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| Section | Weight | Objectives |
|---|---|---|
| Data Operations and Support | 22% | - Monitor and maintain data pipelines - Troubleshoot data workflow issues |
| Data Security and Governance | 18% | - Apply governance and compliance best practices - Implement data security controls |
| Data Store Management | 26% | - Optimize storage performance and cost - Select appropriate data storage solutions |
| Data Ingestion and Transformation | 34% | - Ingest and transform data using AWS services - Build and manage data pipelines |
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NEW QUESTION # 52
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?
Answer: A
Explanation:
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.
Reference:
Athena CTAS Queries
CREATE TABLE Statement in Athena
NEW QUESTION # 53
A data engineer needs to build an extract, transform, and load (ETL) job. The ETL job will process daily incoming .csv files that users upload to an Amazon S3 bucket. The size of each S3 object is less than 100 MB.
Which solution will meet these requirements MOST cost-effectively?
Answer: C
Explanation:
AWS Glue is a fully managed serverless ETL service that can handle various data sources and formats, including .csv files in Amazon S3. AWS Glue provides two types of jobs: PySpark and Python shell. PySpark jobs use Apache Spark to process large-scale data in parallel, while Python shell jobs use Python scripts to process small-scale data in a single execution environment. For this requirement, a Python shell job is more suitable and cost-effective, as the size of each S3 object is less than 100 MB, which does not require distributed processing. A Python shell job can use pandas, a popular Python library fordata analysis, to transform the .csv data as needed. The other solutions are not optimal or relevant for this requirement. Writing a custom Python application and hosting it on an Amazon EKS cluster would require more effort and resources to set up and manage the Kubernetes environment, as well as to handle the data ingestion and transformation logic. Writing a PySpark ETL script and hosting it on an Amazon EMR cluster would also incur more costs and complexity to provision and configure the EMR cluster, as well as to use Apache Spark for processing small data files. Writing an AWS Glue PySpark job would also be less efficient and economical than a Python shell job, as it would involve unnecessary overhead and charges for using Apache Spark for small data files. References:
AWS Glue
Working with Python Shell Jobs
pandas
[AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide]
NEW QUESTION # 54
A company needs to store semi-structured transactional data in a serverless database.
The application writes data infrequently but reads it frequently, with millisecond retrieval required.
Answer: D
Explanation:
Amazon DynamoDB is a serverless, low-latency, NoSQL database ideal for semi-structured data.
Adding DynamoDB Accelerator (DAX) provides microsecond response times for read-heavy workloads.
"For applications requiring millisecond or sub-millisecond reads with serverless operation, use DynamoDB with DAX caching."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf
NEW QUESTION # 55
A data engineer is implementing model governance for machine learning (ML) workflows on AWS. The data engineer needs a solution that can track the complete lifecycle of the ML models, including data preparation, model training, and deployment stages. The solution must ensure reproducibility and audit compliance.
Answer: C
Explanation:
Amazon SageMaker ML Lineage Tracking provides a unified mechanism to automatically capture and track the lineage of ML artifacts (datasets, jobs, and models). It supports auditability, reproducibility, and compliance by maintaining metadata for every stage of the ML lifecycle.
"Use Amazon SageMaker Lineage Tracking to automatically record and track the lineage of data, training jobs, models, and endpoints to ensure auditability and reproducibility."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf
NEW QUESTION # 56
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?
Answer: D
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 # 57
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