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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q80-Q85):

NEW QUESTION # 80
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. Upgrade the reserved node from ra3.4xlarqe to ra3.16xlarqe.
B. Change the primary key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement.
C. Change the sort key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement.
D. Change the distribution key to the table column that has the largest dimension.

Answer: D

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. Reference:
Choosing a data distribution style
Choosing a data sort key
Working with primary keys

NEW QUESTION # 81
A data engineer uses Amazon Redshift to run resource-intensive analytics processes once every month. Every month, the data engineer creates a new Redshift provisioned cluster. The data engineer deletes the Redshift provisioned cluster after the analytics processes are complete every month. Before the data engineer deletes the cluster each month, the data engineer unloads backup data from the cluster to an Amazon S3 bucket.
The data engineer needs a solution to run the monthly analytics processes that does not require the data engineer to manage the infrastructure manually.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use Amazon Redshift Serverless to automatically process the analytics workload.
B. Use Amazon Step Functions to pause the Redshift cluster when the analytics processes are complete and to resume the cluster to run new processes every month.
C. Use the AWS CLI to automatically process the analytics workload.
D. Use AWS CloudFormation templates to automatically process the analytics workload.

Answer: A

Explanation:
Amazon Redshift Serverless is a new feature of Amazon Redshift that enables you to run SQL queries on data in Amazon S3 without provisioning or managing any clusters. You can use Amazon Redshift Serverless to automatically process the analytics workload, as it scales up and down the compute resources based on the query demand, and charges you only for the resources consumed. This solution will meet the requirements with the least operational overhead, as it does not require the data engineer to create, delete, pause, or resume any Redshift clusters, or to manage any infrastructure manually. You can use the Amazon Redshift Data API to run queries from the AWS CLI, AWS SDK, or AWS Lambda functions12.
The other options are not optimal for the following reasons:
A: Use Amazon Step Functions to pause the Redshift cluster when the analytics processes are complete and to resume the cluster to run new processes every month. This option is not recommended, as it would still require the data engineer to create and delete a new Redshift provisioned cluster every month, which can incur additional costs and time. Moreover, this option would require the data engineer to use Amazon Step Functions to orchestrate the workflow of pausing and resuming the cluster, which can add complexity and overhead.
C: Use the AWS CLI to automatically process the analytics workload. This option is vague and does not specify how the AWS CLI is used to process the analytics workload. The AWS CLI can be used to run queries on data in Amazon S3 using Amazon Redshift Serverless, Amazon Athena, or Amazon EMR, but each of these services has different features and benefits. Moreover, this option does not address the requirement of not managing the infrastructure manually, as the data engineer may still need to provision and configure some resources, such as Amazon EMR clusters or Amazon Athena workgroups.
D: Use AWS CloudFormation templates to automatically process the analytics workload. This option is also vague and does not specify how AWS CloudFormation templates are used to process the analytics workload. AWS CloudFormation is a service that lets you model and provision AWS resources using templates. You can use AWS CloudFormation templates to create and delete a Redshift provisioned cluster every month, or to create and configure other AWS resources, such as Amazon EMR, Amazon Athena, or Amazon Redshift Serverless. However, this option does not address the requirement of not managing the infrastructure manually, as the data engineer may still need to write and maintain the AWS CloudFormation templates, and to monitor the status and performance of the resources.
References:
1: Amazon Redshift Serverless
2: Amazon Redshift Data API
3: Amazon Step Functions
4: AWS CLI
5: AWS CloudFormation

NEW QUESTION # 82
A data engineer needs to maintain a central metadata repository that users access through Amazon EMR and Amazon Athena queries. The repository needs to provide the schema and properties of many tables. Some of the metadata is stored in Apache Hive. The data engineer needs to import the metadata from Hive into the central metadata repository.
Which solution will meet these requirements with the LEAST development effort?

A. Use the AWS Glue Data Catalog.
B. Use Amazon EMR and Apache Ranger.
C. Use a Hive metastore on an EMR cluster.
D. Use a metastore on an Amazon RDS for MySQL DB instance.

