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Question 1:
A data engineer working for an analytics company is working on a consumer to a Kinesis Data Streams application. They have written the consumer using Kinesis Client Library (KCL), however, currently they are receiving an ExpiredIteratorException when reading records from Kinesis Data Streams.
What would you recommend to the engineer to solve their issue?
A. Change the capacity mode of the Kinesis Data Stream to on-demand.
B. Increase WCU in DynamoDB checkpointing table.
C. Increase the amount of shards in Kinesis Data Streams.
D. Increase RCU in DynamoDB checkpointing table.
Answer: B
Explanation:
A) Increase WCU in DynamoDB checkpointing table: Kinesis Client Library leverages DynamoDB for coordination and checkpointing. A new shard iterator is returned by every GetRecords request (as NextShardIterator), which you then use in the next GetRecords request (as ShardIterator). If the shard iterator expires immediately, before you can use it, this might indicate that the DynamoDB table used by Kinesis does not have enough capacity.
For more on troubleshooting AWS Kinesis Data Streams: https://docs.aws.amazon.com/streams/latest/dev/troubleshooting-consumers.html#shard-iterator-expires-unexpectedly
Question 2:
You are building a pipeline to process, analyze and classify images. Your image datasets contain images that you need to preprocess as a first step by resizing and enhancing image contrast. Which AWS service should you consider to use to preprocess the datasets?
A. AWS Step Functions
B. AWS Data Pipeline
C. AWS SageMaker Data Wrangler
D. AWS Glue DataBrew
Answer: C
Explanation:
https://docs.aws.amazon.com/databrew/latest/dg/recipe-actions-reference.html
Question 3:
A social media company currently stores 1TB of data within S3 and wants to be able to analyze this data using SQL queries. Which method allows the company to do this with the LEAST effort?
A. Use AWS Kinesis Data Analytics to query the data from S3
B. Use Athena to query the data from S3
C. Create an AWS Glue job to transfer the data to Redshift. Query the data from Redshift
D. Use Kinesis Data Firehose to transfer the data to RDS. Query the data from RDS
Answer: B
Question 4:
A solutions architect at a retail company is working on an application that utilizes SQS for decoupling its components. However, when inspecting application logs, they see that SQS messages are often being processed multiple times. What would you advise the architect to fix the issue?
A. Decrease the visibility timeout for the messages
B. Change the application to use long polling with SQS
C. Increase the visibility timeout for the messages
D. Change the application to use short polling with SQS
Answer: C
Explanation:
Question 5:
Which of the following services are capable of reading from AWS Kinesis Data Streams (SELECT THREE)?
A. Amazon Managed Service for Apache Flink
B. EFS
C. S3
D. EC2
E. EMR
Answer: A, D, E
Explanation:
The currently available services for processing data from Kinesis Data Streams are: Amazon Managed Service for Apache Flink, Spark on Amazon EMR, EC2, Lambda, Kinesis Data Firehose, and the Kinesis Client Library.
S3 and EFS can be output locations using the previously mentioned services, however, they cannot be used with Kinesis Data Streams without an intermediary processing step/service.
For more on AWS Kinesis Data Streams:
https://aws.amazon.com/kinesis/data-streams/
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Question 6:
A company has a daily running ETL process, which processes transactions from a production database. The process is not time sensitive and can be run at any point of the day. Currently, the company is in the process of migrating the ETL job to an AWS Glue Spark job.
As a Certified Data Engineer, what would be the most cost-efficient way to structure the Glue ETL job?
A. Set the Glue to version 2.0
B. Set the execution class of the Glue job to FLEX
C. Set the execution class of the Glue job to STANDARD
D. Set the Glue job to use Spot instances
Answer: D
Question 7:
Which of the following statements are CORRECT regarding AWS SQS (SELECT TWO)?
A. Message size is limited to 256KB
B. Messages cannot be duplicated
C. Messages will be delivered in order
D. Messages can be duplicated
E. Message size is limited to 128KB
Answer: A, D
Explanation:
Message size is limited to 256KB : This statement is correct. In Amazon Simple Queue Service (SQS), the maximum size of a single message is 256KB.
