NEW QUESTION 1
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?

• A. AWS Glue
• B. Amazon EMR
• C. AWS Lambda
• D. Amazon Redshift

Answer: A

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. References:
✑ 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 2
A data engineer must manage the ingestion of real-time streaming data into AWS. The data engineer wants to perform real-time analytics on the incoming streaming data by using time-based aggregations over a window of up to 30 minutes. The data engineer needs a solution that is highly fault tolerant.
Which solution will meet these requirements with the LEAST operational overhead?

• A. Use an AWS Lambda function that includes both the business and the analytics logic to perform time-based aggregations over a window of up to 30 minutes for the data in Amazon Kinesis Data Streams.
• B. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to analyze the data that might occasionally contain duplicates by using multiple types of aggregations.
• C. Use an AWS Lambda function that includes both the business and the analytics logic to perform aggregations for a tumbling window of up to 30 minutes, based on the event timestamp.
• D. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to analyze the data by using multiple types of aggregations to perform time- based analytics over a window of up to 30 minutes.

Answer: A

Explanation:
This solution meets the requirements of managing the ingestion of real-time streaming data into AWS and performing real-time analytics on the incoming streaming data with the least operational overhead. Amazon Managed Service for Apache Flink is a fully managed service that allows you to run Apache Flink applications without having to manage any infrastructure or clusters. Apache Flink is a framework for stateful stream processing that supports various types of aggregations, such as tumbling, sliding, and session windows, over streaming data. By using Amazon Managed Service for Apache Flink, you can easily connect to Amazon Kinesis Data Streams as the source and sink of your streaming data, and perform time-based analytics over a window of up to 30 minutes. This solution is also highly fault tolerant, as Amazon Managed Service for Apache Flink automatically scales, monitors, and restarts your Flink applications in case of failures. References:
✑ Amazon Managed Service for Apache Flink
✑ Apache Flink
✑ Window Aggregations in Flink

NEW QUESTION 3
A company stores details about transactions in an Amazon S3 bucket. The company wants to log all writes to the S3 bucket into another S3 bucket that is in the same AWS Region.
Which solution will meet this requirement with the LEAST operational effort?

• A. Configure an S3 Event Notifications rule for all activities on the transactions S3 bucket to invoke an AWS Lambda functio
• B. Program the Lambda function to write the event to Amazon Kinesis Data Firehos
• C. Configure Kinesis Data Firehose to write the event to the logs S3 bucket.
• D. Create a trail of management events in AWS CloudTrai
• E. Configure the trail to receive data from the transactions S3 bucke
• F. Specify an empty prefix and write-only event
• G. Specify the logs S3 bucket as the destination bucket.
• H. Configure an S3 Event Notifications rule for all activities on the transactions S3 bucket to invoke an AWS Lambda functio
• I. Program the Lambda function to write the events to the logs S3 bucket.
• J. Create a trail of data events in AWS CloudTrai
• K. Configure the trail to receive data from the transactions S3 bucke
• L. Specify an empty prefix and write-only event
• M. Specify the logs S3 bucket as the destination bucket.

Answer: D

Explanation:
This solution meets the requirement of logging all writes to the S3 bucket into another S3 bucket with the least operational effort. AWS CloudTrail is a service that records the API calls made to AWS services, including Amazon S3. By creating a trail of data events, you can capture the details of the requests that are made to the transactions S3 bucket, such as the requester, the time, the IP address, and the response elements. By specifying an empty prefix and write-only events, you can filter the data events to only include the ones that write to the bucket. By specifying the logs S3 bucket as the destination bucket, you can store the CloudTrail logs in another S3 bucket that is in the same AWS Region. This solution does not require any additional coding or configuration, and it is more scalable and reliable than using S3 Event Notifications and Lambda functions. References:
✑ Logging Amazon S3 API calls using AWS CloudTrail
✑ Creating a trail for data events
✑ Enabling Amazon S3 server access logging

NEW QUESTION 4
A company stores datasets in JSON format and .csv format in an Amazon S3 bucket. The company has Amazon RDS for Microsoft SQL Server databases, Amazon DynamoDB tables that are in provisionedcapacity mode, and an Amazon Redshift cluster. A data engineering team must develop a solution that will give data scientists the ability to query all data sources by using syntax similar to SQL.
Which solution will meet these requirements with the LEAST operational overhead?

