Amazon DAS-C01 Exam Practice Test Instant Access

Question 1

A company uses an Amazon Redshift provisioned cluster for data analysis. The data is not encrypted at rest. A data analytics specialist must implement a solution to encrypt the data at rest.

Which solution will meet this requirement with the LEAST operational overhead?

AUse the ALTER TABLE command with the ENCODE option to update existing columns of the Redshift tables to use LZO encoding.

BExport data from the existing Redshift cluster to Amazon S3 by using the UNLOAD command with the ENCRYPTED option. Create a new Redshift cluster with encryption configured. Load data into the new cluster by using the COPY command.

CCreate a manual snapshot of the existing Redshift cluster. Restore the snapshot into a new Redshift cluster with encryption configured.

DModify the existing Redshift cluster to use AWS Key Management Service (AWS KMS) encryption. Wait for the cluster to finish resizing.

Answer : D

Question 2

A company wants to ingest clickstream data from its website into an Amazon S3 bucket. The streaming data is in JSON format. The data in the S3 bucket must be partitioned by product_id.

Which solution will meet these requirements MOST cost-effectively?

ACreate an Amazon Kinesis Data Firehose delivery stream to ingest the streaming data into the S3 bucket. Enable dynamic partitioning. Specify the data field of productjd as one partitioning key.

BCreate an AWS Glue streaming job to partition the data by productjd before delivering the data to the S3 bucket. Create an Amazon Kinesis Data Firehose delivery stream. Specify the AWS Glue job as the destination of the delivery stream.

CCreate an Amazon Kinesis Data Firehose delivery stream to ingest the streaming data into the S3 bucket. Create an AWS Glue ETL job to read the data stream in the S3 bucket, partition the data by productjd, and write the data into another S3 bucket.

DCreate an Amazon Kinesis Data Firehose delivery stream to ingest the streaming data into the S3 bucket. Create an Amazon EMR cluster that includes a job to read the data stream in the S3 bucket, partition the data by productjd, and write the data into another S3 bucket.

Answer : A

Question 3

A network administrator needs to create a dashboard to visualize continuous network patterns over time in a company's AWS account. Currently, the company has VPC Flow Logs enabled and is publishing this data to Amazon CloudWatch Logs. To troubleshoot networking issues quickly, the dashboard needs to display the new data in near-real time.

Which solution meets these requirements?

ACreate a CloudWatch Logs subscription to stream CloudWatch Logs data to an AWS Lambda function that writes the data to an Amazon S3 bucket. Create an Amazon QuickSight dashboard to visualize the data.

BCreate an export task from CloudWatch Logs to an Amazon S3 bucket. Create an Amazon QuickSight dashboard to visualize the data.

CCreate a CloudWatch Logs subscription that uses an AWS Lambda function to stream the CloudWatch Logs data directly into an Amazon OpenSearch Service cluster. Use OpenSearch Dashboards to create the dashboard.

DCreate a CloudWatch Logs subscription to stream CloudWatch Logs data to an AWS Lambda function that writes to an Amazon Kinesis data stream to deliver the data into an Amazon OpenSearch Service cluster. Use OpenSearch Dashboards to create the dashboard.

Answer : D

Question 4

A company ingests a large set of sensor data in nested JSON format from different sources and stores it in an Amazon S3 bucket. The sensor data must be joined with performance data currently stored in an Amazon Redshift cluster.

A business analyst with basic SQL skills must build dashboards and analyze this data in Amazon QuickSight. A data engineer needs to build a solution to prepare the data for use by the business analyst. The data engineer does not know the structure of the JSON file. The company requires a solution with the least possible implementation effort.

Which combination of steps will create a solution that meets these requirements? (Select THREE.)

AUse an AWS Glue ETL job to convert the data into Apache Parquet format and write to Amazon S3.

BUse an AWS Glue crawler to catalog the data.

CUse an AWS Glue ETL job with the ApplyMapping class to un-nest the data and write to Amazon Redshift tables.

DUse an AWS Glue ETL job with the Regionalize class to un-nest the data and write to Amazon Redshift tables.

EUse QuickSight to create an Amazon Athena data source to read the Apache Parquet files in Amazon S3.

FUse QuickSight to create an Amazon Redshift data source to read the native Amazon Redshift tables.

