Question 1

A company receives test results from testing facilities that are located around the world. The company stores

the test results in millions of 1 KB JSON files in an Amazon S3 bucket. A data engineer needs to process the

files, convert them into Apache Parquet format, and load them into Amazon Redshift tables. The data

engineer uses AWS Glue to process the files, AWS Step Functions to orchestrate the processes, and Amazon

EventBridge to schedule jobs.

The company recently added more testing facilities. The time required to process files is increasing. The data

engineer must reduce the data processing time.

Which solution will MOST reduce the data processing time?

Options :

A :

Use AWS Lambda to group the raw input files into larger files. Write the larger files back to Amazon S3. Use AWS Glue to process the files. Load the files into the Amazon Redshift tables.

B :

Use the AWS Glue dynamic frame file-grouping option to ingest the raw input files. Process the files. Load the files into the Amazon Redshift tables.

C :

Use the Amazon Redshift COPY command to move the raw input files from Amazon S3 directly into the Amazon Redshift tables. Process the files in Amazon Redshift.

D :

Use Amazon EMR instead of AWS Glue to group the raw input files. Process the files in Amazon EMR. Load the files into the Amazon Redshift tables.

Answer: B

Question 2

As a Cloud Data Engineer, you are tasked with troubleshooting a recurring issue in an AWS Glue job that is supposed to transform a large dataset. The job fails intermittently, with logs indicating memory errors. The dataset being processed is not unusually large, and similar jobs have run successfully in the past.

Which of the following steps should you take first to resolve this issue?

Options :

A : Check the script for inefficient transformations, such as unnecessary shuffles or large joins.

B : Switch the ETL job to use G.2X instances instead of the standard instances.

C : Increase the number of Data Processing Units (DPUs) allocated to the job.

D : Configure the job to write intermediate results to Amazon S3 to free up memory.

Answer: A

Question 3

As a Data Engineer, you've been given the task of enhancing the performance of a business analytics application. This application predominantly performs complex, read-intensive queries against large, historical datasets.

The data is currently housed in a MySQL database on Amazon RDS. However, due to the increasing dataset size and query complexity, query performance has significantly degraded. To address this issue, you are considering implementing Amazon ElastiCache.

Which caching strategy would be most effective in this scenario to improve query performance?

Options :

A : Use ElastiCache for Memcached implementing an Invalidation strategy, where cache entries are invalidated or updated whenever the database write operations occur.

B : Implement ElastiCache for Redis using a Lazy-Loading strategy, loading data into the cache only when it is requested.

C : Utilize ElastiCache for Memcached with a Write-Through caching strategy, ensuring data is written to both the cache and the database simultaneously.

D : Deploy ElastiCache for Redis with a Write-Around caching strategy, where data is written directly to the database and only the data read from the database is cached.

Answer: B

Question 4

A healthcare application hosted on AWS uses Amazon EC2 instances to manage sensitive patient data that must comply with regulatory security standards. The data is stored on EBS volumes attached to the EC2 instances. The company's security policy mandates that all data at rest be encrypted to protect against unauthorized access.

Which action should the IT department take to secure the EBS volumes in accordance with the company's security policy?

Options :

A : Use AWS Certificate Manager to apply SSL/TLS certificates to the EBS volumes.

B : Encrypt the EBS volumes with Amazon S3 server-side encryption.

C : Enable EBS encryption with AWS Key Management Service (KMS) keys.

D : Run a third-party encryption tool to encrypt data on the EBS volumes before storage.

Answer: C

Question 5

A business specializing in big data analytics is transitioning its data processing from an on-site data center to AWS to cut down on management complexity. They're interested in adopting a serverless architecture wherever possible.

Their current setup involves heavy use of Apache Pig, Apache Oozie, Apache Spark, Apache HBase, and Apache Flink, handling petabytes of data with rapid processing times. The business requires that their new cloud-based solution offer comparable, if not superior, processing performance.

Which AWS service should they choose to achieve serverless ETL at scale?

Options :

A : Use serverless Amazon Athena for interactive query services.

B : Use Amazon S3 with S3 Select for optimized data retrieval.

C : Use Amazon Kinesis Data Analytics for real-time analytics.

D : Use AWS Glue for serverless ETL operations.

Answer: D