• A. Use AWS Glue to crawl the data source
• B. Store metadata in the AWS Glue Data Catalo
• C. Use Amazon Athena to query the dat
• D. Use SQL for structured data source
• E. Use PartiQL for data that is stored in JSON format.
• F. Use AWS Glue to crawl the data source
• G. Store metadata in the AWS Glue Data Catalo
• H. Use Redshift Spectrum to query the dat
• I. Use SQL for structured data source
• J. Use PartiQL for data that is stored in JSON format.
• K. Use AWS Glue to crawl the data source
• L. Store metadata in the AWS Glue Data Catalo
• M. Use AWS Glue jobs to transform data that is in JSON format to Apache Parquet or .csv forma
• N. Store the transformed data in an S3 bucke
• O. Use Amazon Athena to query the original and transformed data from the S3 bucket.
• P. Use AWS Lake Formation to create a data lak
• Q. Use Lake Formation jobs to transform the data from all data sources to Apache Parquet forma
• R. Store the transformed data in an S3 bucke
• S. Use Amazon Athena or Redshift Spectrum to query the data.

Answer: A

Explanation:
The best solution to meet the requirements of giving data scientists the ability to query all data sources by using syntax similar to SQL with the least operational overhead is to use AWS Glue to crawl the data sources, store metadata in the AWS Glue Data Catalog, use Amazon Athena to query the data, use SQL for structured data sources, and use PartiQL for data that is stored in JSON format.
AWS Glue is a serverless data integration service that makes it easy to prepare, clean, enrich, and move data between data stores1. AWS Glue crawlers are processes that connect to a data store, progress through a prioritized list of classifiers to determine the schema for your data, and then create metadata tables in the Data Catalog2. The Data Catalog is a persistent metadata store that contains table definitions, job definitions, and other control information to help you manage your AWS Glue components3. You can use AWS Glue to crawl the data sources, such as Amazon S3, Amazon RDS for Microsoft SQL Server, and Amazon DynamoDB, and store the metadata in the Data Catalog.
Amazon Athena is a serverless, interactive query service that makes it easy to analyze data directly in Amazon S3 using standard SQL or Python4. Amazon Athena also supports PartiQL, a SQL-compatible query language that lets you query, insert, update, and delete data from semi-structured and nested data, such as JSON. You can use Amazon Athena to query the data from the Data Catalog using SQL for structured data sources, such as .csv files and relational databases, and PartiQL for data that is stored in JSON format. You can also use Athena to query data from other data sources, such as Amazon Redshift, using federated queries.
Using AWS Glue and Amazon Athena to query all data sources by using syntax similar to SQL is the least operational overhead solution, as you do not need to provision, manage, or scale any infrastructure, and you pay only for the resources you use. AWS Glue charges you based on the compute time and the data processed by your crawlers and ETL jobs1. Amazon Athena charges you based on the amount of data scanned by your queries. You can also reduce the cost and improve the performance of your queries by using compression, partitioning, and columnar formats for your data in Amazon S3.
