Amazon DAS-C01 Exam Practice Test Instant Access

Question 1

A company's system operators and security engineers need to analyze activities within specific date ranges of AWS CloudTrail logs. All log files are stored in an Amazon S3 bucket, and the size of the logs is more than 5 T B. The solution must be cost-effective and maximize query performance.

Which solution meets these requirements?

ACopy the logs to a new S3 bucket with a prefix structure of <PARTITION COLUMN_NAME>. Use the date column as a partition key. Create a table on Amazon Athena based on the objects in the new bucket. Automatically add metadata partitions by using the MSCK REPAIR TABLE command in Athena. Use Athena to query the table and partitions.

BCreate a table on Amazon Athena. Manually add metadata partitions by using the ALTER TABLE ADD PARTITION statement, and use multiple columns for the partition key. Use Athena to query the table and partitions.

CLaunch an Amazon EMR cluster and use Amazon S3 as a data store for Apache HBase. Load the logs from the S3 bucket to an HBase table on Amazon EMR. Use Amazon Athena to query the table and partitions.

DCreate an AWS Glue job to copy the logs from the S3 source bucket to a new S3 bucket and create a table using Apache Parquet file format, Snappy as compression codec, and partition by date. Use Amazon Athena to query the table and partitions.

Answer : D

This solution meets the requirements because:

AWS Glue is a fully managed extract, transform, and load (ETL) service that can be used to prepare and load data for analytics1.You can use AWS Glue to create a job that copies the CloudTrail logs from the source S3 bucket to a new S3 bucket, and converts them to Apache Parquet format2.Parquet is a columnar storage format that is optimized for analytics and supports compression3.Snappy is a compression codec that provides a good balance between compression ratio and speed4.

AWS Glue can also create a table based on the Parquet files in the new S3 bucket, and partition the table by date2.Partitioning is a technique that divides a large dataset into smaller subsets based on a partition key, such as date5.Partitioning can improve query performance by reducing the amount of data scanned and filtering out irrelevant data5.

Amazon Athena is an interactive query service that allows you to analyze data in S3 using standard SQL6. You can use Athena to query the table created by AWS Glue, and specify the partitions you want to query based on the date range. Athena can leverage the benefits of Parquet format and partitioning to run queries faster and more cost-effectively.

Question 2

A company wants to enrich application logs in near-real-time and use the enriched dataset for further analysis. The application is running on Amazon EC2 instances across multiple Availability Zones and storing its logs using Amazon CloudWatch Logs. The enrichment source is stored in an Amazon DynamoDB table.

Which solution meets the requirements for the event collection and enrichment?

AUse a CloudWatch Logs subscription to send the data to Amazon Kinesis Data Firehose. Use AWS Lambda to transform the data in the Kinesis Data Firehose delivery stream and enrich it with the data in the DynamoDB table. Configure Amazon S3 as the Kinesis Data Firehose delivery destination.

BExport the raw logs to Amazon S3 on an hourly basis using the AWS CLI. Use AWS Glue crawlers to catalog the logs. Set up an AWS Glue connection for the DynamoDB table and set up an AWS Glue ETL job to enrich the data. Store the enriched data in Amazon S3.

CConfigure the application to write the logs locally and use Amazon Kinesis Agent to send the data to Amazon Kinesis Data Streams. Configure a Kinesis Data Analytics SQL application with the Kinesis data stream as the source. Join the SQL application input stream with DynamoDB records, and then store the enriched output stream in Amazon S3 using Amazon Kinesis Data Firehose.

DExport the raw logs to Amazon S3 on an hourly basis using the AWS CLI. Use Apache Spark SQL on Amazon EMR to read the logs from Amazon S3 and enrich the records with the data from DynamoDB. Store the enriched data in Amazon S3.

Answer : A

https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/SubscriptionFilters.html#FirehoseExample

Question 3

A company recently created a test AWS account to use for a development environment The company also created a production AWS account in another AWS Region As part of its security testing the company wants to send log data from Amazon CloudWatch Logs in its production account to an Amazon Kinesis data stream in its test account

Which solution will allow the company to accomplish this goal?

ACreate a subscription filter in the production accounts CloudWatch Logs to target the Kinesis data stream in the test account as its destination In the test account create an 1AM role that grants access to the Kinesis data stream and the CloudWatch Logs resources in the production account

BIn the test account create an 1AM role that grants access to the Kinesis data stream and the CloudWatch Logs resources in the production account Create a destination data stream in Kinesis Data Streams in the test account with an 1AM role and a trust policy that allow CloudWatch Logs in the production account to write to the test account

CIn the test account, create an 1AM role that grants access to the Kinesis data stream and the CloudWatch Logs resources in the production account Create a destination data stream in Kinesis Data Streams in the test account with an 1AM role and a trust policy that allow CloudWatch Logs in the production account to write to the test account

DCreate a destination data stream in Kinesis Data Streams in the test account with an 1AM role and a trust policy that allow CloudWatch Logs in the production account to write to the test account Create a subscription filter in the production accounts CloudWatch Logs to target the Kinesis data stream in the test account as its destination

Answer : D

Question 4

A company uses an Amazon EMR cluster with 50 nodes to process operational data and make the data available for data analysts These jobs run nightly use Apache Hive with the Apache Jez framework as a processing model and write results to Hadoop Distributed File System (HDFS) In the last few weeks, jobs are failing and are producing the following error message

"File could only be replicated to 0 nodes instead of 1"

A data analytics specialist checks the DataNode logs the NameNode logs and network connectivity for potential issues that could have prevented HDFS from replicating data The data analytics specialist rules out these factors as causes for the issue

Which solution will prevent the jobs from failing'?

