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DAS-C01 Online Practice Questions

AWS Certified Data Analytics - Specialty (DAS-C01)

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Question 1 Single choice

An online gaming company is using an Amazon Kinesis Data Analytics SQL application with a Kinesis data stream as its source. The source sends three non-null fields to the application: player_id, score, and
us_5_digit_zip_code.

A data analyst has a .csv mapping file that maps a small number of us_5_digit_zip_code values to a territory code. The data analyst needs to include the territory code, if one exists, as an additional output of the Kinesis Data Analytics application.

How should the data analyst meet this requirement while minimizing costs?

  1. A

    Store the contents of the mapping file in an Amazon DynamoDB table. Preprocess the records as they arrive in the Kinesis Data Analytics application with an AWS Lambda function that fetches the mapping

    and supplements each record to include the territory code, if one exists. Change the SQL query in the application to include the new field in the SELECT statement.

  2. B

    Store the mapping file in an Amazon S3 bucket and configure the reference data column headers for the .csv file in the Kinesis Data Analytics application. Change the SQL query in the application to include a join to the file's S3 Amazon Resource Name (ARN), and add the territory code field to the SELECT columns.

  3. C

    Store the mapping file in an Amazon S3 bucket and configure it as a reference data source for the Kinesis Data Analytics application. Change the SQL query in the application to include a join to the reference table and add the territory code field to the SELECT columns.

  4. D

    Store the contents of the mapping file in an Amazon DynamoDB table. Change the Kinesis Data Analytics application to send its output to an AWS Lambda function that fetches the mapping and supplements each record to include the territory code, if one exists. Forward the record from the Lambda function to the original application destination.

Show answer and explanation

Correct answer: C

Explanation

Correct answer is C as Kinesis Data Analytics allows adding S3 source for reference data which can be referred by Kinesis Data Analytics for data enrichment. Kinesis Data Analytics stores it as an in-application reference table.

Options A & D are wrong as they are not cost-effective.

Option B is wrong as Kinesis Data Analytics stores the reference data as an in-application reference table.

Question 2 Single choice

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?

  1. A

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

  2. B

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

  3. C

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

  4. D

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

Show answer and explanation

Correct answer: C

Question 3 Multiple choice

An airline has been collecting metrics on flight activities for analytics. A recently completed proof of concept demonstrates how the company provides insights to data analysts to improve on-time departures.
The proof of concept used objects in Amazon S3, which contained the metrics in .csv format, and used Amazon Athena for querying the data. As the amount of data increases, the data analyst wants to optimize the storage solution to improve query performance.

Which options should the data analyst use to improve performance as the data lake grows? (Choose three.)

  1. A

    Add a randomized string to the beginning of the keys in S3 to get more throughput across partitions.

  2. B

    Use an S3 bucket in the same account as Athena.

  3. C

    Compress the objects to reduce the data transfer I/O.

  4. D

    Use an S3 bucket in the same Region as Athena.

  5. E

    Preprocess the .csv data to JSON to reduce I/O by fetching only the document keys needed by the query.

  6. F

    Preprocess the .csv data to Apache Parquet to reduce I/O by fetching only the data blocks needed for predicates.

Show answer and explanation

Correct answers: C, D, F

Explanation

Correct answers are C, D & F

Options C & F as using compression and columnar data format helps improve query performance and optimize storage Option D as using Athena and S3 within the same region would help with query performance and cost.

Option A is wrong as S3 scales automatically now and is not bounded by the restriction.

Option B is wrong as using the same account does not help in optimizing the cost of query performance.

Option E is wrong as using JSON is the same as using CSV files and does help in n optimizing the cost or query performance.

Question 4 Single choice

A marketing company collects data from third-party providers and uses transient Amazon EMR clusters to process this data. The company wants to host an Apache Hive metastore that is persistent, reliable, and can be accessed by EMR clusters and multiple AWS services and accounts simultaneously. The metastore must also be available at all times.

Which solution meets these requirements with the LEAST operational overhead?

  1. A

    Use AWS Glue Data Catalog as the metastore

  2. B

    Use an external Amazon EC2 instance running MySQL as the metastore

  3. C

    Use Amazon RDS for MySQL as the metastore

  4. D

    Use Amazon S3 as the metastore

Show answer and explanation

Correct answer: A

Explanation

References:
https://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-hive-metastore-glue.html

Question 5 Single choice

A company wants to use an automatic machine learning (ML) Random Cut Forest (RCF) algorithm to visualize complex real-world scenarios, such as detecting seasonality and trends, excluding outers, and imputing missing values.

The team working on this project is non-technical and is looking for an out-of-the-box solution that will require the LEAST amount of management overhead.

Which solution will meet these requirements?

  1. A

    Use an AWS Glue ML transform to create a forecast and then use Amazon QuickSight to visualize the data.

  2. B

    Use Amazon QuickSight to visualize the data and then use ML-powered forecasting to forecast the key business metrics.

  3. C

    Use a pre-build ML AMI from the AWS Marketplace to create forecasts and then use Amazon QuickSight to visualize the data.

  4. D

    Use calculated fields to create a new forecast and then use Amazon QuickSight to visualize the data.

Show answer and explanation

Correct answer: B

Explanation

References:
https://aws.amazon.com/blogs/big-data/query-visualize-and-forecast-trufactor-web-session-intelligence-with-aws-data-exchange/

Question 6 Multiple choice

An analytics team uses Amazon OpenSearch Service for an analytics API to be used by data analysts.
The OpenSearch Service cluster is configured with three master nodes. The analytics team uses Amazon Managed Streaming for Apache Kafka (Amazon MSK) and a customized data pipeline to ingest and store 2 months of data in an OpenSearch Service cluster. The cluster stopped responding, which is regularly causing timeout requests. The analytics team discovers the cluster is handling too many bulk indexing requests.

