Question 1

In an effort to optimize their online gaming platform's performance, a Cloud Data Engineer decides to analyze the application's debug logs. These logs are generated by various game servers and automatically streamed to CloudWatch Logs. The engineer needs a quick and efficient way to perform exploratory analysis on these logs to identify any recurrent patterns or anomalies indicative of performance issues.

Which AWS service or feature should the engineer leverage for this task?

Options :

A : Employ Amazon OpenSearch Service for real-time analysis of the logs directly from CloudWatch.

B : Utilize CloudWatch Logs Insights for executing queries against the log data and gaining immediate insights.

C : Use AWS Lambda to process and transfer the logs from CloudWatch to Amazon S3, and then analyze them using Amazon Athena.

D : Set up an Amazon EMR cluster with Hadoop to import logs from CloudWatch and conduct a detailed analysis.

Answer: B

Question 2

A company stores petabytes of data in thousands of Amazon S3 buckets in the S3 Standard storage class. The data supports analytics workloads that have unpredictable and variable data access patterns. The company does not access some data for months. However, the company must be able to retrieve all data within milliseconds. The company needs to optimize S3 storage costs. Which solution will meet these requirements with the LEAST operational overhead?

Options :

A :

Use S3 Storage Lens standard metrics to determine when to move objects to more cost-optimized storage classes. Create S3 Lifecycle policies for the S3 buckets to move objects to cost-optimized storage classes. Continue to refine the S3 Lifecycle policies in the future to optimize storage costs.

B :

Use S3 Storage Lens activity metrics to identify S3 buckets that the company accesses infrequently Configure S3 Lifecycle rules to move objects from S3 Standard to the S3 Standard-Infrequent Access (S3 Standard-IA) and S3 Glacier storage classes based on the age of the data.

C : Use S3 Intelligent-Tiering. Activate the Deep Archive Access tier.

D : Use S3 Intelligent-Tiering. Use the default access tier.

Answer: D

Question 3

A Data Engineering team is planning to perform sampling on a large dataset stored in Amazon S3 for exploratory data analysis. Their goal is to understand the overall trends without processing the entire dataset. The dataset is structured and spread across multiple S3 objects.

Which AWS service and method should the team use to implement an efficient and cost-effective sampling strategy?

Options :

A : Implement AWS Glue with a custom Python script to sequentially scan through each S3 object and randomly pick samples from each.

B : Leverage AWS Data Pipeline to orchestrate and move subsets of the data into Amazon RDS, where sampling can be done using SQL queries.

C : Use Amazon S3 Select to efficiently sample individual records from each S3 object, thus reducing the amount of data loaded and processed.

D : Configure Amazon Redshift Spectrum to load the full dataset into a Redshift cluster, and then use SQL queries to perform random sampling.

Answer: C

Question 4

A media company uses software as a service (SaaS) applications to gather data by using third-party tools. The

company needs to store the data in an Amazon S3 bucket. The company will use Amazon Redshift to perform

analytics based on the data.

Which AWS service or feature will meet these requirements with the LEAST operational overhead?

Options :

A : Amazon Managed Streaming for Apache Kafka (Amazon MSK)

B : Amazon AppFlow

C : AWS Glue Data Catalog

D : Amazon Kinesis

Answer: B

Question 5

As a data engineer, you are optimizing query performance for a client's data warehouse solution. The client is dealing with large datasets and is particularly focused on enhancing the performance of join queries while minimizing data shuffling between nodes.

Which distribution style within their data warehouse solution, specifically Amazon Redshift, would be best suited for this purpose?

Options :

A : Adopting the EVEN distribution style in Amazon Redshift, which distributes the data uniformly across all slices and nodes. This may not provide the most optimized method for join queries as it doesn't ensure data localization for join columns.

B : Implementing the AUTO distribution style in Amazon Redshift, allowing the service to automatically manage the distribution of data across nodes based on the table size and query patterns, potentially improving join query performance.

C : Selecting the ALL distribution style in Amazon Redshift, which duplicates the entire table across every node. While this can increase query speed for small tables, it can become inefficient for larger datasets due to high storage usage and performance implications.

D : Utilizing the KEY distribution style in Amazon Redshift by assigning it to a frequently joined column, thereby ensuring that data corresponding to the key is colocated on the same node, which can significantly reduce the amount of data shuffling required for join queries.

Answer: D

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