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The DAS-C01 exam is a challenging and comprehensive test that covers a range of topics related to data analytics. To pass the exam, you will need to demonstrate your understanding of various AWS services and tools that are commonly used in data analytics, such as Amazon S3, Amazon Redshift, and Amazon Kinesis. You will also need to have a solid understanding of data processing frameworks such as Apache Spark and Apache Hadoop.

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Amazon AWS Certified Data Analytics - Specialty (DAS-C01) Exam Sample Questions (Q88-Q93):

NEW QUESTION # 88
A data analyst is using Amazon QuickSight for data visualization across multiple datasets generated by applications. Each application stores files within a separate Amazon S3 bucket. AWS Glue Data Catalog is used as a central catalog across all application data in Amazon S3. A new application stores its data within a separate S3 bucket. After updating the catalog to include the new application data source, the data analyst created a new Amazon QuickSight data source from an Amazon Athena table, but the import into SPICE failed.
How should the data analyst resolve the issue?

A. Edit the permissions for the AWS Glue Data Catalog from within the Amazon QuickSight console.
B. Edit the permissions for the new S3 bucket from within the S3 console.
C. Edit the permissions for the new S3 bucket from within the Amazon QuickSight console.
D. Edit the permissions for the AWS Glue Data Catalog from within the AWS Glue console.

Answer: C

NEW QUESTION # 89
A company has a process that writes two datasets in CSV format to an Amazon S3 bucket every 6 hours. The company needs to join the datasets, convert the data to Apache Parquet, and store the data within another bucket for users to query using Amazon Athen a. The data also needs to be loaded to Amazon Redshift for advanced analytics. The company needs a solution that is resilient to the failure of any individual job component and can be restarted in case of an error.
Which solution meets these requirements with the LEAST amount of operational overhead?

A. Create an AWS Glue job using PySpark that creates dynamic frames of the datasets in Amazon S3, transforms the data, joins the data, writes the data back to Amazon S3, and loads the data to Amazon Redshift. Use an AWS Glue workflow to orchestrate the AWS Glue job.
B. Use AWS Step Functions to orchestrate the AWS Glue job. Create an AWS Glue job using Python Shell that creates dynamic frames of the datasets in Amazon S3, transforms the data, joins the data, writes the data back to Amazon S3, and loads the data to Amazon Redshift.
C. Use AWS Step Functions to orchestrate an Amazon EMR cluster running Apache Spark. Use PySpark to generate data frames of the datasets in Amazon S3, transform the data, join the data, write the data back to Amazon S3, and load the data to Amazon Redshift.
D. Create an AWS Glue job using Python Shell that generates dynamic frames of the datasets in Amazon S3, transforms the data, joins the data, writes the data back to Amazon S3, and loads the data to Amazon Redshift. Use an AWS Glue workflow to orchestrate the AWS Glue job at the desired frequency.

Answer: A

Explanation:
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 CSV and Parquet, and write them to different destinations, such as Amazon S3 and Amazon Redshift2.
AWS Glue provides two types of jobs: Spark and Python Shell. Spark jobs run on Apache Spark, a distributed processing framework that supports a wide range of data processing tasks3. Python Shell jobs run Python scripts on a managed serverless infrastructure4. Spark jobs are more suitable for complex data transformations and joins than Python Shell jobs.
AWS Glue provides dynamic frames, which are an extension of Apache Spark data frames. Dynamic frames handle schema variations and errors in the data more easily than data frames. They also provide a set of transformations that can be applied to the data, such as join, filter, map, etc.
AWS Glue provides workflows, which are directed acyclic graphs (DAGs) that orchestrate multiple ETL jobs and crawlers. Workflows can handle dependencies, retries, error handling, and concurrency for ETL jobs and crawlers. They can also be triggered by schedules or events.
By creating an AWS Glue job using PySpark that creates dynamic frames of the datasets in Amazon S3, transforms the data, joins the data, writes the data back to Amazon S3, and loads the data to Amazon Redshift, the company can perform the required ETL tasks with a single job. By using an AWS Glue workflow to orchestrate the AWS Glue job, the company can schedule and monitor the job execution with minimal operational overhead.

NEW QUESTION # 90
An online retail company is migrating its reporting system to AWS. The company's legacy system runs data processing on online transactions using a complex series of nested Apache Hive queries. Transactional data is exported from the online system to the reporting system several times a day. Schemas in the files are stable between updates.
A data analyst wants to quickly migrate the data processing to AWS, so any code changes should be minimized. To keep storage costs low, the data analyst decides to store the data in Amazon S3. It is vital that the data from the reports and associated analytics is completely up to date based on the data in Amazon S3.
Which solution meets these requirements?

