Amazon DAS-C01 AWS Certified Data Analytics – Specialty Online Training
Amazon DAS-C01 Online Training
The questions for DAS-C01 were last updated at Feb 27,2026.
- Exam Code: DAS-C01
- Exam Name: AWS Certified Data Analytics - Specialty
- Certification Provider: Amazon
- Latest update: Feb 27,2026
A company wants to improve the data load time of a sales data dashboard. Data has been collected as .csv files and stored within an Amazon S3 bucket that is partitioned by date. The data is then loaded to an Amazon Redshift data warehouse for frequent analysis. The data volume is up to 500 GB per day.
Which solution will improve the data loading performance?
- A . Compress .csv files and use an INSERT statement to ingest data into Amazon Redshift.
- B . Split large .csv files, then use a COPY command to load data into Amazon Redshift.
- C . Use Amazon Kinesis Data Firehose to ingest data into Amazon Redshift.
- D . Load the .csv files in an unsorted key order and vacuum the table in Amazon Redshift.
A company has a data warehouse in Amazon Redshift that is approximately 500 TB in size. New data is imported every few hours and read-only queries are run throughout the day and evening. There is a particularly heavy load with no writes for several hours each morning on business days. During those hours, some queries are queued and take a long time to execute. The company needs to optimize query execution and avoid any downtime.
What is the MOST cost-effective solution?
- A . Enable concurrency scaling in the workload management (WLM) queue.
- B . Add more nodes using the AWS Management Console during peak hours. Set the distribution style to ALL.
- C . Use elastic resize to quickly add nodes during peak times. Remove the nodes when they are not needed.
- D . Use a snapshot, restore, and resize operation. Switch to the new target cluster.
A company analyzes its data in an Amazon Redshift data warehouse, which currently has a cluster of
three dense storage nodes. Due to a recent business acquisition, the company needs to load an additional 4 TB of user data into Amazon Redshift. The engineering team will combine all the user data and apply complex calculations that require I/O intensive resources. The company needs to adjust the cluster’s capacity to support the change in analytical and storage requirements.
Which solution meets these requirements?
- A . Resize the cluster using elastic resize with dense compute nodes.
- B . Resize the cluster using classic resize with dense compute nodes.
- C . Resize the cluster using elastic resize with dense storage nodes.
- D . Resize the cluster using classic resize with dense storage nodes.
A company stores its sales and marketing data that includes personally identifiable information (PII) in Amazon S3. The company allows its analysts to launch their own Amazon EMR cluster and run analytics reports with the data. To meet compliance requirements, the company must ensure the data is not publicly accessible throughout this process. A data engineer has secured Amazon S3 but must ensure the individual EMR clusters created by the analysts are not exposed to the public internet.
Which solution should the data engineer to meet this compliance requirement with LEAST amount of effort?
- A . Create an EMR security configuration and ensure the security configuration is associated with the EMR clusters when they are created.
- B . Check the security group of the EMR clusters regularly to ensure it does not allow inbound traffic from IPv4 0.0.0.0/0 or IPv6 ::/0.
- C . Enable the block public access setting for Amazon EMR at the account level before any EMR cluster is created.
- D . Use AWS WAF to block public internet access to the EMR clusters across the board.
A financial company uses Amazon S3 as its data lake and has set up a data warehouse using a multi-node Amazon Redshift cluster. The data files in the data lake are organized in folders based on the data source of each data file. All the data files are loaded to one table in the Amazon Redshift cluster
using a separate COPY command for each data file location. With this approach, loading all the data files into Amazon Redshift takes a long time to complete. Users want a faster solution with little or no increase in cost while maintaining the segregation of the data files in the S3 data lake.
Which solution meets these requirements?
- A . Use Amazon EMR to copy all the data files into one folder and issue a COPY command to load the data into Amazon Redshift.
- B . Load all the data files in parallel to Amazon Aurora, and run an AWS Glue job to load the data into Amazon Redshift.
- C . Use an AWS Glue job to copy all the data files into one folder and issue a COPY command to load the data into Amazon Redshift.
- D . Create a manifest file that contains the data file locations and issue a COPY command to load the data into Amazon Redshift.