Option B is not the best solution, as using AWS Glue to crawl the data sources, store metadata in the AWS Glue Data Catalog, and use Redshift Spectrum to query the data, would incur more costs and complexity than using Amazon Athena. Redshift Spectrum is a feature of Amazon Redshift, a fully managed data warehouse service, that allows you to query and join data across your data warehouse and your data lake using standard SQL. While Redshift Spectrum is powerful and useful for many data warehousing scenarios, it is not necessary or cost-effective for querying all data sources by using syntax similar to SQL. Redshift Spectrum charges you based on the amount of data scanned by your queries, which is similar to Amazon Athena, but it also requires you to have an Amazon Redshift cluster, which charges you based on the node type, the number of nodes, and the duration of the cluster5. These costs can add up quickly, especially if you have large volumes of data and complex queries. Moreover, using Redshift Spectrum would introduce additional latency and complexity, as you would have to provision and manage the cluster, and create an external schema and database for the data in the Data Catalog, instead of querying it directly from Amazon Athena.
Option C is not the best solution, as using AWS Glue to crawl the data sources, store metadata in the AWS Glue Data Catalog, use AWS Glue jobs to transform data that is in JSON format to Apache Parquet or .csv format, store the transformed data in an S3 bucket, and use Amazon Athena to query the original and transformed data from the S3 bucket, would incur more costs and complexity than using Amazon Athena with PartiQL. AWS Glue jobs are ETL scripts that you can write in Python or Scala to transform your data and load it to your target data store. 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 schemes6. While using AWS Glue jobs and Parquet can improve the performance and reduce the cost of your queries, they would also increase the complexity and the operational overhead of the data pipeline, as you would have to write, run, and monitor the ETL jobs, and store the transformed data in a separate location in Amazon S3. Moreover, using AWS Glue jobs and Parquet would introduce additional latency, as you would have to wait for the ETL jobs to finish before querying the transformed data.
Option D is not the best solution, as using AWS Lake Formation to create a data lake, use Lake Formation jobs to transform the data from all data sources to Apache Parquet format, store the transformed data in an S3 bucket, and use Amazon Athena or RedshiftSpectrum to query the data, would incur more costs and complexity than using Amazon Athena with PartiQL. AWS Lake Formation is a service that helps you centrally govern, secure, and globally share data for analytics and machine learning7. Lake Formation jobs are ETL jobs that you can create and run using the Lake Formation console or API. While using Lake Formation and Parquet can improve the performance and reduce the cost of your queries, they would also increase the complexity and the operational overhead of the data pipeline, as you would have to create, run, and monitor the Lake Formation jobs, and store the transformed data in a separate location in Amazon S3. Moreover, using Lake Formation and Parquet would introduce additional latency, as you would have to wait for the Lake Formation jobs to finish before querying the transformed data. Furthermore, using Redshift Spectrum to query the data would also incur the same costs and complexity as mentioned in option B. References:
✑ What is Amazon Athena?
✑ Data Catalog and crawlers in AWS Glue
✑ AWS Glue Data Catalog
✑ Columnar Storage Formats
✑ AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
✑ AWS Glue Schema Registry
✑ What is AWS Glue?
✑ Amazon Redshift Serverless
✑ Amazon Redshift provisioned clusters
✑ [Querying external data using Amazon Redshift Spectrum]
✑ [Using stored procedures in Amazon Redshift]
✑ [What is AWS Lambda?]
✑ [PartiQL for Amazon Athena]
✑ [Federated queries in Amazon Athena]
✑ [Amazon Athena pricing]
✑ [Top 10 performance tuning tips for Amazon Athena]
✑ [AWS Glue ETL jobs]
✑ [AWS Lake Formation jobs]

