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Q#1

A banking company is currently using Amazon Redshift for sensitive data. An audit found that the current cluster is unencrypted. Compliance requires that a database with sensitive data must be encrypted using a hardware security module (HSM) with customer-managed keys.

Which modifications are required in the cluster to ensure compliance?

A. Create a new HSM-encrypted Amazon Redshift cluster and migrate the data to the new cluster.
B. Modify the DB parameter group with the appropriate encryption settings and then restart the cluster.
C. Enable HSM encryption in Amazon Redshift using the command line.
D. Modify the Amazon Redshift cluster from the console and enable encryption using the HSM option.

Correct Answer: A

When you modify your cluster to enable AWS KMS encryption, Amazon Redshift automatically migrates your data to a new encrypted cluster.

Reference: https://docs.aws.amazon.com/redshift/latest/mgmt/working-with-db-encryption.html

Q#2

A company is sending historical datasets to Amazon S3 for storage. A data engineer at the company wants to make these datasets available for analysis using Amazon Athena. The engineer also wants to encrypt the Athena query results in an S3 results location by using AWS solutions for encryption.

The requirements for encrypting the query results are as
follows:

  • Use custom keys for encryption of the primary dataset query results.
  • Use generic encryption for all other query results.
  • Provide an audit trail for the primary dataset queries that show when the keys were used and by whom.

A. Use server-side encryption with S3 managed encryption keys (SSE-S3) for the primary dataset. Use SSE-S3 for the other datasets.
B. Use server-side encryption with customer-provided encryption keys (SSE-C) for the primary dataset. Use server-side encryption with S3 managed encryption keys (SSE-S3) for the other datasets.
C. Use server-side encryption with AWS KMS managed customer master keys (SSE-KMS CMKs) for the primary dataset. Use server-side encryption with S3 managed encryption keys (SSE-S3) for the other datasets.
D. Use client-side encryption with AWS Key Management Service (AWS KMS) customer-managed keys for the primary dataset. Use S3 client-side encryption with client-side keys for the other datasets.

Correct Answer: A

Reference: https://d1.awsstatic.com/product-marketing/S3/Amazon_S3_Security_eBook_2020.pdf

Q#3

A company has collected more than 100 TB of log files in the last 24 months. The files are stored as raw text in a dedicated Amazon S3 bucket. Each object has a key of the form year-month-day_log_HHmmss.txt where HHmmss represents the time the log file was initially created. A table was created in Amazon Athena that points to the S3 bucket.

One-time queries are run against a subset of columns in the table several times an hour.
A data analyst must make changes to reduce the cost of running these queries. Management wants a solution with minimal maintenance overhead.

Which combination of steps should the data analyst take to meet these requirements? (Choose three.)

A. Convert the log files to Apache Avro format.
B. Add a key prefix of the form date=year-month-day/ to the S3 objects to partition the data.
C. Convert the log files to Apache Parquet format.
D. Add a key prefix of the form year-month-day/ to the S3 objects to partition the data.
E. Drop and recreate the table with the PARTITIONED BY clause. Run the ALTER TABLE ADD PARTITION statement.
F. Drop and recreate the table with the PARTITIONED BY clause. Run the MSCK REPAIR TABLE statement.

Correct Answer: BCF

Reference: https://docs.aws.amazon.com/athena/latest/ug/msck-repair-table.html

Q# 4

A company is providing analytics services to its sales and marketing departments. The departments can access the data only through their business intelligence (BI) tools, which run queries on Amazon Redshift using an Amazon Redshift internal user to connect.

Each department is assigned a user in the Amazon Redshift database with the permissions needed for that department. The marketing data analysts must be granted direct access to the advertising table, which is stored in Apache Parquet format in the marketing S3 bucket of the company data lake. The company data lake is managed by AWS Lake Formation.

Finally, access must be limited to the three promotion columns in the table.
Which combination of steps will meet these requirements? (Choose three.)

