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Guide for AWS Certified Data Analytics - Specialty

Quiz AWS Certified Data Analytics - Specialty

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Quiz

1/10
A financial company hosts a data lake in Amazon S3 and a data warehouse on an Amazon Redshift
cluster. The company uses Amazon QuickSight to build dashboards and wants to secure access from
its on-premises Active Directory to Amazon QuickSight.

How should the data be secured?
Select the answer
1 correct answer
A.
Use an Active Directory connector and single sign-on (SSO) in a corporate network environment.
B.
Use a VPC endpoint to connect to Amazon S3 from Amazon QuickSight and an IAM role to
authenticate Amazon Redshift.
C.
Establish a secure connection by creating an S3 endpoint to connect Amazon QuickSight and a VPC
endpoint to connect to Amazon Redshift.
D.
Place Amazon QuickSight and Amazon Redshift in the security group and use an Amazon S3
endpoint to connect Amazon QuickSight to Amazon S3.

Quiz

2/10
A financial services company needs to aggregate daily stock trade data from the exchanges into a
data store. The company requires that data be streamed directly into the data store, but also
occasionally allows data to be modified using SQL. The solution should integrate complex, analytic
queries running with minimal latency. The solution must provide a business intelligence dashboard
that enables viewing of the top contributors to anomalies in stock prices.

Which solution meets the company’s requirements?
Select the answer
1 correct answer
A.
Use Amazon Kinesis Data Firehose to stream data to Amazon S3. Use Amazon Athena as a data
source for Amazon QuickSight to create a business intelligence dashboard.
B.
Use Amazon Kinesis Data Streams to stream data to Amazon Redshift. Use Amazon Redshift as a
data source for Amazon QuickSight to create a business intelligence dashboard.
C.
Use Amazon Kinesis Data Firehose to stream data to Amazon Redshift. Use Amazon Redshift as a
data source for Amazon QuickSight to create a business intelligence dashboard.
D.
Use Amazon Kinesis Data Streams to stream data to Amazon S3. Use Amazon Athena as a data
source for Amazon QuickSight to create a business intelligence dashboard.

Quiz

3/10
A real estate company has a mission-critical application using Apache HBase in Amazon EMR.
Amazon EMR is configured with a single master node. The company has over 5 TB of data stored on
an Hadoop Distributed File System (HDFS). The company wants a cost-effective solution to make its
HBase data highly available.

Which architectural pattern meets company’s requirements?
Select the answer
1 correct answer
A.
Use Spot Instances for core and task nodes and a Reserved Instance for the EMR master node.
Configure
the EMR cluster with multiple master nodes. Schedule automated snapshots using Amazon
EventBridge.
B.
Store the data on an EMR File System (EMRFS) instead of HDFS. Enable EMRFS consistent view.
Create an EMR HBase cluster with multiple master nodes. Point the HBase root directory to an
Amazon S3 bucket.
C.
Store the data on an EMR File System (EMRFS) instead of HDFS and enable EMRFS consistent view.
Run two separate EMR clusters in two different Availability Zones. Point both clusters to the same
HBase root directory in the same Amazon S3 bucket.
D.
Store the data on an EMR File System (EMRFS) instead of HDFS and enable EMRFS consistent view.
Create a primary EMR HBase cluster with multiple master nodes. Create a secondary EMR HBase
read- replica cluster in a separate Availability Zone. Point both clusters to the same HBase root
directory in the same Amazon S3 bucket.

Quiz

4/10
A software company hosts an application on AWS, and new features are released weekly. As part of
the application testing process, a solution must be developed that analyzes logs from each Amazon
EC2 instance to ensure that the application is working as expected after each deployment. The
collection and analysis solution should be highly available with the ability to display new information
with minimal delays.

Which method should the company use to collect and analyze the logs?
Select the answer
1 correct answer
A.
Enable detailed monitoring on Amazon EC2, use Amazon CloudWatch agent to store logs in
Amazon S3, and use Amazon Athena for fast, interactive log analytics.
B.
Use the Amazon Kinesis Producer Library (KPL) agent on Amazon EC2 to collect and send data to
Kinesis Data Streams to further push the data to Amazon Elasticsearch Service and visualize using
Amazon QuickSight.
C.
Use the Amazon Kinesis Producer Library (KPL) agent on Amazon EC2 to collect and send data to
Kinesis Data Firehose to further push the data to Amazon Elasticsearch Service and Kibana.
D.
Use Amazon CloudWatch subscriptions to get access to a real-time feed of logs and have the logs
delivered to Amazon Kinesis Data Streams to further push the data to Amazon Elasticsearch Service
and Kibana.

Quiz

5/10
A data analyst is using AWS Glue to organize, cleanse, validate, and format a 200 GB dataset. The
data analyst triggered the job to run with the Standard worker type. After 3 hours, the AWS Glue job
status is still RUNNING. Logs from the job run show no error codes. The data analyst wants to
improve the job execution time without overprovisioning.

