Amazon DAS-C01 Exam Practice Test Instant Access

Question 1

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 2

A gaming company is collecting cllckstream data into multiple Amazon Kinesis data streams. The company uses Amazon Kinesis Data Firehose delivery streams to store the data in JSON format in Amazon S3 Data scientists use Amazon Athena to query the most recent data and derive business insights. The company wants to reduce its Athena costs without having to recreate the data pipeline. The company prefers a solution that will require less management effort

Which set of actions can the data scientists take immediately to reduce costs?

AChange the Kinesis Data Firehose output format to Apache Parquet Provide a custom S3 object YYYYMMDD prefix expression and specify a large buffer size For the existing data, run an AWS Glue ETL job to combine and convert small JSON files to large Parquet files and add the YYYYMMDD prefix Use ALTER TABLE ADD PARTITION to reflect the partition on the existing Athena table.

BCreate an Apache Spark Job that combines and converts JSON files to Apache Parquet files Launch an Amazon EMR ephemeral cluster daily to run the Spark job to create new Parquet files in a different S3 location Use ALTER TABLE SET LOCATION to reflect the new S3 location on the existing Athena table.

CCreate a Kinesis data stream as a delivery target for Kinesis Data Firehose Run Apache Flink on Amazon Kinesis Data Analytics on the stream to read the streaming data, aggregate ikand save it to Amazon S3 in Apache Parquet format with a custom S3 object YYYYMMDD prefix Use ALTER TABLE ADD PARTITION to reflect the partition on the existing Athena table

DIntegrate an AWS Lambda function with Kinesis Data Firehose to convert source records to Apache Parquet and write them to Amazon S3 In parallel, run an AWS Glue ETL job to combine and convert existing JSON files to large Parquet files Create a custom S3 object YYYYMMDD prefix Use ALTER TABLE ADD PARTITION to reflect the partition on the existing Athena table.

Answer : D

Question 3

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?

AUse 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.

BUse 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.

CUse 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.

DUse 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.

Answer : D

Question 4

A company receives data from its vendor in JSON format with a timestamp in the file name. The vendor uploads the data to an Amazon S3 bucket, and the data is registered into the company's data lake for analysis and reporting. The company has configured an S3 Lifecycle policy to archive all files to S3 Glacier after 5 days.

The company wants to ensure that its AWS Glue crawler catalogs data only from S3 Standard storage and ignores the archived files. A data analytics specialist must implement a solution to achieve this goal without changing the current S3 bucket configuration.

Which solution meets these requirements?

AUse the exclude patterns feature of AWS Glue to identify the S3 Glacier files for the crawler to exclude.

BSchedule an automation job that uses AWS Lambda to move files from the original S3 bucket to a new S3 bucket for S3 Glacier storage.

CUse the excludeStorageClasses property in the AWS Glue Data Catalog table to exclude files on S3 Glacier storage

DUse the include patterns feature of AWS Glue to identify the S3 Standard files for the crawler to include.

Answer : A

Question 5

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?

AGeospatial chart

BLine chart

CHeat map

DTree map

Answer : A

Question 6

A company is using an AWS Lambda function to run Amazon Athena queries against a cross-account AWS Glue Data Catalog. A query returns the following error:

HIVE METASTORE ERROR

The error message states that the response payload size exceeds the maximum allowed payload size. The queried table is already partitioned, and the data is stored in an

Amazon S3 bucket in the Apache Hive partition format.

Which solution will resolve this error?

AModify the Lambda function to upload the query response payload as an object into the S3 bucket. Include an S3 object presigned URL as the payload in the Lambda function response.

BRun the MSCK REPAIR TABLE command on the queried table.

CCreate a separate folder in the S3 bucket. Move the data files that need to be queried into that folder. Create an AWS Glue crawler that points to the folder instead of the S3 bucket.

DCheck the schema of the queried table for any characters that Athena does not support. Replace any unsupported characters with characters that Athena supports.

Answer : A

Question 7

A market data company aggregates external data sources to create a detailed view of product consumption in different countries. The company wants to sell this data to external parties through a subscription. To achieve this goal, the company needs to make its data securely available to external parties who are also AWS users.

What should the company do to meet these requirements with the LEAST operational overhead?

AStore the data in Amazon S3. Share the data by using presigned URLs for security.

BStore the data in Amazon S3. Share the data by using S3 bucket ACLs.

CUpload the data to AWS Data Exchange for storage. Share the data by using presigned URLs for security.

DUpload the data to AWS Data Exchange for storage. Share the data by using the AWS Data Exchange sharing wizard.

Answer : A

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