A. Grant permissions in Amazon Redshift to allow the marketing Amazon Redshift user to access the three promotion columns of the advertising external table.
B. Create an Amazon Redshift Spectrum IAM role with permissions for Lake Formation. Attach it to the Amazon Redshift cluster.
C. Create an Amazon Redshift Spectrum IAM role with permissions for the marketing S3 bucket. Attach it to the Amazon Redshift cluster.
D. Create an external schema in Amazon Redshift by using the Amazon Redshift Spectrum IAM role. Grant usage to the marketing Amazon Redshift user.
E. Grant permissions in Lake Formation to allow the Amazon Redshift Spectrum role to access the three promotion columns of the advertising table.
F. Grant permissions in Lake Formation to allow the marketing IAM group to access the three promotion columns of the advertising table.

Correct Answer: BDE

Q#5

An airline has .csv-formatted data stored in Amazon S3 with an AWS Glue Data Catalog. Data analysts want to join this data with call center data stored in Amazon Redshift as part of a daily batch process. The Amazon Redshift cluster is already under a heavy load.

The solution must be managed, serverless, well-functioning, and minimize the load on the
existing Amazon Redshift cluster. The solution should also require minimal effort and development activity.

Which solution meets these requirements?

A. Unload the call center data from Amazon Redshift to Amazon S3 using an AWS Lambda function. Perform the join with AWS Glue ETL scripts.
B. Export the call center data from Amazon Redshift using a Python shell in AWS Glue. Perform the join with AWS Glue ETL scripts.
C. Create an external table using Amazon Redshift Spectrum for the call center data and perform the join with Amazon Redshift.
D. Export the call center data from Amazon Redshift to Amazon EMR using Apache Sqoop. Perform the join with Apache Hive.

Correct Answer: C

Q#6

A media analytics company consumes a stream of social media posts. The posts are sent to an Amazon Kinesis data stream partitioned on user_id. An AWS Lambda function retrieves the records and validates the content before loading the posts into an Amazon OpenSearch Service (Amazon Elasticsearch Service) cluster.

The validation process needs to receive the posts for a given user in the order they were received by the Kinesis data stream.

During peak hours, the social media posts take more than an hour to appear in the Amazon OpenSearch Service (Amazon ES) cluster. A data analytics specialist must implement a solution that reduces this latency with the least possible operational overhead.

Which solution meets these requirements?

A. Migrate the validation process from Lambda to AWS Glue.
B. Migrate the Lambda consumers from standard data stream iterators to an HTTP/2 stream consumer.
C. Increase the number of shards in the Kinesis data stream.
D. Send the posts stream to Amazon Managed Streaming for Apache Kafka instead of the Kinesis data stream.

Correct Answer: C

For real-time processing of streaming data, Amazon Kinesis partitions data into multiple shards that can then be consumed by multiple Amazon EC2 Reference: https://d1.awsstatic.com/whitepapers/AWS_Cloud_Best_Practices.pdf

Q#7

A company operates toll services for highways across the country and collects data that is used to understand usage patterns. Analysts have requested the ability to run traffic reports in near-real-time.

The company is interested in building an ingestion pipeline that loads all the data into an Amazon Redshift cluster and alerts operations personnel when toll traffic for a particular toll station does not meet a specified threshold. Station data and the corresponding threshold values are stored in Amazon S3.

Which approach is the MOST efficient way to meet these requirements?

A. Use Amazon Kinesis Data Firehose to collect data and deliver it to Amazon Redshift and Amazon Kinesis Data Analytics simultaneously.

Create a reference data source in Kinesis Data Analytics to temporarily store the threshold values from Amazon S3 and compare the count of vehicles for a particular toll station against its corresponding threshold value. Use AWS Lambda to publish an Amazon Simple Notification Service (Amazon SNS) notification if the threshold is not met.

B. Use Amazon Kinesis Data Streams to collect all the data from toll stations. Create a stream in Kinesis Data Streams to temporarily store the threshold values from Amazon S3. Send both streams to Amazon Kinesis Data Analytics to compare the count of vehicles for a particular toll station against its corresponding threshold value.