NEW QUESTION 5
A company uses Amazon RDS to store transactional data. The company runs an RDS DB instance in a private subnet. A developer wrote an AWS Lambda function with default settings to insert, update, or delete data in the DB instance.
The developer needs to give the Lambda function the ability to connect to the DB instance privately without using the public internet.
Which combination of steps will meet this requirement with the LEAST operational overhead? (Choose two.)

• A. Turn on the public access setting for the DB instance.
• B. Update the security group of the DB instance to allow only Lambda function invocations on the database port.
• C. Configure the Lambda function to run in the same subnet that the DB instance uses.
• D. Attach the same security group to the Lambda function and the DB instanc
• E. Include a self-referencing rule that allows access through the database port.
• F. Update the network ACL of the private subnet to include a self-referencing rule that allows access through the database port.

Answer: CD

Explanation:
To enable the Lambda function to connect to the RDS DB instance privately without using the public internet, the best combination of steps is to configure the Lambda function to run in the same subnet that the DB instance uses, and attach the same security group to the Lambda function and the DB instance. This way, the Lambda function and the DB instance can communicate within the same private network, and the security group can allow traffic between them on the database port. This solution has the least operational overhead, as it does not require any changes to the public access setting, the network ACL, or the security group of the DB instance.
The other options are not optimal for the following reasons:
✑ A. Turn on the public access setting for the DB instance. This option is not recommended, as it would expose the DB instance to the public internet, which can compromise the security and privacy of the data. Moreover, this option would not enable the Lambda function to connect to the DB instance privately, as it would still require the Lambda function to use the public internet to access the DB instance.
✑ B. Update the security group of the DB instance to allow only Lambda function invocations on the database port. This option is not sufficient, as it would only modify the inbound rules of the security group of the DB instance, but not the outbound rules of the security group of the Lambda function. Moreover, this option would not enable the Lambda function to connect to the DB instance privately, as it would still require the Lambda function to use the public internet to access the DB instance.
✑ E. Update the network ACL of the private subnet to include a self-referencing rule
that allows access through the database port. This option is not necessary, as the network ACL of the private subnet already allows all traffic within the subnet by default. Moreover, this option would not enable the Lambda function to connect to the DB instance privately, as it would still require the Lambda function to use the public internet to access the DB instance.
References:
✑ 1: Connecting to an Amazon RDS DB instance
✑ 2: Configuring a Lambda function to access resources in a VPC
✑ 3: Working with security groups
✑ : Network ACLs

NEW QUESTION 6
A company uses an Amazon Redshift provisioned cluster as its database. The Redshift cluster has five reserved ra3.4xlarge nodes and uses key distribution.
A data engineer notices that one of the nodes frequently has a CPU load over 90%. SQL Queries that run on the node are queued. The other four nodes usually have a CPU load under 15% during daily operations.
The data engineer wants to maintain the current number of compute nodes. The data engineer also wants to balance the load more evenly across all five compute nodes.
Which solution will meet these requirements?

• A. Change the sort key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement.
• B. Change the distribution key to the table column that has the largest dimension.
• C. Upgrade the reserved node from ra3.4xlarqe to ra3.16xlarqe.
• D. Change the primary key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement.

Answer: B

Explanation:
Changing the distribution key to the table column that has the largest dimension will help to balance the load more evenly across all five compute nodes. The distribution key determines how the rows of a table are distributed among the slices of the cluster. If the distribution key is not chosen wisely, it can cause data skew, meaning some slices will have more data than others, resulting in uneven CPU load and query performance. By choosing the table column that has the largest dimension, meaning the column that has the most distinct values, as the distribution key, the data engineer can ensure that the rows are distributed more uniformly across the slices, reducing data skew and improving query performance.
The other options are not solutions that will meet the requirements. Option A, changing the sort key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement, will not affect the data distribution or the CPU load. The sort key determines the order in which the rows of a table are stored on disk, which can improve the performance of range-restricted queries, but not the load balancing. Option C, upgrading the reserved node from ra3.4xlarge to ra3.16xlarge, will not maintain the current number of compute nodes, as it will increase the cost and the capacity of the cluster. Option D, changing the primary key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement, will not affect the data distribution or the CPU load either. The primary key is a constraint that enforces the uniqueness of the rows in a table, but it does not influence the data layout or the query optimization. References:
✑ Choosing a data distribution style
✑ Choosing a data sort key
✑ Working with primary keys

NEW QUESTION 7
A data engineer is configuring Amazon SageMaker Studio to use AWS Glue interactive sessions to prepare data for machine learning (ML) models.
The data engineer receives an access denied error when the data engineer tries to prepare the data by using SageMaker Studio.
Which change should the engineer make to gain access to SageMaker Studio?

• A. Add the AWSGlueServiceRole managed policy to the data engineer's IAM user.
• B. Add a policy to the data engineer's IAM user that includes the sts:AssumeRole action for the AWS Glue and SageMaker service principals in the trust policy.
• C. Add the AmazonSageMakerFullAccess managed policy to the data engineer's IAM user.
• D. Add a policy to the data engineer's IAM user that allows the sts:AddAssociation action for the AWS Glue and SageMaker service principals in the trust policy.

