Expert Answers to Amazon Web Services Exam DAS-C01 Questions

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AWS Certified Data Analytics AWS Certified Data Analytics - Specialty

AWS Certified Data Analytics - Specialty

Last Update Dec 24, 2024
Total Questions : 207

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Questions 2

A company has developed several AWS Glue jobs to validate and transform its data from Amazon S3 and load it into Amazon RDS for MySQL in batches once every day. The ETL jobs read the S3 data using a DynamicFrame. Currently, the ETL developers are experiencing challenges in processing only the incremental data on every run, as the AWS Glue job processes all the S3 input data on each run.

Which approach would allow the developers to solve the issue with minimal coding effort?

Options:

Have the ETL jobs read the data from Amazon S3 using a DataFrame.

Enable job bookmarks on the AWS Glue jobs.

Create custom logic on the ETL jobs to track the processed S3 objects.

Have the ETL jobs delete the processed objects or data from Amazon S3 after each run.

Discussion 0

Questions 3

A company is designing a data warehouse to support business intelligence reporting. Users will access the executive dashboard heavily each Monday and Friday morning

for I hour. These read-only queries will run on the active Amazon Redshift cluster, which runs on dc2.8xIarge compute nodes 24 hours a day, 7 days a week. There are

three queues set up in workload management: Dashboard, ETL, and System. The Amazon Redshift cluster needs to process the queries without wait time.

What is the MOST cost-effective way to ensure that the cluster processes these queries?

Options:

Perform a classic resize to place the cluster in read-only mode while adding an additional node to the cluster.

Enable automatic workload management.

Perform an elastic resize to add an additional node to the cluster.

Enable concurrency scaling for the Dashboard workload queue.

Discussion 0

Questions 4

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?

Options:

Copy the logs to a new S3 bucket with a prefix structure of . 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.

Create 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.

Launch 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.

Create 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.

Discussion 0

Questions 5

A company wants to collect and process events data from different departments in near-real time. Before storing the data in Amazon S3, the company needs to clean the data by standardizing the format of the address and timestamp columns. The data varies in size based on the overall load at each particular point in time. A single data record can be 100 KB-10 MB.

How should a data analytics specialist design the solution for data ingestion?

Options:

Use Amazon Kinesis Data Streams. Configure a stream for the raw data. Use a Kinesis Agent to write data to the stream. Create an Amazon Kinesis Data Analytics application that reads data from the raw stream, cleanses it, and stores the output to Amazon S3.

Use Amazon Kinesis Data Firehose. Configure a Firehose delivery stream with a preprocessing AWS Lambda function for data cleansing. Use a KinesisAgent to write data to the delivery stream. Configure Kinesis Data Firehose to deliver the data to Amazon S3.

Use Amazon Managed Streaming for Apache Kafka. Configure a topic for the raw data. Use a Kafka producer to write data to the topic. Create an application on Amazon EC2 that reads data from the topic by using the Apache Kafka consumer API, cleanses the data, and writes to Amazon S3.

Use Amazon Simple Queue Service (Amazon SQS). Configure an AWS Lambda function to read events from the SQS queue and upload the events to Amazon S3.

Discussion 0

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