AMonitor the HDFSUtilization metric. If the value crosses a user-defined threshold add task nodes to the EMR cluster

BMonitor the HDFSUtilization metri.c If the value crosses a user-defined threshold add core nodes to the EMR cluster

CMonitor the MemoryAllocatedMB metric. If the value crosses a user-defined threshold, add task nodes to the EMR cluster

DMonitor the MemoryAllocatedMB metric. If the value crosses a user-defined threshold, add core nodes to the EMR cluster.

Answer : C

Question 5

A machinery company wants to collect data from sensors. A data analytics specialist needs to implement a solution that aggregates the data in near-real time and saves the data to a persistent data store. The data must be stored in nested JSON format and must be queried from the data store with a latency of single-digit milliseconds.

Which solution will meet these requirements?

AUse Amazon Kinesis Data Streams to receive the data from the sensors. Use Amazon Kinesis Data Analytics to read the stream, aggregate the data, and send the data to an AWS Lambda function. Configure the Lambda function to store the data in Amazon DynamoDB.

BUse Amazon Kinesis Data Firehose to receive the data from the sensors. Use Amazon Kinesis Data Analytics to aggregate the data. Use an AWS Lambda function to read the data from Kinesis Data Analytics and store the data in Amazon S3.

CUse Amazon Kinesis Data Firehose to receive the data from the sensors. Use an AWS Lambda function to aggregate the data during capture. Store the data from Kinesis Data Firehose in Amazon DynamoDB.

DUse Amazon Kinesis Data Firehose to receive the data from the sensors. Use an AWS Lambda function to aggregate the data during capture. Store the data in Amazon S3.

Answer : C

This solution meets the requirements because:

Amazon Kinesis Data Firehose is a fully managed service that can capture, transform, and load streaming data into AWS data stores, such as Amazon S3, Amazon Redshift, Amazon Elasticsearch Service, and Amazon DynamoDB1. It can receive data from sensors and other sources and deliver it to a destination with near-real time latency.

AWS Lambda is a serverless compute service that can run code in response to events and automatically manage the underlying compute resources2.It can be used to perform custom transformations on the data during capture by Kinesis Data Firehose3. It can aggregate the data according to the desired logic and output format.

Amazon DynamoDB is a fully managed NoSQL database service that supports key-value and document data models4. It can store nested JSON data as document attributes and provide single-digit millisecond latency for queries. It can be used as a persistent data store for the aggregated sensor data.

Question 6

A company plans to store quarterly financial statements in a dedicated Amazon S3 bucket. The financial statements must not be modified or deleted after they are saved to the S3 bucket.

Which solution will meet these requirements?

ACreate the S3 bucket with S3 Object Lock in governance mode.

BCreate the S3 bucket with MFA delete enabled.

CCreate the S3 bucket with S3 Object Lock in compliance mode.

DCreate S3 buckets in two AWS Regions. Use S3 Cross-Region Replication (CRR) between the buckets.

Answer : A

This solution meets the requirements because:

S3 Object Lock is a feature in Amazon S3 that allows users and businesses to store files in a highly secure, tamper-proof way.It's used for situations in which businesses must be able to prove that data has not been modified or destroyed after it was written, and it relies on a model known as write once, read many (WORM)1.

S3 Object Lock provides two ways to manage object retention: retention periods and legal holds. A retention period specifies a fixed period of time during which an object remains locked.A legal hold provides the same protection as a retention period, but it has no expiration date2.

S3 Object Lock has two retention modes: governance mode and compliance mode. Governance mode allows users with specific IAM permissions to overwrite or delete an object version before its retention period expires.Compliance mode prevents anyone, including the root user of the account that owns the bucket, from overwriting or deleting an object version or altering its lock settings until the retention period expires2.

By creating the S3 bucket with S3 Object Lock in compliance mode, the company can ensure that the quarterly financial statements are stored in a WORM model and cannot be modified or deleted by anyone until the retention period expires or the legal hold is removed.This can help meet regulatory requirements that require WORM storage, or to add another layer of protection against object changes and deletion2.

Question 7

A banking company is currently using an Amazon Redshift cluster with dense storage (DS) nodes to store sensitive dat

a. 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.)

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

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

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

DEnable HSM with key rotation through the AWS CLI.

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

Answer : B, D

Amazon DAS-C01 AWS Certified Data Analytics - Specialty Exam Practice Test