Answer: B

Explanation:
This solution meets the requirement of gaining access to SageMaker Studio to use AWS Glue interactive sessions. AWS Glue interactive sessions are a way to use AWS Glue DataBrew and AWS Glue Data Catalog from within SageMaker Studio. To use AWS Glue interactive sessions, the data engineer’s IAM user needs to have permissions to assume the AWS Glue service role and the SageMaker execution role. By adding a policy to the data engineer’s IAM user that includes the sts:AssumeRole action for the AWS Glue and SageMaker service principals in the trust policy, the data engineer can grant these permissions and avoid the access denied error. The other options are not sufficient or necessary to resolve the error. References:
✑ Get started with data integration from Amazon S3 to Amazon Redshift using AWS Glue interactive sessions
✑ Troubleshoot Errors - Amazon SageMaker
✑ AccessDeniedException on sagemaker:CreateDomain in AWS SageMaker Studio, despite having SageMakerFullAccess

NEW QUESTION 8
An airline company is collecting metrics about flight activities for analytics. The company is conducting a proof of concept (POC) test to show how analytics can provide insights that the company can use to increase on-time departures.
The POC test uses objects in Amazon S3 that contain the metrics in .csv format. The POC test uses Amazon Athena to query the data. The data is partitioned in the S3 bucket by date.
As the amount of data increases, the company wants to optimize the storage solution to improve query performance.
Which combination of solutions will meet these requirements? (Choose two.)

• A. Add a randomized string to the beginning of the keys in Amazon S3 to get more throughput across partitions.
• B. Use an S3 bucket that is in the same account that uses Athena to query the data.
• C. Use an S3 bucket that is in the same AWS Region where the company runs Athena queries.
• D. Preprocess the .csvdata to JSON format by fetchingonly the document keys that the query requires.
• E. Preprocess the .csv data to Apache Parquet format by fetching only the data blocks that are needed for predicates.

Answer: CE

Explanation:
Using an S3 bucket that is in the same AWS Region where the company runs Athena queries can improve query performance by reducing data transfer latency and costs. Preprocessing the .csv data to Apache Parquet format can also improve query performance by enabling columnar storage, compression, and partitioning, which can reduce the amount of data scanned and fetched by the query. These solutions can optimize the storage solution for the POC test without requiring much effort or changes to the existing data pipeline. The other solutions are not optimal or relevant for this requirement. Adding a randomized string to the beginning of the keys in Amazon S3 can improve the throughput across partitions, but it can also make the data harder to query and manage. Using an S3 bucket that is in the same account that uses Athena to query the data does not have any significant impact on query performance, as long as the proper permissions are granted. Preprocessing the .csv data to JSON format does not offer any benefits over the .csv format, as both are row-based and verbose formats that require more data scanning and fetching than columnar formats like Parquet. References:
✑ Best Practices When Using Athena with AWS Glue
✑ Optimizing Amazon S3 Performance
✑ AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

NEW QUESTION 9
A company uses Amazon S3 to store semi-structured data in a transactional data lake. Some of the data files are small, but other data files are tens of terabytes.
A data engineer must perform a change data capture (CDC) operation to identify changed data from the data source. The data source sends a full snapshot as a JSON file every day and ingests the changed data into the data lake.
Which solution will capture the changed data MOST cost-effectively?

• A. Create an AWS Lambda function to identify the changes between the previous data and the current dat
• B. Configure the Lambda function to ingest the changes into the data lake.
• C. Ingest the data into Amazon RDS for MySQ
• D. Use AWS Database Migration Service (AWS DMS) to write the changed data to the data lake.
• E. Use an open source data lake format to merge the data source with the S3 data lake to insert the new data and update the existing data.
• F. Ingest the data into an Amazon Aurora MySQL DB instance that runs Aurora Serverles
• G. Use AWS Database Migration Service (AWS DMS) to write the changed data to the data lake.

