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A data engineer needs to create a new empty table in Amazon Athena that has the same schema as an existing table named old-table.
Which SQL statement should the data engineer use to meet this requirement?
A.
B.
C.
D.
Problem Analysis:
The goal is to create a new empty table in Athena with the same schema as an existing table (old_table).
The solution must avoid copying any data.
Key Considerations:
CREATE TABLE AS (CTAS) is commonly used in Athena for creating new tables based on an existing table.
Adding the WITH NO DATA clause ensures only the schema is copied, without transferring any data.
Solution Analysis:
Option A: Copies both schema and data. Does not meet the requirement for an empty table.
Option B: Inserts data into an existing table, which does not create a new table.
Option C: Creates an empty table but does not copy the schema.
Option D: Creates a new table with the same schema and ensures it is empty by using WITH NO DATA.
Final Recommendation:
Use D. CREATE TABLE new_table AS (SELECT * FROM old_table) WITH NO DATA to create an empty table with the same schema.
A data engineer must ingest a source of structured data that is in .csv format into an Amazon S3 data lake. The .csv files contain 15 columns. Data analysts need to run Amazon Athena queries on one or two columns of the dataset. The data analysts rarely query the entire file.
Which solution will meet these requirements MOST cost-effectively?
A marketing company uses Amazon S3 to store marketing dat
a. The company uses versioning in some buckets. The company runs several jobs to read and load data into the buckets.
To help cost-optimize its storage, the company wants to gather information about incomplete multipart uploads and outdated versions that are present in the S3 buckets.
Which solution will meet these requirements with the LEAST operational effort?
The company wants to gather information about incomplete multipart uploads and outdated versions in its Amazon S3 buckets to optimize storage costs.
Option B: Use Amazon S3 Inventory configurations reports to gather the information. S3 Inventory provides reports that can list incomplete multipart uploads and versions of objects stored in S3. It offers an easy, automated way to track object metadata across buckets, including data necessary for cost optimization, without manual effort.
Options A (AWS CLI), C (S3 Storage Lens), and D (usage reports) either do not specifically gather the required information about incomplete uploads and outdated versions or require more manual intervention.
A company needs a solution to manage costs for an existing Amazon DynamoDB table. The company also needs to control the size of the table. The solution must not disrupt any ongoing read or write operations. The company wants to use a solution that automatically deletes data from the table after 1 month.
Which solution will meet these requirements with the LEAST ongoing maintenance?
The requirement is to manage the size of an Amazon DynamoDB table by automatically deleting data older than 1 month without disrupting ongoing read or write operations. The simplest and most maintenance-free solution is to use DynamoDB Time-to-Live (TTL).
Option A: Use the DynamoDB TTL feature to automatically expire data based on timestamps. DynamoDB TTL allows you to specify an attribute (e.g., a timestamp) that defines when items in the table should expire. After the expiration time, DynamoDB automatically deletes the items, freeing up storage space and keeping the table size under control without manual intervention or disruptions to ongoing operations.
Other options involve higher maintenance and manual scheduling or scanning operations, which increase complexity unnecessarily compared to the native TTL feature.
A data engineer must use AWS services to ingest a dataset into an Amazon S3 data lake. The data engineer profiles the dataset and discovers that the dataset contains personally identifiable information (PII). The data engineer must implement a solution to profile the dataset and obfuscate the PII.
Which solution will meet this requirement with the LEAST operational effort?
AWS Glue is a fully managed service that provides a serverless data integration platform for data preparation, data cataloging, and data loading. AWS Glue Studio is a graphical interface that allows you to easily author, run, and monitor AWS Glue ETL jobs. AWS Glue Data Quality is a feature that enables you to validate, cleanse, and enrich your data using predefined or custom rules. AWS Step Functions is a service that allows you to coordinate multiple AWS services into serverless workflows.
Using the Detect PII transform in AWS Glue Studio, you can automatically identify and label the PII in your dataset, such as names, addresses, phone numbers, email addresses, etc. You can then create a rule in AWS Glue Data Quality to obfuscate the PII, such as masking, hashing, or replacing the values with dummy data. You can also use other rules to validate and cleanse your data, such as checking for null values, duplicates, outliers, etc. You can then use an AWS Step Functions state machine to orchestrate a data pipeline to ingest the data into the S3 data lake. You can use AWS Glue DataBrew to visually explore and transform the data, AWS Glue crawlers to discover and catalog the data, and AWS Glue jobs to load the data into the S3 data lake.
This solution will meet the requirement with the least operational effort, as it leverages the serverless and managed capabilities of AWS Glue, AWS Glue Studio, AWS Glue Data Quality, and AWS Step Functions. You do not need to write any code to identify or obfuscate the PII, as you can use the built-in transforms and rules in AWS Glue Studio and AWS Glue Data Quality. You also do not need to provision or manage any servers or clusters, as AWS Glue and AWS Step Functions scale automatically based on the demand.
The other options are not as efficient as using the Detect PII transform in AWS Glue Studio, creating a rule in AWS Glue Data Quality, and using an AWS Step Functions state machine. Using an Amazon Kinesis Data Firehose delivery stream to process the dataset, creating an AWS Lambda transform function to identify the PII, using an AWS SDK to obfuscate the PII, and setting the S3 data lake as the target for the delivery stream will require more operational effort, as you will need to write and maintain code to identify and obfuscate the PII, as well as manage the Lambda function and its resources. Using the Detect PII transform in AWS Glue Studio to identify the PII, obfuscating the PII, and using an AWS Step Functions state machine to orchestrate a data pipeline to ingest the data into the S3 data lake will not be as effective as creating a rule in AWS Glue Data Quality to obfuscate the PII, as you will need to manually obfuscate the PII after identifying it, which can be error-prone and time-consuming. Ingesting the dataset into Amazon DynamoDB, creating an AWS Lambda function to identify and obfuscate the PII in the DynamoDB table and to transform the data, and using the same Lambda function to ingest the data into the S3 data lake will require more operational effort, as you will need to write and maintain code to identify and obfuscate the PII, as well as manage the Lambda function and its resources. You will also incur additional costs and complexity by using DynamoDB as an intermediate data store, which may not be necessary for your use case.Reference:
[AWS Step Functions]
[AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide], Chapter 6: Data Integration and Transformation, Section 6.1: AWS Glue
