Free Amazon AWS-DEA-C01 Exam Questions

Question 1

Files from multiple data sources arrive in an Amazon S3 bucket on a regular basis. A data engineer wants to ingest new files into Amazon Redshift in near real time when the new files arrive in the S3 bucket. Which solution will meet these requirements?

A : Use the query editor v2 to schedule a COPY command to load new files into Amazon Redshift.

B : Use the zero-ETL integration between Amazon Aurora and Amazon Redshift to load new files into Amazon Redshift.

C : Use AWS Glue job bookmarks to extract, transform, and load (ETL) load new files into Amazon Redshift.

D : Use S3 Event Notifications to invoke an AWS Lambda function that loads new files into Amazon Redshift.

Answer: D

Question 2

A data engineer has two datasets that contain sales information for multiple cities and states. One dataset is named reference, and the other dataset is named primary. The data engineer needs a solution to determine whether a specific set of values in the city and state columns of the primary dataset exactly match the same specific values in the reference dataset. The data engineer wants to use Data Quality Definition Language (DQDL) rules in an AWS Glue Data Quality job. Which rule will meet these requirements?

A : DatasetMatch "reference" "city->ref_city, state->ref_state" = 1.0

B : ReferentialIntegrity "city,state" "reference.{ref_city,ref_state}" = 1.0

C : DatasetMatch "reference" "city->ref_city, state->ref_state" = 100

D : ReferentialIntegrity "city,state" "reference.{ref_city,ref_state}" = 100

Answer: A

Question 3

A data engineer has two datasets that contain sales information for multiple cities and states. One dataset is named reference, and the other dataset is named primary. The data engineer needs a solution to determine whether a specific set of values in the city and state columns of the primary dataset exactly match the same specific values in the reference dataset. The data engineer wants to use Data Quality Definition Language (DQDL) rules in an AWS Glue Data Quality job. Which rule will meet these requirements?

A : DatasetMatch "reference" "city->ref_city, state->ref_state" = 1.0

B : ReferentialIntegrity "city,state" "reference.{ref_city,ref_state}" = 1.0

C : DatasetMatch "reference" "city->ref_city, state->ref_state" = 100

D : ReferentialIntegrity "city,state" "reference.{ref_city,ref_state}" = 100

Answer: A

Question 4

A Data Engineering Team is working on optimizing a complex query that involves numerous joins and aggregations on a large dataset. The dataset is stored in a graph data structure for which the relationships between entities are as crucial as the entities themselves.

The team is deciding on the most appropriate data structure or AWS service to use for this task to ensure both performance efficiency and query optimization. Which of the following should they consider?

A : Implement the data in an Amazon Neptune graph database, taking advantage of its support for highly connected data and complex traversal queries.

B : Store the data in an Amazon DynamoDB table, using its fast and flexible NoSQL database service to manage the relationships and queries efficiently.

C : Utilize Amazon Redshift, leveraging its columnar storage and massively parallel processing (MPP) architecture to handle complex queries and aggregations efficiently.

D : Opt for an Amazon Elastic MapReduce (EMR) cluster with Apache Spark, using Spark's GraphX library for graph processing and query optimizations on large datasets.

Answer: A

Question 5

A research institution is looking to transition their on-premises data analytics platform, which includes Apache Hadoop clusters and a comprehensive data catalog managed in an Apache Hive metastore, to the AWS cloud. They are adopting Amazon EMR for their Hadoop workloads and require a serverless, cost-effective solution to migrate and manage their existing data catalog.

Which solution should the institution implement to migrate their Hive metastore to AWS while ensuring a serverless and cost-effective data catalog management?

A : Migrate the Hive metastore to Amazon RDS with AWS Database Migration Service (DMS), then connect the RDS instance to Amazon EMR clusters.

B : Use AWS Glue Data Catalog to import the existing Hive metastore, making it the centralized metadata repository, and integrate it with Amazon EMR.

C : Set up an Amazon EMR cluster with a configured Hive metastore, migrate the on-premises metastore to EMR, and utilize AWS Glue Data Catalog for catalog synchronization.

D : Implement Amazon Athena to reconstruct the data catalog from the Hive metastore data stored in Amazon S3, integrating it with EMR for analytics.

Answer: B