Answer: C

Explanation:
An open source data lake format, such as Apache Parquet, Apache ORC, or Delta Lake, is a cost-effective way to perform a change data capture (CDC) operation on semi-structured data stored in Amazon S3. An open source data lake format allows you to query data directly from S3 using standard SQL, without the need to move or copy data to another service. An open source data lake format also supports schema evolution, meaning it can handle changes in the data structure over time. An open source data lake format also supports upserts, meaning it can insert new data and update existing data in the same operation, using a merge command. This way, you can efficiently capture the changes from the data source and apply them to the S3 data lake, without duplicating or losing any data. The other options are not as cost-effective as using an open source data lake format, as they involve additional steps or costs. Option A requires you to create and maintain an AWS Lambda function, which can be complex and error-prone. AWS Lambda also has some limits on the execution time, memory, and concurrency, which can affect the performance and reliability of the CDC operation. Option B and D require you to ingest the data into a relational database service, such as Amazon RDS or Amazon Aurora, which can be expensive and unnecessary for semi-structured data. AWS Database Migration Service (AWS DMS) can write the changed data to the data lake, but it alsocharges you for the data replication and transfer. Additionally, AWS DMS does not support JSON as a source data type, so you would need to convert the data to a supported format before using AWS DMS. References:
✑ What is a data lake?
✑ Choosing a data format for your data lake
✑ Using the MERGE INTO command in Delta Lake
✑ [AWS Lambda quotas]
✑ [AWS Database Migration Service quotas]

NEW QUESTION 10
A company uses Amazon Athena for one-time queries against data that is in Amazon S3. The company has several use cases. The company must implement permission controls to separate query processes and access to query history among users, teams, and applications that are in the same AWS account.
Which solution will meet these requirements?

• A. Create an S3 bucket for each use cas
• B. Create an S3 bucket policy that grants permissions to appropriate individual IAM user
• C. Apply the S3 bucket policy to the S3 bucket.
• D. Create an Athena workgroup for each use cas
• E. Apply tags to the workgrou
• F. Create an 1AM policy that uses the tags to apply appropriate permissions to the workgroup.
• G. Create an JAM role for each use cas
• H. Assign appropriate permissions to the role for each use cas
• I. Associate the role with Athena.
• J. Create an AWS Glue Data Catalog resource policy that grants permissions to appropriate individual IAM users for each use cas
• K. Apply the resource policy to the specific tables that Athena uses.

Answer: B

Explanation:
Athena workgroups are a way to isolate query execution and query history among users, teams, and applications that share the same AWS account. By creating a workgroup for each use case, the company can control the access and actions on the workgroup resource using resource-level IAM permissions or identity-based IAM policies. The company can also use tags to organize and identify the workgroups, and use them as conditions in the IAM policies to grant or deny permissions to the workgroup. This solution meets the requirements of separating query processes and access to query history among users, teams, and applications that are in the same AWS account. References:
✑ Athena Workgroups
✑ IAM policies for accessing workgroups
✑ Workgroup example policies

NEW QUESTION 11
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.)

• A. Use gzip compression to compress individual files to sizes that are between 1 GB and 5 GB.
• B. Use a columnar storage file format.
• C. Partition the data based on the most common query predicates.
• D. Split the data into files that are less than 10 KB.
• E. Use file formats that are not

Answer: BC

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 12
A company uses an Amazon Redshift cluster that runs on RA3 nodes. The company wants to scale read and write capacity to meet demand. A data engineer needs to identify a solution that will turn on concurrency scaling.
Which solution will meet this requirement?

• A. Turn on concurrency scaling in workload management (WLM) for Redshift Serverless workgroups.
• B. Turn on concurrency scaling at the workload management (WLM) queue level in the Redshift cluster.
• C. Turn on concurrency scaling in the settings duringthe creation of andnew Redshift cluster.
• D. Turn on concurrency scaling for the daily usage quota for the Redshift cluster.

Answer: B

Explanation:
Concurrency scaling is a feature that allows you to support thousands of concurrent users and queries, with consistently fast query performance. When you turn on concurrency scaling, Amazon Redshift automatically adds query processing power in seconds to process queries without any delays. You can manage which queries are sent to the concurrency-scaling cluster by configuring WLM queues. To turn on concurrency scaling for a queue, set the Concurrency Scaling mode value to auto. The other options are either incorrect or irrelevant, as they do not enable concurrency scaling for the existing Redshift cluster on RA3 nodes. References:
✑ Working with concurrency scaling - Amazon Redshift
✑ Amazon Redshift Concurrency Scaling - Amazon Web Services
✑ Configuring concurrency scaling queues - Amazon Redshift
✑ AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 6, page 163)

NEW QUESTION 13
A company is planning to use a provisioned Amazon EMR cluster that runs Apache Spark jobs to perform big data analysis. The company requires high reliability. A big data team must follow best practices for running cost-optimized and long-running workloads on Amazon EMR. The team must find a solution that will maintain the company's current level of performance.
Which combination of resources will meet these requirements MOST cost-effectively? (Choose two.)

