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| Section | Weight | Objectives |
|---|---|---|
| Data Operations and Support | 22% | - Automate data pipelines
|
| Data Security and Governance | 18% | - Ensure data encryption
|
| Data Store Management | 26% | - Manage data lifecycle
|
| Data Ingestion and Transformation | 34% | - Perform data ingestion
|
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NEW QUESTION # 70
A company needs to implement a new inventory management system that provides near real-time updates and visibility across all AWS Regions. The new solution must provide centralized access control over data access and permissions. The company has a separate inventory management team assigned to each Region. Each inventory management team needs to update inventory levels.
A data engineer must implement Amazon Redshift data sharing with write capabilities. The solution must follow the principle of least privilege.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: A
Explanation:
Amazon Redshift data sharing now supports bi-directional and selective write access through datashare namespaces.
This allows centralized management of permissions from headquarters while giving Regional clusters controlled write access to specific datasets - adhering to least privilege and minimizing admin overhead.
"Use Redshift datashares with write permissions and namespaces to enable controlled cross-Region data collaboration while maintaining centralized governance."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf This configuration ensures near real-time inventory updates, centralized access management, and compliance with AWS security best practices.
NEW QUESTION # 71
A company is building an analytics solution. The solution uses Amazon S3 for data lake storage and Amazon Redshift for a data warehouse. The company wants to use Amazon Redshift Spectrum to query the data that is in Amazon S3.
Which actions will provide the FASTEST queries? (Choose two.)
Answer: D,E
Explanation:
Amazon Redshift Spectrum is a feature that allows you to run SQL queries directly against data in Amazon S3, without loading or transforming the data. Redshift Spectrum can query 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 ofcolumns. 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, using a columnar storage file format, such as Parquet, will provide faster queries, as it allows Redshift Spectrum to scan only the relevant columns and skip the rest, reducing the amount of data read from S3. Additionally, partitioning the data based on the most common query predicates, such as date, time, region, etc., will provide faster queries, as it allows Redshift Spectrum to prune the partitions that do not match the query criteria, reducing the amount of data scanned from S3. Partitioning also improves the performance of joins and aggregations, as it reduces data skew and shuffling.
The other options are not as effective as using a columnar storage file format and partitioning the data. Using gzip compression to compress individual files to sizes that are between 1 GB and 5 GB will reduce the data size, but it will not improve the query performance significantly, as gzip is not a splittable compression algorithm and requires decompression before reading. Splitting the data into files that are less than 10 KB will increase the number of files and the metadata overhead, which will degrade the query performance. Using file formats that are not supported by Redshift Spectrum, such as XML, will not work, as Redshift Spectrum will not be able to read or parse the data. References:
Amazon Redshift Spectrum
Choosing the Right Data Format
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 4: Data Lakes and Data Warehouses, Section 4.3: Amazon Redshift Spectrum
NEW QUESTION # 72
A company is migrating on-premises workloads to AWS. The company wants to reduce overall operational overhead. The company also wants to explore serverless options.
The company's current workloads use Apache Pig, Apache Oozie, Apache Spark, Apache Hbase, and Apache Flink. The on-premises workloads process petabytes of data in seconds. The company must maintain similar or better performance after the migration to AWS.
Which extract, transform, and load (ETL) service will meet these requirements?
Answer: D
Explanation:
AWS Glue is a fully managed serverless ETL service that can handle petabytes of data in seconds. AWS Glue can run Apache Spark and Apache Flink jobs without requiring any infrastructure provisioning or management. AWS Glue can also integrate with Apache Pig, Apache Oozie, and Apache Hbase using AWS Glue Data Catalog and AWS Glue workflows. AWS Glue can reduce the overall operational overhead by automating the data discovery, data preparation, and data loading processes. AWS Glue can also optimize the cost and performance of ETL jobs by using AWS Glue Job Bookmarking, AWS Glue Crawlers, and AWS Glue Schema Registry. Reference:
AWS Glue
AWS Glue Data Catalog
AWS Glue Workflows
[AWS Glue Job Bookmarking]
[AWS Glue Crawlers]
[AWS Glue Schema Registry]
[AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide]
NEW QUESTION # 73
A company stores a large dataset in an Amazon S3 bucket. A data engineer frequently runs complex queries on the dataset by using Amazon Athena. The data engineer needs to optimize query performance and optimize costs for queries that are run multiple times with the same parameters.
Which solution will meet these requirements?
Answer: D
Explanation:
Comprehensive and Detailed Explanation (150-250 words)
Amazon Athena charges per amount of data scanned by each query. When the same query with identical parameters is executed multiple times, re-scanning the data repeatedly increases both cost and execution time.
Athena provides query result reuse, which allows previously computed query results to be reused when an identical query is rerun within a configurable time window.
By enabling query result reuse at the Athena workgroup level, Athena automatically returns cached results instead of re-reading data from Amazon S3. This significantly improves query performance while reducing data scan costs. This feature is specifically designed for workloads where analysts frequently rerun complex queries with the same parameters, which directly matches the scenario.
Converting data to JSON would degrade performance because JSON is not a columnar format and increases scan size. Using Amazon EMR introduces unnecessary infrastructure management and cost for a query optimization problem that Athena already solves natively. Amazon Redshift Spectrum is intended for querying S3 data from within Redshift and does not address repeated-query optimization in Athena.
Therefore, configuring query result reuse in the Athena workgroup is the most cost-effective and operationally efficient solution.
NEW QUESTION # 74
A company receives .csv files that contain physical address data. The data is in columns that have the following names: Door_No, Street_Name, City, and Zip_Code. The company wants to create a single column to store these values in the following format:
Which solution will meet this requirement with the LEAST coding effort?
Answer: D
Explanation:
The NEST TO MAP transformation allows you to combine multiple columns into a single column that contains a JSON object with key-value pairs. This is the easiest way to achieve the desired format for the physical address data, as you can simply select the columns to nest and specify the keys for each column. The NEST TO ARRAY transformation creates a single column that contains an array of values, which is not the same as the JSON object format. The PIVOT transformation reshapes the data by creating new columns from unique values in a selected column, which is not applicable for this use case. Writing a Lambda function in Python requires more coding effort than using AWS Glue DataBrew, which provides a visual and interactive interface for data transformations. References:
7 most common data preparation transformations in AWS Glue DataBrew (Section: Nesting and unnesting columns) NEST TO MAP - AWS Glue DataBrew (Section: Syntax)
NEW QUESTION # 75
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