Answer : B, D, F

Question 5

A financial services firm is processing a stream of real-time data from an application by using Apache Kafka and Kafka MirrorMaker. These tools run on premises and stream data to Amazon Managed Streaming for Apache Kafka (Amazon MSK) in the us-east-1 Region. An Apache Flink consumer running on Amazon EMR enriches the data in real time and transfers the output files to an Amazon S3 bucket. The company wants to ensure that the streaming application is highly available across AWS Regions with an RTO of less than 2 minutes.

Which solution meets these requirements?

ALaunch another Amazon MSK and Apache Flink cluster in the us-west-1 Region that is the same size as the original
cluster in the us-east-1 Region. Simultaneously publish and process the data in both Regions. In the event of a disaster that impacts one of the Regions, switch to the other Region.

BSet up Cross-Region Replication from the Amazon S3 bucket in the us-east-1 Region to the us-west-1 Region. In the event of a disaster, immediately create Amazon MSK and Apache Flink clusters in the us-west-1 Region and start publishing data to this Region.

CAdd an AWS Lambda function in the us-east-1 Region to read from Amazon MSK and write to a global Amazon

DDynamoDB table in on-demand capacity mode. Export the data from DynamoDB to Amazon S3 in the us-west-1 Region. In the event of a disaster that impacts the us-east-1 Region, immediately create Amazon MSK and Apache Flink clusters in the us-west-1 Region and start publishing data to this Region.

ESet up Cross-Region Replication from the Amazon S3 bucket in the us-east-1 Region to the us-west-1 Region. In the event of a disaster, immediately create Amazon MSK and Apache Flink clusters in the us-west-1 Region and start publishing data to this Region. Store 7 days of data in on-premises Kafka clusters and recover the data missed during the recovery time from the on-premises cluster.

Answer : A

Question 6

A company receives datasets from partners at various frequencies. The datasets include baseline data and incremental data. The company needs to merge and store all the datasets without reprocessing the data.

Which solution will meet these requirements with the LEAST development effort?

AUse an AWS Glue job with a temporary table to process the datasets. Store the data in an Amazon RDS table.

BUse an Apache Spark job in an Amazon EMR cluster to process the datasets. Store the data in EMR File System (EMRFS).

CUse an AWS Glue job with job bookmarks enabled to process the datasets. Store the data in Amazon S3.

DUse an AWS Lambda function to process the datasets. Store the data in Amazon S3.

Answer : C

AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy to prepare and load data for analytics1. It can process datasets from various sources and formats, such as JDBC, Amazon S3, Amazon RDS, etc.

AWS Glue job bookmarks are a feature that helps AWS Glue track data that has already been processed during a previous run of an ETL job.This can prevent the reprocessing of old data and enable the processing of new data when rerunning on a scheduled interval2. Job bookmarks can handle both baseline data and incremental data from different sources.

Amazon S3 is a highly scalable, durable, and secure object storage service that can store any amount and type of data3. It can be used as a data lake to store the merged and processed datasets from AWS Glue. It can also integrate with other AWS services, such as Amazon Athena, Amazon Redshift Spectrum, Amazon EMR, etc., for further analysis and processing.

Question 7

A financial company uses Amazon Athena to query data from an Amazon S3 data lake. Files are stored in the S3 data lake in Apache ORC format. Data analysts recently introduced nested fields in the data lake ORC files, and noticed that queries are taking longer to run in Athen

a. A data analysts discovered that more data than what is required is being scanned for the queries.

What is the MOST operationally efficient solution to improve query performance?

AFlatten nested data and create separate files for each nested dataset.

BUse the Athena query engine V2 and push the query filter to the source ORC file.

CUse Apache Parquet format instead of ORC format.

DRecreate the data partition strategy and further narrow down the data filter criteria.

Answer : B

This solution meets the requirement because:

The Athena query engine V2 is a new version of the Athena query engine that introduces several improvements and new features, such as federated queries, geospatial functions, prepared statements, schema evolution support, and more1.

One of the improvements of the Athena query engine V2 is that it supports predicate pushdown for nested fields in ORC files. Predicate pushdown is a technique that allows filtering data at the source before it is scanned and loaded into memory.This can reduce the amount of data scanned and processed by Athena, which can improve query performance and reduce cost12.

By using the Athena query engine V2 and pushing the query filter to the source ORC file, the data analysts can leverage the predicate pushdown feature for nested fields and avoid scanning more data than what is required for the queries. This can improve query performance without changing the data format or partitioning strategy.

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