Amazon DAS-C01 Exam Practice Test Instant Access

Question 1

A company wants to use a data lake that is hosted on Amazon S3 to provide analytics services for historical dat

a. The data lake consists of 800 tables but is expected to grow to thousands of tables. More than 50 departments use the tables, and each department has hundreds of users. Different departments need access to specific tables and columns.

Which solution will meet these requirements with the LEAST operational overhead?

ACreate an 1AM role for each department. Use AWS Lake Formation based access control to grant each 1AM role access to specific tables and columns. Use Amazon Athena to analyze the data.

BCreate an Amazon Redshift cluster for each department. Use AWS Glue to ingest into the Redshift cluster only the tables and columns that are relevant to that department. Create Redshift database users. Grant the users access to the relevant department's Redshift cluster. Use Amazon Redshift to analyze the data.

CCreate an 1AM role for each department. Use AWS Lake Formation tag-based access control to grant each 1AM role
access to only the relevant resources. Create LF-tags that are attached to tables and columns. Use Amazon Athena to analyze the data.

DCreate an Amazon EMR cluster for each department. Configure an 1AM service role for each EMR cluster to access

Erelevant S3 files. For each department's users, create an 1AM role that provides access to the relevant EMR cluster. Use Amazon EMR to analyze the data.

Answer : C

Question 2

A company wants to use automatic machine learning (ML) to create and visualize forecasts of complex scenarios and trends.

Which solution will meet these requirements with the LEAST management overhead?

AUse an AWS Glue ML job to transform the data and create forecasts. Use Amazon QuickSight to visualize the data.

BUse Amazon QuickSight to visualize the data. Use ML-powered forecasting in QuickSight to create forecasts.

CUse a prebuilt ML AMI from the AWS Marketplace to create forecasts. Use Amazon QuickSight to visualize the data.

DUse Amazon SageMaker inference pipelines to create and update forecasts. Use Amazon QuickSight to visualize the combined data.

Answer : B

Question 3

A company has an application that ingests streaming dat

a. The company needs to analyze this stream over a 5-minute timeframe to evaluate the stream for anomalies with Random Cut Forest (RCF) and summarize the current count of status codes. The source and summarized data should be persisted for future use.

Which approach would enable the desired outcome while keeping data persistence costs low?

AIngest the data stream with Amazon Kinesis Data Streams. Have an AWS Lambda consumer evaluate the stream, collect the number status codes, and evaluate the data against a previously trained RCF model. Persist the source and results as a time series to Amazon DynamoDB.

BIngest the data stream with Amazon Kinesis Data Streams. Have a Kinesis Data Analytics application evaluate the stream over a 5-minute window using the RCF function and summarize the count of status codes. Persist the source and results to Amazon S3 through output delivery to Kinesis Data Firehose.

CIngest the data stream with Amazon Kinesis Data Firehose with a delivery frequency of I minute or I MB in Amazon S3. Ensure Amazon S3 triggers an event to invoke an AWS Lambda consumer that evaluates the batch data, collects the number status codes, and evaluates the data against a previously trained RCF model. Persist the source and results as a time series to Amazon DynamoDB.

DIngest the data stream with Amazon Kinesis Data Firehose with a delivery frequency of 5 minutes or I MB into Amazon S3. Have a Kinesis Data Analytics application evaluate the stream over a I-minute window using the RCF function and summarize the count of status codes. Persist the results to Amazon S3 through a Kinesis Data Analytics output to an AWS Lambda integration.

Answer : B

Question 4

A marketing company has an application that stores event data in an Amazon RDS database. The company is replicating this data to Amazon Redshift for reporting and

business intelligence (BI) purposes. New event data is continuously generated and ingested into the RDS database throughout the day and captured by a change data

capture (CDC) replication task in AWS Database Migration Service (AWS DMS). The company requires that the new data be replicated to Amazon Redshift in near-real

time.

Which solution meets these requirements?

AUse Amazon Kinesis Data Streams as the destination of the CDC replication task in AWS DMS. Use an AWS Glue streaming job to read changed records from Kinesis Data Streams and perform an upsert into the Redshift cluster.

BUse Amazon S3 as the destination of the CDC replication task in AWS DMS. Use the COPY command to load data into the Redshift cluster.

CUse Amazon DynamoDB as the destination of the CDC replication task in AWS DMS. Use the COPY command to load data into the Redshift cluster.

DUse Amazon Kinesis Data Firehose as the destination of the CDC replication task in AWS DMS. Use an AWS Glue streaming job to read changed records from Kinesis Data Firehose and perform an upsert into the Redshift cluster.

Answer : A

Question 5

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?

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

BEnable automatic workload management.

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

DEnable concurrency scaling for the Dashboard workload queue.

Answer : D

Question 6

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?

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

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

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

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

Answer : C

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 JDBC, Amazon S3, Amazon RDS, etc.

AWS Glue job bookmarks are a feature that helps AWS Glue track data that has already been processed during a previous run of an ETL job.This can prevent the reprocessing of old data and enable the processing of new data when rerunning on a scheduled interval2. Job bookmarks can handle both baseline data and incremental data from different sources.

Amazon S3 is a highly scalable, durable, and secure object storage service that can store any amount and type of data3. It can be used as a data lake to store the merged and processed datasets from AWS Glue. It can also integrate with other AWS services, such as Amazon Athena, Amazon Redshift Spectrum, Amazon EMR, etc., for further analysis and processing.

Question 7

A financial company uses Amazon Athena to query data from an Amazon S3 data lake. Files are stored in the S3 data lake in Apache ORC format. Data analysts recently introduced nested fields in the data lake ORC files, and noticed that queries are taking longer to run in Athen

a. A data analysts discovered that more data than what is required is being scanned for the queries.

What is the MOST operationally efficient solution to improve query performance?

AFlatten nested data and create separate files for each nested dataset.

BUse the Athena query engine V2 and push the query filter to the source ORC file.

CUse Apache Parquet format instead of ORC format.

DRecreate the data partition strategy and further narrow down the data filter criteria.

Answer : B

This solution meets the requirement because:

The Athena query engine V2 is a new version of the Athena query engine that introduces several improvements and new features, such as federated queries, geospatial functions, prepared statements, schema evolution support, and more1.

One of the improvements of the Athena query engine V2 is that it supports predicate pushdown for nested fields in ORC files. Predicate pushdown is a technique that allows filtering data at the source before it is scanned and loaded into memory.This can reduce the amount of data scanned and processed by Athena, which can improve query performance and reduce cost12.

By using the Athena query engine V2 and pushing the query filter to the source ORC file, the data analysts can leverage the predicate pushdown feature for nested fields and avoid scanning more data than what is required for the queries. This can improve query performance without changing the data format or partitioning strategy.

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