Messages can be duplicated : This statement is correct. By default, SQS allows duplicate messages to be sent. It does not guarantee message deduplication. SQS FIFO queues guarantees exactly-once delivery, using a deduplication ID.
Messages cannot be duplicated: Incorrect due to above.
Message size is limited to 128KB: Incorrect due to above.
Messages will be delivered in order: This statement is incorrect. SQS has best-effort-ordering, which does not guarantee that messages are delivered in order. For this, the SQS FIFO queue must be used.
For more on AWS SQS:
https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/standard-queues.html
Question 8:
A data engineer at a company is tasked with designing a data integration and transformation solution for the organization‘s data lake in AWS. The goal is to ensure efficient, automated, and scalable data ingestion and transformation workflows. Which AWS service is best suited for achieving this, offering capabilities for data cataloging, ETL job orchestration, and serverless execution?
A. AWS Glue
B. AWS Lambda
C. AWS Data Pipeline
D. AWS Step Functions
Answer: A
Explanation:
AWS Glue : Explanation: AWS Glue is the best choice as it provides a comprehensive solution for data integration and transformation. It offers data cataloging, ETL job orchestration, and serverless execution capabilities, making it ideal for efficient, automated, and scalable workflows in a data lake. See: https://docs.aws.amazon.com/glue/latest/dg/what-is-glue.html
AWS Data Pipeline. Explanation: While AWS Data Pipeline can orchestrate data workflows, it‘s not as feature-rich as AWS Glue when it comes to ETL and serverless execution, making it less suitable for the described scenario.
AWS Step Functions. Explanation: AWS Step Functions are great for orchestrating serverless workflows, but do not offer the full range of ETL and data cataloging capabilities required for a data lake integration and transformation solution.
AWS Lambda: Explanation. AWS Lambda is a serverless compute service, but is not specifically designed for data integration and ETL tasks. It can be part of a larger solution, but would require more custom development compared to AWS Glue.
Question 9:
A data engineer working in an analytics company has been tasked to migrate their Apache Cassandra database to AWS. What AWS service should the engineer use to migrate the database to AWS, with the LEAST amount of operational overhead?
A. DocumentDB
B. Amazon Neptune
C. Amazon Keyspaces
D. Amazon RDS
Answer: C
Question 10:
A data analyst at a social media company wants to create a new Redshift table from a query. What would you recommend to the analyst?
A. Use the SELECT INTO command on Redshift to query data and create a table from the results of the query
B. Use the CREATE TABLE command on Redshift to create a table from a given query
C. Use the COPY command on Redshift to create a table from a given query
D. Use the SELECT command on Redshift and save the intermediary results to S3. E. Use the COPY command to create a new table on Redshift from the S3 data
Answer: A
Explanation:
Use the SELECT INTO command on Redshift to query data and create a table from the results of the query : The SELECT INTO command selects rows defined by any query and inserts them into a new table. You can specify whether to create a temporary or a persistent table.
For more on Redshift SELECT INTO: https://docs.aws.amazon.com/redshift/latest/dg/r_SELECT_INTO.html
Incorrect answers:
Use the SELECT command on Redshift and save the intermediary results to S3. Use the COPY command to create a new table on Redshift from the S3 data: This is unnecessarily complex, and the SELECT command cannot be used to save intermediary results to S3.
Use the COPY command on Redshift to create a table from a given query: COPY command loads data into a table from data files or from an Amazon DynamoDB table.
Use the CREATE TABLE command on Redshift to create a table from a given query: CREATE TABLE command creates a new table in the current database. You define a list of columns, which each hold data of a distinct type.
For more on Redshift SQL commands:
https://docs.aws.amazon.com/redshift/latest/dg/c_SQL_commands.html
For a full set of 390+ questions. Go to
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SkillCertPro offers detailed explanations to each question which helps to understand the concepts better.
It is recommended to score above 85% in SkillCertPro exams before attempting a real exam.
SkillCertPro updates exam questions every 2 weeks.
You will get life time access and life time free updates
SkillCertPro assures 100% pass guarantee in first attempt.