• A. Use Hadoop Distributed File System (HDFS) as a persistent data store.
• B. Use Amazon S3 as a persistent data store.
• C. Use x86-based instances for core nodes and task nodes.
• D. Use Graviton instances for core nodes and task nodes.
• E. Use Spot Instances for all primary nodes.

Answer: BD

Explanation:
The best combination of resources to meet the requirements of high reliability, cost-optimization, and performance for running Apache Spark jobs on Amazon EMR is to use Amazon S3 as a persistent data store and Graviton instances for core nodes and task nodes.
Amazon S3 is a highly durable, scalable, and secure object storage service that can store any amount of data for a variety of use cases, including big data analytics1. Amazon S3 is a better choice than HDFS as a persistent data store for Amazon EMR, as it decouples the storage from the compute layer, allowing for more flexibility and cost-efficiency. Amazon S3 also supports data encryption, versioning, lifecycle management, and cross-region replication1. Amazon EMR integrates seamlessly with Amazon S3, using EMR File System (EMRFS) to access data stored in Amazon S3 buckets2. EMRFS also supports consistent view, which enables Amazon EMR to provide read-after-write consistency for Amazon S3 objects that are accessed through EMRFS2.
Graviton instances are powered by Arm-based AWS Graviton2 processors that deliver up to 40% better price performance over comparable current generation x86-based instances3. Graviton instances are ideal for running workloads that are CPU-bound, memory-bound, or network-bound, such as big data analytics, web servers, and open- source databases3. Graviton instances are compatible with Amazon EMR, and can beused for both core nodes and task nodes. Core nodes are responsible for running the data processing frameworks, such as Apache Spark, and storing data in HDFS or the local file system. Task nodes are optional nodes that can be added to a cluster to increase the processing power and throughput. By using Graviton instances for both core nodes and task nodes, you can achieve higher performance and lower cost than using x86-based instances.
Using Spot Instances for all primary nodes is not a good option, as it can compromise the reliability and availability of the cluster. Spot Instances are spare EC2 instances that are available at up to 90% discount compared to On-Demand prices, but they can be interrupted by EC2 with a two-minute notice when EC2 needs the capacity back. Primary nodes are the nodes that run the cluster software, such as Hadoop, Spark, Hive, and Hue, and are essential for the cluster operation. If a primary node is interrupted by EC2, the cluster will fail or become unstable. Therefore, it is recommended to use On-Demand Instances or Reserved Instances for primary nodes, and use Spot Instances only for task nodes that can tolerate interruptions. References:
✑ Amazon S3 - Cloud Object Storage
✑ EMR File System (EMRFS)
✑ AWS Graviton2 Processor-Powered Amazon EC2 Instances
✑ [Plan and Configure EC2 Instances]
✑ [Amazon EC2 Spot Instances]
✑ [Best Practices for Amazon EMR]

NEW QUESTION 14
A financial company wants to implement a data mesh. The data mesh must support centralized data governance, data analysis, and data access control. The company has decided to use AWS Glue for data catalogs and extract, transform, and load (ETL) operations.
Which combination of AWS services will implement a data mesh? (Choose two.)

• A. Use Amazon Aurora for data storag
• B. Use an Amazon Redshift provisioned cluster for data analysis.
• C. Use Amazon S3 for data storag
• D. Use Amazon Athena for data analysis.
• E. Use AWS Glue DataBrewfor centralized data governance and access control.
• F. Use Amazon RDS for data storag
• G. Use Amazon EMR for data analysis.
• H. Use AWS Lake Formation for centralized data governance and access control.

