Question 1

As a data engineering consultant, you've been approached by a client who's experiencing difficulties with their Amazon DynamoDB setup. They've noticed that their query performance degrades as their dataset grows, despite having a primary key design that they believed would be optimal. They ask for your advice on how they can maintain consistent, fast query performance as their dataset scales.

Which of the following suggestions would be the most effective for maintaining query performance in DynamoDB at scale?

Options :

A : Suggest optimizing the table-s primary key by considering composite keys, which combine partition keys and sort keys to form a unique identifier for each item.

B : Advise on sharding the data by creating multiple tables with the same schema based on access patterns, thus distributing the load more evenly across the tables.

C : Recommend implementing DynamoDB Accelerator (DAX) to provide a fully managed, highly available, in-memory cache, particularly for read-intensive application workloads.

D : Propose the use of DynamoDB Streams to capture table activity and automate the redistribution of items to different partitions based on their access patterns using AWS Lambda functions.

Answer: C

Question 2

A data engineer has a one-time task to read data from objects that are in Apache Parquet format in an Amazon S3 bucket. The data engineer needs to query only one column of the data. Which solution will meet these requirements with the LEAST operational overhead?

Options :

A :

Confiqure an AWS Lambda function to load data from the S3 bucket into a pandas dataframe- Write a SQL SELECT statement on the dataframe to query the required column.

B : Use S3 Select to write a SQL SELECT statement to retrieve the required column from the S3 objects.

C : Prepare an AWS Glue DataBrew project to consume the S3 objects and to query the required column.

D :

Run an AWS Glue crawler on the S3 objects. Use a SQL SELECT statement in Amazon Athena to query the required column.

Answer: B,D

Question 3

A media company uploads new video content to an Amazon S3 bucket. They require an automated process to transcode these videos immediately after upload. The transcoded videos should then be saved to a different S3 bucket.

What AWS service combination should the company use to automate the transcoding process immediately after video files are uploaded to S3?

Options :

A : Configure S3 Event Notifications to trigger an AWS Lambda function for video transcoding, and save the output to a different S3 bucket.

B : Implement Amazon CloudWatch Events to monitor the S3 bucket and trigger Amazon Elastic Transcoder jobs for new uploads.

C : Set up AWS Data Pipeline to detect new files in S3 and initiate Amazon Elastic Transcoder jobs for each video, storing results in another bucket.

D : Use Amazon S3 Lifecycle policies to move the uploaded videos to Amazon Elastic Transcoder for processing and output to another S3 bucket.

Answer: A

Question 4

A medium-sized enterprise with fluctuating data analytics demands is exploring Amazon Redshift options. They are looking for a configuration that minimizes the need for active resource management, especially during unpredictable periods of high query volume. The ideal solution should adapt to changing demands while maintaining operational efficiency.

Which Redshift configuration should they consider to meet these requirements of adaptability and ease of management?

Options :

A : Choose Redshift Serverless, as it provides the flexibility to automatically adapt to changing query volumes without manual scaling.

B : Opt for Redshift with RA3 nodes, which offer scalable storage and compute capabilities, and configure auto-scaling policies to handle variable query loads.

C : Select Redshift with On-Demand pricing and set up scaling triggers based on performance metrics to manage varying loads efficiently.

D : Implement a traditional Redshift cluster with dense compute nodes, using dynamic resizing features for adapting to fluctuating workloads.

Answer: A

Question 5

In a collaborative project involving several departments, a Cloud Data Engineering team has been tasked with setting up a data lake in AWS. The data lake stores sensitive customer data in an Amazon S3 bucket within the primary AWS account (Account A). However, a partner company needs access to certain datasets for analysis, which will be performed in their own AWS account (Account B). For compliance purposes, it's vital to ensure that data is encrypted in transit and at rest, and the encryption keys must not be shared or transferred out of the primary account.

What approach should the Cloud Data Engineering team adopt to securely configure encryption for access across these AWS account boundaries?

Options :

A : Implement server-side encryption on the S3 bucket using AWS Key Management Service (AWS KMS) with a customer-managed key (CMK) in Account A. Share the CMK with Account B and apply necessary IAM policies in Account B for decryption.

B : Utilize Amazon S3 server-side encryption with customer-provided keys (SSE-C), storing the encryption keys in AWS Secrets Manager within Account A, and grant Account B access to these keys.

C : Configure an Amazon S3 bucket policy granting cross-account access to Account B and enable AWS KMS cross-account encryption by creating a new KMS key in Account B and referencing it in the S3 bucket policy of Account A.

D : Enable Amazon S3 server-side encryption with an AWS-managed key (SSE-S3) and use a resource-based policy on the S3 bucket to grant access to Account B.

Answer: A

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