AWS-Certified-Data-Analytics-Specialty Amazon Exam Questions and Free Practice Test

Question 7

A banking company is currently using an Amazon Redshift cluster with dense storage (DS) nodes to store sensitive data. An audit found that the cluster is unencrypted. Compliance requirements state that a database with sensitive data must be encrypted through a hardware security module (HSM) with automated key rotation.
Which combination of steps is required to achieve compliance? (Choose two.)

A. Set up a trusted connection with HSM using a client and server certificate with automatic key rotation.

B. Modify the cluster with an HSM encryption option and automatic key rotation.

C. Create a new HSM-encrypted Amazon Redshift cluster and migrate the data to the new cluster.

D. Enable HSM with key rotation through the AWS CLI.

E. Enable Elliptic Curve Diffie-Hellman Ephemeral (ECDHE) encryption in the HSM.

Correct Answer:BD

Question 8

A company wants to improve the data load time of a sales data dashboard. Data has been collected as .csv files and stored within an Amazon S3 bucket that is partitioned by date. The data is then loaded to an Amazon Redshift data warehouse for frequent analysis. The data volume is up to 500 GB per day.
Which solution will improve the data loading performance?

A. Compress .csv files and use an INSERT statement to ingest data into Amazon Redshift.

B. Split large .csv files, then use a COPY command to load data into Amazon Redshift.

C. Use Amazon Kinesis Data Firehose to ingest data into Amazon Redshift.

D. Load the .csv files in an unsorted key order and vacuum the table in Amazon Redshift.

Correct Answer:B
https://docs.aws.amazon.com/redshift/latest/dg/c_loading-data-best-practices.html

Question 9

A retail company leverages Amazon Athena for ad-hoc queries against an AWS Glue Data Catalog. The data analytics team manages the data catalog and data access for the company. The data analytics team wants to separate queries and manage the cost of running those queries by different workloads and teams. Ideally, the data analysts want to group the queries run by different users within a team, store the query results in individual Amazon S3 buckets specific to each team, and enforce cost constraints on the queries run against the Data Catalog.
Which solution meets these requirements?

A. Create IAM groups and resource tags for each team within the compan

B. Set up IAM policies that controluser access and actions on the Data Catalog resources.

C. Create Athena resource groups for each team within the company and assign users to these group

D. Add S3 bucket names and other query configurations to the properties list for the resource groups.

E. Create Athena workgroups for each team within the compan

F. Set up IAM workgroup policies that control user access and actions on the workgroup resources.

G. Create Athena query groups for each team within the company and assign users to the groups.

Correct Answer:C
https://aws.amazon.com/about-aws/whats-new/2019/02/athena_workgroups/

Question 10

A data analyst is using Amazon QuickSight for data visualization across multiple datasets generated by applications. Each application stores files within a separate Amazon S3 bucket. AWS Glue Data Catalog is used as a central catalog across all application data in Amazon S3. A new application stores its data within a separate S3 bucket. After updating the catalog to include the new application data source, the data analyst created a new Amazon QuickSight data source from an Amazon Athena table, but the import into SPICE failed.
How should the data analyst resolve the issue?

A. Edit the permissions for the AWS Glue Data Catalog from within the Amazon QuickSight console.

B. Edit the permissions for the new S3 bucket from within the Amazon QuickSight console.

C. Edit the permissions for the AWS Glue Data Catalog from within the AWS Glue console.

D. Edit the permissions for the new S3 bucket from within the S3 console.

Correct Answer:B

Question 11

A smart home automation company must efficiently ingest and process messages from various connected devices and sensors. The majority of these messages are comprised of a large number of small files. These messages are ingested using Amazon Kinesis Data Streams and sent to Amazon S3 using a Kinesis data stream consumer application. The Amazon S3 message data is then passed through a processing pipeline built on Amazon EMR running scheduled PySpark jobs.
The data platform team manages data processing and is concerned about the efficiency and cost of downstream data processing. They want to continue to use PySpark.
Which solution improves the efficiency of the data processing jobs and is well architected?

A. Send the sensor and devices data directly to a Kinesis Data Firehose delivery stream to send the data to Amazon S3 with Apache Parquet record format conversion enable

B. Use Amazon EMR running PySpark to process the data in Amazon S3.

C. Set up an AWS Lambda function with a Python runtime environmen

D. Process individual Kinesis data stream messages from the connected devices and sensors using Lambda.

E. Launch an Amazon Redshift cluste

F. Copy the collected data from Amazon S3 to Amazon Redshift and move the data processing jobs from Amazon EMR to Amazon Redshift.

G. Set up AWS Glue Python jobs to merge the small data files in Amazon S3 into larger files and transform them to Apache Parquet forma

H. Migrate the downstream PySpark jobs from Amazon EMR to AWS Glue.

Correct Answer:D
https://aws.amazon.com/it/about-aws/whats-new/2020/04/aws-glue-now-supports-serverless-streaming-etl/

Question 12

An ecommerce company ingests a large set of clickstream data in JSON format and stores the data in Amazon S3. Business analysts from multiple product divisions need to use Amazon Athena to analyze the data. The company's analytics team must design a solution to monitor the daily data usage for Athena by each product division. The solution also must produce a warning when a divisions exceeds its quota
Which solution will meet these requirements with the LEAST operational overhead?

A. Use a CREATE TABLE AS SELECT (CTAS) statement to create separate tables for each product division Use AWS Budgets to track Athena usage Configure a threshold for the budget Use Amazon Simple Notification Service (Amazon SNS) to send notifications when thresholds are breached.

B. Create an AWS account for each division Provide cross-account access to an AWS Glue Data Catalog to all the account

C. Set an Amazon CloudWatch alarm to monitor Athena usag

D. Use Amazon Simple Notification Service (Amazon SNS) to send notifications.

E. Create an Athena workgroup for each division Configure a data usage control for each workgroup and a time period of 1 day Configure an action to send notifications to an Amazon Simple Notification Service(Amazon SNS) topic

F. Create an AWS account for each division Configure an AWS Glue Data Catalog in each account Set an Amazon CloudWatch alarm to monitor Athena usage Use Amazon Simple Notification Service (Amazon SNS) to send notifications.

Correct Answer:C

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