Question 1

A financial services company is using Amazon RDS for PostgreSQL to manage sensitive customer data. The database is accessed by various applications deployed in an AWS environment. The company's security policy mandates that the data must be encrypted at rest. The database administrator needs to enable encryption for the existing unencrypted database with minimal downtime.

Which of the following approaches should the administrator take?

Options :

A : Enable encryption on the existing RDS instance directly via the AWS Management Console.

B : Take a snapshot of the existing RDS instance, copy the snapshot with encryption enabled, and then restore the database from the encrypted snapshot.

C : Create a new RDS instance with encryption enabled and migrate the data from the existing unencrypted database using AWS Database Migration Service (DMS).

D : Use AWS Key Management Service (KMS) to encrypt the existing database file at the storage level.

Answer: B

Question 2

As a Data Engineer, you are looking to optimize an AWS Glue job that is currently handling a large dataset stored in CSV format. You've observed that the job's initialization and execution times are longer than desired. Notably, most processing tasks within this job involve retrieving specific columns from the dataset rather than the entire data range.

Which of the following options would most effectively enhance the Glue job's performance?

Options :

A : Convert the CSV data into Parquet format using AWS Glue, and then run the ETL job on the Parquet data, which is more efficient for querying and processing.

B : Increase the number of DPUs (Data Processing Units) allocated to the Glue job to expedite data processing and reduce job runtime.

C : Implement job bookmarking along with increasing the job timeout setting, ensuring longer runtimes for processing extensive datasets without interruptions.

D : Utilize Glue-s "Bookmark" feature to process only the new or changed data, ensuring subsequent runs of the job are faster.

Answer: A

Question 3

A data engineer must ingest a source of structured data that is in .csv format into an Amazon S3 data lake. The

.csv files contain 15 columns. Data analysts need to run Amazon Athena queries on one or two columns of the

dataset. The data analysts rarely query the entire file.

Which solution will meet these requirements MOST cost-effectively?

Options :

A : Use an AWS Glue PySpark job to ingest the source data into the data lake in .csv format.

B : Create an AWS Glue extract, transform, and load (ETL) job to read from the .csv structured data source.

Configure the job to ingest the data into the data lake in JSON format.

C : Use an AWS Glue PySpark job to ingest the source data into the data lake in Apache Avro format.

D :

Create an AWS Glue extract, transform, and load (ETL) job to read from the .csv structured data source. Configure the job to write the data into the data lake in Apache Parquet format.

Answer: D

Question 4

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 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?

Options :

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

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