Which actions should the data analyst take?
Select the answer
1 correct answer
A.
Enable job bookmarks in AWS Glue to estimate the number of data processing units (DPUs). Based
on the profiled metrics, increase the value of the executor-cores job parameter.
B.
Enable job metrics in AWS Glue to estimate the number of data processing units (DPUs). Based on
the
profiled metrics, increase the value of the maximum capacity job parameter.
C.
Enable job metrics in AWS Glue to estimate the number of data processing units (DPUs). Based on
the profiled metrics, increase the value of the spark.yarn.executor.memoryOverhead job parameter.
D.
Enable job bookmarks in AWS Glue to estimate the number of data processing units (DPUs). Based
on the profiled metrics, increase the value of the num-executors job parameter.

Quiz

6/10
A company has a business unit uploading .csv files to an Amazon S3 bucket. The company’s data
platform team has set up an AWS Glue crawler to do discovery, and create tables and schemas. An
AWS Glue job writes processed data from the created tables to an Amazon Redshift database. The

AWS Glue job handles column mapping and creating the Amazon Redshift table appropriately. When
the AWS Glue job is rerun for any reason in a day, duplicate records are introduced into the Amazon
Redshift table.

Which solution will update the Redshift table without duplicates when jobs are rerun?
Select the answer
1 correct answer
A.
Modify the AWS Glue job to copy the rows into a staging table. Add SQL commands to replace the
existing rows in the main table as postactions in the DynamicFrameWriter class.
B.
Load the previously inserted data into a MySQL database in the AWS Glue job. Perform an upsert
operation in MySQL, and copy the results to the Amazon Redshift table.
C.
Use Apache Spark’s DataFrame dropDuplicates() API to eliminate duplicates and then write the
data to Amazon Redshift.
D.
Use the AWS Glue ResolveChoice built-in transform to select the most recent value of the column.

Quiz

7/10
A streaming application is reading data from Amazon Kinesis Data Streams and immediately writing
the data to an Amazon S3 bucket every 10 seconds. The application is reading data from hundreds of
shards. The batch interval cannot be changed due to a separate requirement. The data is being
accessed by Amazon Athen
a. Users are seeing degradation in query performance as time progresses.

Which action can help improve query performance?
Select the answer
1 correct answer
A.
Merge the files in Amazon S3 to form larger files.
B.
Increase the number of shards in Kinesis Data Streams.
C.
Add more memory and CPU capacity to the streaming application.
D.
Write the files to multiple S3 buckets.

Quiz

8/10
A company uses Amazon Elasticsearch Service (Amazon ES) to store and analyze its website
clickstream dat
a. The company ingests 1 TB of data daily using Amazon Kinesis Data Firehose and stores one day’s

worth of data in an Amazon ES cluster.

The company has very slow query performance on the Amazon ES index and occasionally sees errors
from Kinesis Data Firehose when attempting to write to the index. The Amazon ES cluster has 10
nodes running a single index and 3 dedicated master nodes. Each data node has 1.5 TB of Amazon
EBS storage attached and the cluster is configured with 1,000 shards. Occasionally,
JVMMemoryPressure errors are found in the cluster logs.

Which solution will improve the performance of Amazon ES?
Select the answer
1 correct answer
A.
Increase the memory of the Amazon ES master nodes.
B.
Decrease the number of Amazon ES data nodes.
C.
Decrease the number of Amazon ES shards for the index.
D.
Increase the number of Amazon ES shards for the index.

Quiz

9/10
A manufacturing company has been collecting IoT sensor data from devices on its factory floor for a
year and is storing the data in Amazon Redshift for daily analysis. A data analyst has determined that,
at an expected ingestion rate of about 2 TB per day, the cluster will be undersized in less than 4
months. A long-term solution is needed. The data analyst has indicated that most queries only
reference the most recent 13 months of data, yet there are also quarterly reports that need to query
all the data generated from the past 7 years. The chief technology officer (CTO) is concerned about
the costs, administrative effort, and performance of a long-term solution.

Which solution should the data analyst use to meet these requirements?
Select the answer
1 correct answer
A.
Create a daily job in AWS Glue to UNLOAD records older than 13 months to Amazon S3 and delete
those records from Amazon Redshift. Create an external table in Amazon Redshift to point to the S3
location. Use Amazon Redshift Spectrum to join to data that is older than 13 months.
B.
Take a snapshot of the Amazon Redshift cluster. Restore the cluster to a new cluster using dense
storage nodes with additional storage capacity.
C.
Execute a CREATE TABLE AS SELECT (CTAS) statement to move records that are older than 13
months to quarterly partitioned data in Amazon Redshift Spectrum backed by Amazon S3.
D.
Unload all the tables in Amazon Redshift to an Amazon S3 bucket using S3 Intelligent-Tiering. Use
AWS Glue to crawl the S3 bucket location to create external tables in an AWS Glue Data Catalog.
Create an Amazon EMR cluster using Auto Scaling for any daily analytics needs, and use Amazon
Athena for the quarterly reports, with both using the same AWS Glue Data Catalog.

Quiz

10/10
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?
Select the answer
1 correct answer
A.
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.
B.
Use Amazon CloudWatch Events with the rate (1 hour) expression to execute the AWS Glue
crawler every hour.
C.
Using the AWS CLI, modify the execution schedule of the AWS Glue crawler from 8 hours to 1
minute.
D.
Run the AWS Glue crawler from an AWS Lambda function triggered by an S3:ObjectCreated:*
event notification on the S3 bucket.
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  • Quiz name:AWS Certified Data Analytics - Specialty
  • Total number of questions:207
  • Number of questions for the test:50
  • Pass score:80%

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