Answer: BE

Explanation:
A data mesh is an architectural framework that organizes data into domains and treats data as products that are owned and offered for consumption by different teams1. A data mesh requires a centralized layer for data governance and access control, as well as a distributed layer for data storage and analysis. AWS Glue can provide data catalogs and ETL operations for the data mesh, but it cannot provide data governance and access control by itself2. Therefore, the company needs to use another AWS service for this purpose. AWS Lake Formation is a service that allows you to create, secure, and manage data lakes on AWS3. It integrates with AWS Glue and other AWS services to provide centralized data governance and access control for the data mesh. Therefore, option E is correct.
For data storage and analysis, the company can choose from different AWS services depending on their needs and preferences. However, one of the benefits of a data mesh is that it enables data to be stored and processed in a decoupled and scalable way1. Therefore, using serverless or managed services that can handle large volumes and varieties of data is preferable. Amazon S3 is a highly scalable, durable, and secure object storage service that can store any type of data. Amazon Athena is a serverless interactive query service that can analyze data in Amazon S3 using standard SQL. Therefore, option B is a good choice for data storage and analysis in a data mesh. Option A, C, and D are not optimal because they either use relational databases that are not suitable for storing diverse and unstructured data, or they require more management and provisioning than serverless services. References:
✑ 1: What is a Data Mesh? - Data Mesh Architecture Explained - AWS
✑ 2: AWS Glue - Developer Guide
✑ 3: AWS Lake Formation - Features
✑ [4]: Design a data mesh architecture using AWS Lake Formation and AWS Glue
✑ [5]: Amazon S3 - Features
✑ [6]: Amazon Athena - Features

NEW QUESTION 15
A company needs to partition the Amazon S3 storage that the company uses for a data lake. The partitioning will use a path of the S3 object keys in the following format: s3://bucket/prefix/year=2023/month=01/day=01.
A data engineer must ensure that the AWS Glue Data Catalog synchronizes with the S3 storage when the company adds new partitions to the bucket.
Which solution will meet these requirements with the LEAST latency?

• A. Schedule an AWS Glue crawler to run every morning.
• B. Manually run the AWS Glue CreatePartition API twice each day.
• C. Use code that writes data to Amazon S3 to invoke the Boto3 AWS Glue create partition API call.
• D. Run the MSCK REPAIR TABLE command from the AWS Glue console.

Answer: C

Explanation:
The best solution to ensure that the AWS Glue Data Catalog synchronizes with the S3 storage when the company adds new partitions to the bucket with the least latency is to use code that writes data to Amazon S3 to invoke the Boto3 AWS Glue create partition API call. This way, the Data Catalog is updated as soon as new data is written to S3, and the partition information is immediately available for querying by other
services. The Boto3 AWS Glue create partition API call allows you to create a new partition in the Data Catalog by specifying the table name, the database name, and the partition values1. You can use this API call in your code that writes data to S3, such as a Python script or an AWS Glue ETL job, to create a partition for each new S3 object key that matches the partitioning scheme.
Option A is not the best solution, as scheduling an AWS Glue crawler to run every morning would introduce a significant latency between the time new data is written to S3 and the time the Data Catalog is updated. AWS Glue crawlers are processes that connect to a data store, progress through a prioritized list of classifiers to determine the schema for your data, and then create metadata tables in the Data Catalog2. Crawlers can be scheduled to run periodically, such as daily or hourly, but they cannot runcontinuously or in real-time. Therefore, using a crawler to synchronize the Data Catalog with the S3 storage would not meet the requirement of the least latency.
Option B is not the best solution, as manually running the AWS Glue CreatePartition API twice each day would also introduce a significant latency between the time new data is written to S3 and the time the Data Catalog is updated. Moreover, manually running the API would require more operational overhead and human intervention than using code that writes data to S3 to invoke the API automatically.
Option D is not the best solution, as running the MSCK REPAIR TABLE command from the AWS Glue console would also introduce a significant latency between the time new data is written to S3 and the time the Data Catalog is updated. The MSCK REPAIR TABLE command is a SQL command that you can run in the AWS Glue console to add partitions to the Data Catalog based on the S3 object keys that match the partitioning scheme3. However, this command is not meant to be run frequently or in real-time, as it can take a long time to scan the entire S3 bucket and add the partitions. Therefore, using this command to synchronize the Data Catalog with the S3 storage would not meet the requirement of the least latency. References:
✑ AWS Glue CreatePartition API
✑ Populating the AWS Glue Data Catalog
✑ MSCK REPAIR TABLE Command
✑ AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

NEW QUESTION 16
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