A. Use an S3 Select query to ensure that the data is properly updated. Create an AWS Glue Data Catalog to manage the Hive metadata over the S3 Select table. Create an Amazon EMR cluster and use the metadata in the AWS Glue Data Catalog to run Hive processing queries in Amazon EMR.
B. Create an AWS Glue Data Catalog to manage the Hive metadata. Create an Amazon EMR cluster with consistent view enabled. Run emrfs sync before each analytics step to ensure data changes are updated. Create an EMR cluster and use the metadata in the AWS Glue Data Catalog to run Hive processing queries in Amazon EMR.
C. Create an AWS Glue Data Catalog to manage the Hive metadata. Create an AWS Glue crawler over Amazon S3 that runs when data is refreshed to ensure that data changes are updated. Create an Amazon EMR cluster and use the metadata in the AWS Glue Data Catalog to run Hive processing queries in Amazon EMR.
D. Create an Amazon Athena table with CREATE TABLE AS SELECT (CTAS) to ensure data is refreshed from underlying queries against the raw dataset. Create an AWS Glue Data Catalog to manage the Hive metadata over the CTAS table. Create an Amazon EMR cluster and use the metadata in the AWS Glue Data Catalog to run Hive processing queries in Amazon EMR.

Answer: C

NEW QUESTION # 91
An insurance company has raw data in JSON format that is sent without a predefined schedule through an Amazon Kinesis Data Firehose delivery stream to an Amazon S3 bucket. An AWS Glue crawler is scheduled to run every 8 hours to update the schema in the data catalog of the tables stored in the S3 bucket. Data analysts analyze the data using Apache Spark SQL on Amazon EMR set up with AWS Glue Data Catalog as the metastore. Data analysts say that, occasionally, the data they receive is stale. A data engineer needs to provide access to the most up-to-date data.
Which solution meets these requirements?

A. Run the AWS Glue crawler from an AWS Lambda function triggered by an S3:ObjectCreated:* event notification on the S3 bucket.
B. Use Amazon CloudWatch Events with the rate (1 hour) expression to execute the AWS Glue crawler every hour.
C. Create an external schema based on the AWS Glue Data Catalog on the existing Amazon Redshift cluster to query new data in Amazon S3 with Amazon Redshift Spectrum.
D. Using the AWS CLI, modify the execution schedule of the AWS Glue crawler from 8 hours to 1 minute.

Answer: A

Explanation:
Explanation
https://docs.aws.amazon.com/AmazonS3/latest/dev/NotificationHowTo.html "you can use a wildcard (for example, s3:ObjectCreated:*) to request notification when an object is created regardless of the API used"
"AWS Lambda can run custom code in response to Amazon S3 bucket events. You upload your custom code to AWS Lambda and create what is called a Lambda function. When Amazon S3 detects an event of a specific type (for example, an object created event), it can publish the event to AWS Lambda and invoke your function in Lambda. In response, AWS Lambda runs your function."

NEW QUESTION # 92
A marketing company wants to improve its reporting and business intelligence capabilities. During the planning phase, the company interviewed the relevant stakeholders and discovered that:
* The operations team reports are run hourly for the current month's data.
* The sales team wants to use multiple Amazon QuickSight dashboards to show a rolling view of the last
30 days based on several categories.
* The sales team also wants to view the data as soon as it reaches the reporting backend.
* The finance team's reports are run daily for last month's data and once a month for the last 24 months of data.
Currently, there is 400 TB of data in the system with an expected additional 100 TB added every month. The company is looking for a solution that is as cost-effective as possible.
Which solution meets the company's requirements?

A. Store the last 2 months of data in Amazon Redshift and the rest of the months in Amazon S3. Use a long- running Amazon EMR with Apache Spark cluster to query the data as needed. Configure Amazon QuickSight with Amazon EMR as the data source.
B. Store the last 24 months of data in Amazon S3 and query it using Amazon Redshift Spectrum.
Configure Amazon QuickSight with Amazon Redshift Spectrum as the data source.
C. Store the last 2 months of data in Amazon Redshift and the rest of the months in Amazon S3. Set up an external schema and table for Amazon Redshift Spectrum. Configure Amazon QuickSight with Amazon Redshift as the data source.
D. Store the last 24 months of data in Amazon Redshift. Configure Amazon QuickSight with Amazon Redshift as the data source.

Answer: C

NEW QUESTION # 93
......

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