A manufacturing company wants to create an operational analytics dashboard to visualize metrics from equipment in near-
real time. The company uses Amazon Kinesis Data Streams to stream the data to other applications. The dashboard must
automatically refresh every 5 seconds. A data analytics specialist must design a solution that requires the least possible
implementation effort.
Which solution meets these requirements?
B
Explanation:
Reference: https://aws.amazon.com/blogs/big-data/analyze-a-time-series-in-real-time-with-aws-lambda-amazon-kinesis-and-
amazon-dynamodb-streams/
An IoT company wants to release a new device that will collect data to track sleep overnight on an intelligent mattress.
Sensors will send data that will be uploaded to an Amazon S3 bucket. About 2 MB of data is generated each night for each
bed. Data must be processed and summarized for each user, and the results need to be available as soon as possible. Part
of the process consists of time windowing and other functions. Based on tests with a Python script, every run will require
about 1 GB of memory and will complete within a couple of minutes.
Which solution will run the script in the MOST cost-effective way?
A
A retail company has 15 stores across 6 cities in the United States. Once a month, the sales team requests a visualization in
Amazon QuickSight that provides the ability to easily identify revenue trends across cities and stores. The visualization also
helps identify outliers that need to be examined with further analysis.
Which visual type in QuickSight meets the sales team's requirements?
A
Explanation:
Reference: https://docs.aws.amazon.com/quicksight/latest/user/geospatial-charts.html
A gaming company is building a serverless data lake. The company is ingesting streaming data into Amazon Kinesis Data
Streams and is writing the data to Amazon S3 through Amazon Kinesis Data Firehose. The company is using 10 MB as the
S3 buffer size and is using 90 seconds as the buffer interval. The company runs an AWS Glue ETL job to merge and
transform the data to a different format before writing the data back to Amazon S3.
Recently, the company has experienced substantial growth in its data volume. The AWS Glue ETL jobs are frequently
showing an OutOfMemoryError error.
Which solutions will resolve this issue without incurring additional costs? (Choose two.)
A D
Explanation:
Reference: https://docs.aws.amazon.com/glue/latest/dg/grouping-input-files.html
https://docs.aws.amazon.com/glue/latest/dg/grouping-input-files.html
A company using Amazon QuickSight Enterprise edition has thousands of dashboards, analyses, and datasets. The
company struggles to manage and assign permissions for granting users access to various items within QuickSight. The
company wants to make it easier to implement sharing and permissions management.
Which solution should the company implement to simplify permissions management?
B
Explanation:
Reference: https://awscli.amazonaws.com/v2/documentation/api/latest/reference/quicksight/update-folder-permissions.html
A manufacturing company uses Amazon Connect to manage its contact center and Salesforce to manage its customer
relationship management (CRM) data. The data engineering team must build a pipeline to ingest data from the contact
center and CRM system into a data lake that is built on Amazon S3.
What is the MOST efficient way to collect data in the data lake with the LEAST operational overhead?
B
Explanation:
Reference: https://aws.amazon.com/kinesis/data-firehose/?kinesis-blogs.sort-by=item.additionalFields.createdDate&kinesis-
blogs.sort-order=desc
A company has a data warehouse in Amazon Redshift that is approximately 500 TB in size. New data is imported every few
hours and read-only queries are run throughout the day and evening. There is a
particularly heavy load with no writes for several hours each morning on business days. During those hours, some queries
are queued and take a long time to execute. The company needs to optimize query execution and avoid any downtime.
What is the MOST cost-effective solution?
A
A company analyzes historical data and needs to query data that is stored in Amazon S3. New data is generated daily as
.csv files that are stored in Amazon S3. The companys analysts are using Amazon Athena to perform SQL queries against a
recent subset of the overall data. The amount of data that is ingested into Amazon S3 has increased substantially over time,
and the query latency also has increased.
Which solutions could the company implement to improve query performance? (Choose two.)
B C
Explanation:
Reference: https://www.upsolver.com/blog/apache-parquet-why-use https://aws.amazon.com/blogs/big-data/work-with-
partitioned-data-in-aws-glue/
A company has a marketing department and a finance department. The departments are storing data in Amazon S3 in their
own AWS accounts in AWS Organizations. Both departments use AWS Lake Formation to catalog and secure their data.
The departments have some databases and tables that share common names.
The marketing department needs to securely access some tables from the finance department.
Which two steps are required for this process? (Choose two.)
A B
Explanation:
Granting Lake Formation Permissions
Creating an IAM role (AWS CLI)
Reference: https://docs.aws.amazon.com/lake-formation/latest/dg/lake-formation-permissions.html
https://docs.aws.amazon.com/IAM/latest/UserGuide/id_roles_create_for-user.html
A company developed a new elections reporting website that uses Amazon Kinesis Data Firehose to deliver full logs from
AWS WAF to an Amazon S3 bucket. The company is now seeking a low-cost option to perform this infrequent data analysis
with visualizations of logs in a way that requires minimal development effort.
Which solution meets these requirements?
D