Which actions would improve the performance of the OpenSearch Service cluster? (Choose two.)

  1. A

    Reduce the number of API bulk requests on the OpenSearch Service cluster and reduce the size of each bulk request.

  2. B

    Scale out the OpenSearch Service cluster by increasing the number of nodes.

  3. C

    Reduce the number of API bulk requests on the OpenSearch Service cluster, but increase the size of each bulk request

  4. D

    Increase the number of master nodes for the OpenSearch Service cluster

  5. E

    Scale down the pipeline component that is used to ingest the data into the OpenSearch Service cluster.

Show answer and explanation

Correct answers: A, B

Question 7 Multiple choice

A retail company is using an Amazon S3 bucket to host an ecommerce data lake. The company is using AWS Lake Formation to manage the data lake.

A data analytics specialist must provide access to a new business analyst team. The team will use

Amazon Athena from the AWS Management Console to query data from existing web_sales and customer tables in the ecommerce database. The team needs read-only access and the ability to uniquely identify customers by using first and last names. However, the team must not be able to see any other personally identifiable data. The table structure is as follows:

Which combination of steps should the data analytics specialist take to provide the required permission by using the principle of least privilege? (Choose three.)

  1. A

    In AWS Lake Formation, grant the business_analyst group SELECT and ALTER permissions for the web_sales table.

  2. B

    In AWS Lake Formation, grant the business_analyst group the SELECT permission for the web_sales table.

  3. C

    In AWS Lake Formation, grant the business_analyst group the SELECT permission for the customer table. Under columns, choose filter type "Include columns" with columns fisrt_name, last_name, and customer_id.

  4. D

    In AWS Lake Formation, grant the business_analyst group SELECT and ALTER permissions for the customer table. Under columns, choose filter type "Include columns" with columns fisrt_name and last_name.

  5. E

    Create users under a business_analyst IAM group. Create a policy that allows the lakeformation:GetDataAccess action, the athena:* action, and the glue:Get* action.

  6. F

    Create users under a business_analyst IAM group. Create a policy that allows the lakeformation:GetDataAccess action, the athena:* action, and the glue:Get* action. In addition, allow the s3:GetObject action, the s3:PutObject action, and the s3:GetBucketLocation action for the Athena
    query results S3 bucket.

Show answer and explanation

Correct answers: B, C, F

Explanation

A - WRONG, ALTER grants write access to raw data.
B - CORRECT, SELECT grants read access to the transaction data, where no customer information is present.
C - CORRECT, Lake Formation allows specifying which columns are accessible. customer_id is needed in order to join with web_sales table.
D - WRONG, ALTER grants write access to raw data.
E - WRONG, s3:GetObject is needed to see Athena results (
https://docs.aws.amazon.com/athena/latest/ug/querying.html
).
F - CORRECT, even though it's Athena that puts the objects in the bucket and not the IAM user itself, granting s3:PutObject on the Athena bucket doesn't provide for the original S3 bucket where raw data resides.

Question 8 Single choice

A utility company wants to visualize data for energy usage on a daily basis in Amazon QuickSight A data analytics specialist at the company has built a data pipeline to collect and ingest the data into Amazon S3
Each day the data is stored in an individual csv file in an S3 bucket This is an example of the naming structure 20210707_datacsv 20210708_datacsv.

To allow for data querying in QuickSight through Amazon Athena the specialist used an AWS Glue crawler to create a table with the path "s3 //powertransformer/20210707_data csv" However when the data is
queried, it returns zero rows.

How can this issue be resolved?

  1. A

    Modify the IAM policy for the AWS Glue crawler to access Amazon S3.

  2. B

    Ingest the files again.

  3. C

    Store the files in Apache Parquet format.

  4. D

    Update the table path to "s3://powertransformer/".

Show answer and explanation

Correct answer: D

Explanation

References:
https://aws.amazon.com/premiumsupport/knowledge-center/find-s3-source-file-athena-table-row/

Question 9 Single choice

An IoT company is collecting data from multiple sensors and is streaming the data to Amazon Managed Streaming for Apache Kafka (Amazon MSK). Each sensor type has its own topic, and each topic has the same number of partitions.

The company is planning to turn on more sensors. However, the company wants to evaluate which sensor types are producing the most data so that the company can scale accordingly. The company needs to know which sensor types have the largest values for the following metrics: BytesInPerSec and MessagesInPerSec.

Which level of monitoring for Amazon MSK will meet these requirements?

  1. A

    DEFAULT level

  2. B

    PER_TOPIC_PER_BROKER level

  3. C

    PER_BROKER level

  4. D

    PER_TOPIC level

Show answer and explanation

Correct answer: D

Question 10 Single choice

A global company has different sub-organizations, and each sub-organization sells its products and services in various countries. The company's senior leadership wants to quickly identify which sub-organization is the strongest performer in each country. All sales data is stored in Amazon S3 in Parquet format.

Which approach can provide the visuals that senior leadership requested with the least amount of effort?

  1. A

    Use Amazon QuickSight with Amazon Athena as the data source. Use heat maps as the visual type.

  2. B

    Use Amazon QuickSight with Amazon S3 as the data source. Use heat maps as the visual type.

  3. C

    Use Amazon QuickSight with Amazon Athena as the data source. Use pivot tables as the visual type.

  4. D

    Use Amazon QuickSight with Amazon S3 as the data source. Use pivot tables as the visual type.

Show answer and explanation

Correct answer: A

Explanation

QuickSight does not support S3 files with parquet format, Athena does it. For visualization is better a graph than a pivot table.
https://docs.aws.amazon.com/athena/latest/ug/when-should-i-use-ate.html
https://docs.aws.amazon.com/quicksight/latest/user/supported-data-sources.html