A company’s marketing team has asked for help in identifying a high performing long-term storage service for their data based on the following requirements:
The data size is approximately 32 TB uncompressed.
There is a low volume of single-row inserts each day.
There is a high volume of aggregation queries each day.
Multiple complex joins are performed.
The queries typically involve a small subset of the columns in a table.
Which storage service will provide the MOST performant solution?
- A . Amazon Aurora MySQL
- B . Amazon Redshift
- C . Amazon Neptune
- D . Amazon Elasticsearch
A technology company is creating a dashboard that will visualize and analyze time-sensitive data. The data will come in through Amazon Kinesis Data Firehose with the butter interval set to 60 seconds. The dashboard must support near-real-time data.
Which visualization solution will meet these requirements?
- A . Select Amazon Elasticsearch Service (Amazon ES) as the endpoint for Kinesis Data Firehose. Set up a Kibana dashboard using the data in Amazon ES with the desired analyses and visualizations.
- B . Select Amazon S3 as the endpoint for Kinesis Data Firehose. Read data into an Amazon SageMaker Jupyter notebook and carry out the desired analyses and visualizations.
- C . Select Amazon Redshift as the endpoint for Kinesis Data Firehose. Connect Amazon QuickSight with SPICE to Amazon Redshift to create the desired analyses and visualizations.
- D . Select Amazon S3 as the endpoint for Kinesis Data Firehose. Use AWS Glue to catalog the data and Amazon Athena to query it. Connect Amazon QuickSight with SPICE to Athena to create the desired analyses and visualizations.
A financial company uses Apache Hive on Amazon EMR for ad-hoc queries. Users are complaining of sluggish performance.
A data analyst notes the following:
Approximately 90% of queries are submitted 1 hour after the market opens.
Hadoop Distributed File System (HDFS) utilization never exceeds 10%.
Which solution would help address the performance issues?
- A . Create instance fleet configurations for core and task nodes. Create an automatic scaling policy to scale out the instance groups based on the Amazon CloudWatch CapacityRemainingGB metric. Create an automatic scaling policy to scale in the instance fleet based on the CloudWatch CapacityRemainingGB metric.
- B . Create instance fleet configurations for core and task nodes. Create an automatic scaling policy to scale out the instance groups based on the Amazon CloudWatch YARNMemoryAvailablePercentage metric. Create an automatic scaling policy to scale in the instance fleet based on the CloudWatch YARNMemoryAvailablePercentage metric.
- C . Create instance group configurations for core and task nodes. Create an automatic scaling policy to scale out the instance groups based on the Amazon CloudWatch CapacityRemainingGB metric. Create an automatic scaling policy to scale in the instance groups based on the CloudWatch CapacityRemainingGB metric.
- D . Create instance group configurations for core and task nodes. Create an automatic scaling policy to scale out the instance groups based on the Amazon CloudWatch YARNMemoryAvailablePercentage metric. Create an automatic scaling policy to scale in the instance groups based on the CloudWatch YARNMemoryAvailablePercentage metric.
A media company has been performing analytics on log data generated by its applications. There has been a recent increase in the number of concurrent analytics jobs running, and the overall performance of existing jobs is decreasing as the number of new jobs is increasing. The partitioned data is stored in Amazon S3 One Zone-Infrequent Access (S3 One Zone-IA) and the analytic processing is performed on Amazon EMR clusters using the EMR File System (EMRFS) with consistent view enabled. A data analyst has determined that it is taking longer for the EMR task nodes to list objects in Amazon S3.
Which action would MOST likely increase the performance of accessing log data in Amazon S3?
- A . Use a hash function to create a random string and add that to the beginning of the object prefixes when storing the log data in Amazon S3.
- B . Use a lifecycle policy to change the S3 storage class to S3 Standard for the log data.
- C . Increase the read capacity units (RCUs) for the shared Amazon DynamoDB table.
- D . Redeploy the EMR clusters that are running slowly to a different Availability Zone.
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?
- A . Have the ETL jobs read the data from Amazon S3 using a DataFrame.
- B . Enable job bookmarks on the AWS Glue jobs.
- C . Create custom logic on the ETL jobs to track the processed S3 objects.
- D . Have the ETL jobs delete the processed objects or data from Amazon S3 after each run.
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