

Answer: A,C

Explanation:
The performance issue in Amazon Redshift Spectrum queries arises due to the nature of CSV files, which are row-based storage formats. Spectrum is more optimized for columnar formats, which significantly improve performance by reducing the amount of data scanned. Also, partitioning data based on relevant columns like order date can further reduce the amount of data scanned, as queries can focus only on the necessary partitions.
* A. Configure the third-party application to create the files in a columnar format:
* Columnar formats (like Parquet or ORC) store data in a way that is optimized for analytical queries because they allow queries to scan only the columns required, rather than scanning all columns in a row-based format like CSV.
* Amazon Redshift Spectrum works much more efficiently with columnar formats, reducing the amount of data that needs to be scanned, which improves query performance.

NEW QUESTION # 162
A company stores logs in an Amazon S3 bucket. When a data engineer attempts to access several log files, the data engineer discovers that some files have been unintentionally deleted.
The data engineer needs a solution that will prevent unintentional file deletion in the future.
Which solution will meet this requirement with the LEAST operational overhead?

A. Use an Amazon S3 Glacier storage class to archive the data that is in the S3 bucket.
B. Enable S3 Versioning for the S3 bucket.
C. Configure replication for the S3 bucket.
D. Manually back up the S3 bucket on a regular basis.

Answer: B

Explanation:
To prevent unintentional file deletions and meet the requirement with minimal operational overhead, enabling S3 Versioningis the best solution.
* S3 Versioning:
* S3 Versioning allows multiple versions of an object to be stored in the same S3 bucket. When a file is deleted or overwritten, S3 preserves the previous versions, which means you canrecover from accidental deletions or modifications.
* Enabling versioning requires minimal overhead, as it is abucket-level settingand does not require additional backup processes or data replication.
* Users can recover specific versions of files that were unintentionally deleted, meeting the needs of the data engineer to avoid accidental data loss.
Reference:Amazon S3 Versioning
Alternatives Considered:
A (Manual backups): Manually backing up the bucket requires higher operational effort and maintenance compared to enabling S3 Versioning, which is automated.
C (S3 Replication): Replication ensures data is copied to another bucket but does not provide protection against accidental deletion. It would increase operational costs without solving the core issue of accidental deletion.
D (S3 Glacier): Storing data in Glacier provides long-term archival storage but is not designed to prevent accidental deletion. Glacier is also more suitable for archival and infrequently accessed data, not for active logs.
References:
Amazon S3 Versioning Documentation
S3 Data Protection Best Practices

NEW QUESTION # 163
A data engineer needs to build an enterprise data catalog based on the company's Amazon S3 buckets and Amazon RDS databases. The data catalog must include storage format metadata for the data in the catalog.
Which solution will meet these requirements with the LEAST effort?

A. Use scripts to scan data elements and to assign data classifications based on the format of the data.
B. Use an AWS Glue crawler to scan the S3 buckets and RDS databases and build a data catalog. Use data stewards to inspect the data and update the data catalog with the data format.
C. Use an AWS Glue crawler to build a data catalog. Use AWS Glue crawler classifiers to recognize the format of data and store the format in the catalog.
D. Use Amazon Macie to build a data catalog and to identify sensitive data elements. Collect the data format information from Macie.

Answer: B

Explanation:
To build an enterprise data catalog with metadata for storage formats, the easiest and most efficient solution is using anAWS Glue crawler. The Glue crawler can scanAmazon S3buckets andAmazon RDSdatabases to automatically create a data catalog that includes metadata such as the schema and storage format (e.g., CSV, Parquet, etc.). By usingAWS Glue crawler classifiers, you can configure the crawler to recognize the format of the data and store this information directly in the catalog.
* Option B: Use an AWS Glue crawler to build a data catalog. Use AWS Glue crawler classifiers to recognize the format of data and store the format in the catalog.This option meets the requirements with the least effort because Glue crawlers automate the discovery and cataloging of data from multiple sources, including S3 and RDS, while recognizing various file formats via classifiers.
Other options (A, C, D) involve additional manual steps, like having data stewards inspect the data, or using services likeAmazon Maciethat focus more on sensitive data detection rather than format cataloging.
References:
AWS Glue Crawler Documentation
AWS Glue Classifiers

NEW QUESTION # 164
A company hosts its applications on Amazon EC2 instances. The company must use SSL/TLS connections that encrypt data in transit to communicate securely with AWS infrastructure that is managed by a customer.
A data engineer needs to implement a solution to simplify the generation, distribution, and rotation of digital certificates. The solution must automatically renew and deploy SSL/TLS certificates.
Which solution will meet these requirements with the LEAST operational overhead?

A. Store self-managed certificates on the EC2 instances.
B. Use AWS Certificate Manager (ACM).
C. Use Amazon Elastic Container Service (Amazon ECS) Service Connect.
D. Implement custom automation scripts in AWS Secrets Manager.

Answer: B

Explanation:
The best solution for managing SSL/TLS certificates on EC2 instances withminimal operational overheadis to useAWS Certificate Manager (ACM). ACM simplifies certificate management by automating the provisioning, renewal, and deployment of certificates.
* AWS Certificate Manager (ACM):
* ACM managesSSL/TLS certificatesfor EC2 and other AWS resources, including automatic certificate renewal. This reduces the need for manual management and avoids operational complexity.
* ACM also integrates with other AWS services to simplify secure connections between AWS infrastructure and customer-managed environments.
Reference:AWS Certificate Manager
Alternatives Considered:
A (Self-managed certificates): Managing certificates manually on EC2 instances increases operational overhead and lacks automatic renewal.
C (Secrets Manager automation): While Secrets Manager can store keys and certificates, it requires custom automation for rotation and does not handle SSL/TLS certificates directly.
D (ECS Service Connect): This is unrelated to SSL/TLS certificate management and would not address the operational need.
References:
AWS Certificate Manager Documentation

NEW QUESTION # 165
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