Use AWS Lambda to publish an Amazon Simple Notification Service (Amazon SNS) notification if the threshold is not met. Connect Amazon Kinesis Data Firehose to Kinesis Data Streams to deliver the data to Amazon Redshift.

C. Use Amazon Kinesis Data Firehose to collect data and deliver it to Amazon Redshift. Then, automatically trigger an AWS Lambda function that queries the data in Amazon Redshift, compares the count of vehicles for a particular toll station against its corresponding threshold values read from Amazon S3, and publishes an Amazon Simple Notification Service (Amazon SNS) notification if the threshold is not met.

D. Use Amazon Kinesis Data Firehose to collect data and deliver it to Amazon Redshift and Amazon Kinesis Data Analytics simultaneously. Use Kinesis Data Analytics to compare the count of vehicles against the threshold value for the station stored in a table as an in-application stream based on information stored in Amazon S3.

Configure an AWS Lambda function as an output for the application that will publish an Amazon Simple Queue Service (Amazon SQS) notification to alert operations personnel if the threshold is not met.

Correct Answer: D

Q#8

A telecommunications company is looking for an anomaly-detection solution to identify fraudulent calls. The company currently uses Amazon Kinesis to stream voice call records in a JSON format from its on-premises database to Amazon S3. The existing dataset contains voice call records with 200 columns. To detect fraudulent calls, the solution would
need to look at 5 of these columns only.

The company is interested in a cost-effective solution using AWS that requires minimal effort and experience in anomaly detection algorithms. Which solution meets these requirements?

A. Use an AWS Glue job to transform the data from JSON to Apache Parquet. Use AWS Glue crawlers to discover the schema and build the AWS Glue Data Catalog. Use Amazon Athena to create a table with a subset of columns. Use Amazon QuickSight to visualize the data and then use Amazon QuickSight machine learning-powered anomaly
detection.

B. Use Kinesis Data Firehose to detect anomalies on a data stream from Kinesis by running SQL queries, which compute an anomaly score for all calls and store the output in Amazon RDS. Use Amazon Athena to build a dataset and Amazon QuickSight to visualize the results.

C. Use an AWS Glue job to transform the data from JSON to Apache Parquet. Use AWS Glue crawlers to discover the schema and build the AWS Glue Data Catalog. Use Amazon SageMaker to build an anomaly detection model that can detect fraudulent calls by ingesting data from Amazon S3.

D. Use Kinesis Data Analytics to detect anomalies on a data stream from Kinesis by running SQL queries, which compute an anomaly score for all calls. Connect Amazon QuickSight to Kinesis Data Analytics to visualize the anomaly scores.

Correct Answer: A

Q#9

A company currently uses Amazon Athena to query its global datasets. The regional data is stored in Amazon S3 in the us-east-1 and us-west-2 Regions. The data is not encrypted. To simplify the query process and manage it centrally, the company wants to use Athena in us-west-2 to query data from Amazon S3 in both Regions. The solution should be as
low-cost as possible.

What should the company do to achieve this goal?

A. Use AWS DMS to migrate the AWS Glue Data Catalog from us-east-1 to us-west-2. Run Athena queries in west-2.

B. Run the AWS Glue crawler in us-west-2 to catalog datasets in all Regions. Once the data is crawled, run Athena queries in us-west-2.

C. Enable cross-Region replication for the S3 buckets in us-east-1 to replicate data in us-west-2. Once the data is replicated in us-west-2, run the AWS Glue crawler there to update the AWS Glue Data Catalog in us-west-2 and run Athena queries.

D. Update AWS Glue resource policies to provide us-east-1 AWS Glue Data Catalog access to us-west-2. Once the catalog in us-west-2 has access to the catalog in us-east-1, run Athena queries in us-west-2.

Correct Answer: C

Q#10

A company wants to research user turnover by analyzing the past 3 months of user activities. With millions of users, 1.5 TB of uncompressed data is generated each day. A 30-node Amazon Redshift cluster with 2.56 TB of solid-state drive (SSD) storage for each node is required to meet the query performance goals.