Answer: A

Explanation:
The AWS Glue Data Catalog is an Apache Hive metastore-compatible catalog that provides a central metadata repository for various data sources and formats. You can use the AWS Glue Data Catalog as an external Hive metastore for Amazon EMR and Amazon Athena queries, and import metadata from existing Hive metastores into the Data Catalog. This solution requires the least development effort, as you can use AWS Glue crawlers to automatically discover and catalog the metadata from Hive, and use the AWS Glue console, AWS CLI, or Amazon EMR API to configure the Data Catalog as the Hive metastore. The other options are either more complex or require additional steps, such as setting up Apache Ranger for security, managing a Hive metastore on an EMR cluster or an RDS instance, or migrating the metadata manually.
References:
* Using the AWS Glue Data Catalog as the metastore for Hive (Section: Specifying AWS Glue Data Catalog as the metastore)
* Metadata Management: Hive Metastore vs AWS Glue (Section: AWS Glue Data Catalog)
* AWS Glue Data Catalog support for Spark SQL jobs (Section: Importing metadata from an existing Hive metastore)
* AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 5, page 131)

NEW QUESTION # 83
A retail company stores data from a product lifecycle management (PLM) application in an on-premises MySQL database. The PLM application frequently updates the database when transactions occur.
The company wants to gather insights from the PLM application in near real time. The company wants to integrate the insights with other business datasets and to analyze the combined dataset by using an Amazon Redshift data warehouse.
The company has already established an AWS Direct Connect connection between the on-premises infrastructure and AWS.
Which solution will meet these requirements with the LEAST development effort?

A. Run scheduled AWS DataSync tasks to synchronize data from the MySQL database. Set Amazon Redshift as the destination for the tasks.
B. Use the Amazon AppFlow SDK to build a custom connector for the MySQL database to continuously replicate the database changes. Set Amazon Redshift as the destination for the connector.
C. Run a full load plus CDC task in AWS Database Migration Service (AWS DMS) to continuously replicate the MySQL database changes. Set Amazon Redshift as the destination for the task.
D. Run a scheduled AWS Glue extract, transform, and load (ETL) job to get the MySQL database updates by using a Java Database Connectivity (JDBC) connection. Set Amazon Redshift as the destination for the ETL job.

Answer: C

Explanation:
Problem Analysis:
The company needs near real-time replication of MySQL updates to Amazon Redshift.
Minimal development effort is required for this solution.
Key Considerations:
AWS DMS provides a full load + CDC (Change Data Capture) mode for continuous replication of database changes.
DMS integrates natively with both MySQL and Redshift, simplifying setup.
Solution Analysis:
Option A: AWS Glue Job
Glue is batch-oriented and does not support near real-time replication.
Option B: DMS with Full Load + CDC
Efficiently handles initial database load and continuous updates.
Requires minimal setup and operational overhead.
Option C: AppFlow SDK
AppFlow is not designed for database replication. Custom connectors increase development effort.
Option D: DataSync
DataSync is for file synchronization and not suitable for database updates.
Final Recommendation:
Use AWS DMS in full load + CDC mode for continuous replication.
Reference:
AWS Database Migration Service Documentation
Setting Up DMS with Redshift

NEW QUESTION # 84
A company currently uses a provisioned Amazon EMR cluster that includes general purpose Amazon EC2 instances. The EMR cluster uses EMR managed scaling between one to five task nodes for the company's long-running Apache Spark extract, transform, and load (ETL) job. The company runs the ETL job every day.
When the company runs the ETL job, the EMR cluster quickly scales up to five nodes. The EMR cluster often reaches maximum CPU usage, but the memory usage remains under 30%.
The company wants to modify the EMR cluster configuration to reduce the EMR costs to run the daily ETL job.
Which solution will meet these requirements MOST cost-effectively?

A. Change the task node type from general purpose EC2 instances to memory optimized EC2 instances.
B. Reduce the scaling cooldown period for the provisioned EMR cluster.
C. Increase the maximum number of task nodes for EMR managed scaling to 10.
D. Switch the task node type from general purpose EC2 instances to compute optimized EC2 instances.

Answer: D

Explanation:
The company's Apache Spark ETL job on Amazon EMR uses high CPU but low memory, meaning that compute-optimized EC2 instances would be the most cost-effective choice. These instances are designed for high-performance compute applications, where CPU usage is high, but memory needs are minimal, which is exactly the case here.
* Compute Optimized Instances:
* Compute-optimized instances, such as the C5 series, provide a higher ratio of CPU to memory, which is more suitable for jobs with high CPU usage and relatively low memory consumption.
* Switching from general-purpose EC2 instances to compute-optimized instances can reduce costs while improving performance, as these instances are optimized for workloads like Spark jobs that perform a lot of computation.

NEW QUESTION # 85
......

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