The company wants to run an additional analysis on a year\’s worth of historical data to examine trends indicating which features are most popular. This analysis will be done once a week.

What is the MOST cost-effective solution?

A. Increase the size of the Amazon Redshift cluster to 120 nodes so it has enough storage capacity to hold 1 year of data. Then use Amazon Redshift for the additional analysis.

B. Keep the data from the last 90 days in Amazon Redshift. Move data older than 90 days to Amazon S3 and store it in Apache Parquet format partitioned by date. Then use Amazon Redshift Spectrum for the additional analysis.

C. Keep the data from the last 90 days in Amazon Redshift. Move data older than 90 days to Amazon S3 and store it in Apache Parquet format partitioned by date. Then provision a persistent Amazon EMR cluster and use Apache Presto for the additional analysis.

D. Resize the cluster node type to the dense storage node type (DS2) for an additional 16 TB storage capacity on each individual node in the Amazon Redshift cluster. Then use Amazon Redshift for the additional analysis.

Correct Answer: B

Q#11

A company has developed an Apache Hive script to batch process data started in Amazon S3. The script needs to run once every day and store the output in Amazon S3. The company tested the script, and it completes within 30 minutes on a small local three-node cluster.

Which solution is the MOST cost-effective for scheduling and executing the script?

A. Create an AWS Lambda function to spin up an Amazon EMR cluster with a Hive execution step. Set KeepJobFlowAliveWhenNoSteps to false and disable the termination protection flag. Use Amazon CloudWatch Events to schedule the

B. Use the AWS Management Console to spin up an Amazon EMR cluster with Python Hue. Hive, and Apache Oozie. Set the termination protection flag to true and use Spot Instances for the core nodes of the cluster. Configure an Oozie workflow in the cluster to invoke the Hive script daily.

C. Create an AWS Glue job with the Hive script to perform the batch operation. Configure the job to run once a day using a time-based schedule.

D. Use AWS Lambda layers and load the Hive runtime to AWS Lambda and copy the Hive script. Schedule the Lambda function to run daily by creating a workflow using AWS Step Functions.

Correct Answer: C

Q#12

A manufacturing company is storing data from its operational systems in Amazon S3. The company\\’s business analysts need to perform one-time queries of the data in Amazon S3 with Amazon Athena. The company needs to access the Athena network from the on-premises network by using a JDBC connection.

The company has created a VPC Security policy mandate that requests to AWS services cannot traverse the Internet. Which combination of steps should a data analytics specialist take to meet these requirements? (Choose two.)

A. Establish an AWS Direct Connect connection between the on-premises network and the VPC.
B. Configure the JDBC connection to connect to Athena through Amazon API Gateway.
C. Configure the JDBC connection to use a gateway VPC endpoint for Amazon S3.
D. Configure the JDBC connection to use an interface VPC endpoint for Athena.
E. Deploy Athena within a private subnet.

Correct Answer: AE

AWS Direct Connect makes it easy to establish a dedicated connection from an on-premises network to one or more VPCs in the same region.

Reference: https://docs.aws.amazon.com/whitepapers/latest/aws-vpc-connectivity-options/aws-direct-connect.html
https://stackoverflow.com/questions/68798311/aws-athena-connect-from-lambda

Q#13

A marketing company collects data from third-party providers and uses transient Amazon EMR clusters to process this data. The company wants to host an Apache Hive metastore that is persistent, reliable, and can be accessed by EMR clusters and multiple AWS services and accounts simultaneously. The metastore must also be available at all times.

Which solution meets these requirements with the LEAST operational overhead?

A. Use AWS Glue Data Catalog as the metastore
B. Use an external Amazon EC2 instance running MySQL as the metastore
C. Use Amazon RDS for MySQL as the metastore
D. Use Amazon S3 as the metastore

Correct Answer: A

Reference: https://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-hive-metastore-glue.html

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