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Question 1:
A company stores its sales data in JSON format in Amazon S3. The data engineering team needs to use Amazon Athena to run ad-hoc queries on this data. They want to create a structured view of the data for easier querying. What is the first step the team should take in Athena?
A. Execute a CREATE DATABASE command in Athena to store the structured view.
B. Use a CREATE EXTERNAL TABLE command in Athena to define the schema of the JSON data.
C. Implement an AWS Glue crawler to convert the JSON data to a CSV format for Athena.
D. Configure an AWS Data Pipeline job to transform and load the data into Amazon Redshift for querying.
Answer: B
Explanation:
Correct answer: B. Use a CREATE EXTERNAL TABLE command in Athena to define the schema of the JSON data.
The first step in querying JSON data in Amazon S3 using Athena is to define the schema of the data. This is achieved by using a CREATE EXTERNAL TABLE command in Athena. This command allows Athena to understand the structure of the JSON data, enabling efficient querying.
Option A is incorrect. Creating a database is a prerequisite step, but the first action specific to querying JSON data is defining the schema with a CREATE EXTERNAL TABLE command.
Option C is incorrect. Using an AWS Glue crawler to convert the format is unnecessary for querying JSON data in Athena, as Athena can query JSON format directly.
Option D is incorrect. Configuring an AWS Data Pipeline job to move data to Amazon Redshift is beyond the scope of querying data directly in S3 using Athena.
Question 2:
A financial services company is preparing for a regulatory audit and needs to provide detailed logs of all data processing activities within their AWS environment. Which AWS service should they use to extract these logs for the audit?
A. Use AWS CloudTrail to extract logs of all data processing activities.
B. Implement Amazon CloudWatch to generate detailed operational logs.
C. Configure Amazon Inspector to provide security assessment logs.
D. Utilize AWS Config to extract configuration change logs.
Answer: A
Explanation:
Correct answer: A. Use AWS CloudTrail to extract logs of all data processing activities.
AWS CloudTrail is specifically designed to log and monitor actions across AWS accounts, including data processing activities. It provides detailed information about user activities, API calls, and resource accesses, making it an ideal tool for extracting logs needed for regulatory audits.
Option B is incorrect. Amazon CloudWatch primarily focuses on performance monitoring and operational metrics rather than providing detailed logs of user activities and data processing.
Option C is incorrect. Amazon Inspector is used for security assessments and vulnerability management, not for logging general data processing activities.
Option D is incorrect. AWS Config tracks resource configurations and changes but doesn't provide comprehensive logs of all data processing activities.
Question 3:
A data analyst is using Amazon Athena to analyze web traffic data stored in Amazon S3. The requirement is to create a persistent summary view that aggregates daily traffic data by region. Which approach should the analyst use in Athena to achieve this?
A. Use a CREATE TABLE AS SELECT (CTAS) statement to aggregate and store the data in a new table.
B. Implement an AWS Lambda function to preprocess the data in S3 before querying with Athena.
C. Apply a CREATE VIEW statement in Athena to create a summary view of the daily traffic by region.
D. Configure AWS Glue DataBrew to aggregate the data and then query the results with Athena.
Answer: C
Explanation:
Correct answer: C. Apply a CREATE VIEW statement in Athena to create a summary view of the daily traffic by region.
Creating a view in Athena with a CREATE VIEW statement is an effective way to create a persistent, logical representation of the aggregated data. This approach allows the analyst to easily query daily traffic data by region without physically altering or moving the underlying data in S3.
Option A is incorrect. A CTAS statement creates a new physical table, which might be unnecessary for the requirement of simply aggregating data for querying.
Option B is incorrect. Using AWS Lambda for preprocessing is an indirect method and not as efficient as creating a view directly in Athena.
Option D is incorrect. While AWS Glue DataBrew can be used for data preparation, it’s not necessary for this task where a simple view in Athena suffices.
Question 4:
An online media company stores a vast amount of video content. New content is accessed frequently, but older content is only occasionally accessed. The company seeks a cost-effective storage solution that aligns with this lifecycle. What storage strategy should they adopt?
A. Store all video content in Amazon S3 Glacier.
B. Use Amazon S3 Standard for new content and transition to S3 Standard-Infrequent Access (IA) for older content.
C. Maintain all content in Amazon EBS for constant availability.
D. Utilize Amazon RDS for storing all video content.
Answer: B
Explanation:
Correct answer: B. Use Amazon S3 Standard for new content and transition to S3 Standard-Infrequent Access (IA) for older content.
Storing new content in Amazon S3 Standard (for frequent access) and transitioning older content to S3 Standard-Infrequent Access (IA) is a cost-effective strategy. S3 Standard-IA offers lower storage costs for data that is less frequently accessed, matching the company’s access patterns for older content.
Option A is incorrect. Amazon S3 Glacier is designed for long-term archival and is not optimal for new content that is accessed frequently.
Option C is incorrect. Amazon EBS provides block storage for EC2 instances and is typically more expensive for storing large media files, especially those not accessed frequently.
Option D is incorrect. Amazon RDS is a database service and not suitable for cost-effective storage of large volumes of video content.
Question 5:
Your team is responsible for managing a complex AWS data infrastructure that includes multiple resources like Amazon S3 buckets, Amazon Redshift clusters, and AWS Glue ETL jobs. You want to ensure that the infrastructure is defined as code and can be consistently provisioned and updated across different environments. Which AWS service allows you to achieve this goal by defining your infrastructure as code using JSON or YAML templates?
A. AWS Lambda
B. AWS Glue
C. AWS Cloud Development Kit (AWS CDK)
D. AWS CloudFormation
Answer: D
Explanation:
Correct answer: D. AWS CloudFormation
AWS CloudFormation allows you to define your infrastructure as code using JSON or YAML templates. You can specify all the AWS resources and their configurations in a template, and CloudFormation will provision and manage those resources for you in a consistent and repeatable manner.
Option A is incorrect. AWS Lambda is a compute service for running code in response to events and is not primarily used for defining infrastructure.
Option B is incorrect. AWS Glue is an ETL service for data preparation and transformation and is not used for defining infrastructure as code.
Option C is incorrect. AWS Cloud Development Kit (AWS CDK) is another option for defining infrastructure as code, but in this question, we are specifically looking for a service that uses JSON or YAML templates, which is AWS CloudFormation’s approach.
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Question 6:
A data analyst is using Athena notebooks with Apache Spark to explore a large dataset stored in Amazon S3. The dataset is in CSV format and contains complex nested structures. The analyst needs to flatten the data for easier analysis. What should be the first step in the Athena notebook using Spark?
A. Use a Spark SQL CREATE TABLE statement to import the CSV data into a flat table structure.
B. Execute a PySpark script in the notebook to read the CSV file and flatten the nested structures.
C. Configure an AWS Glue ETL job to preprocess the data and flatten the structure before analysis.
D. Apply an Amazon Athena query in the notebook to convert the CSV data to a JSON format.
Answer: B
Explanation:
Correct answer: B. Execute a PySpark script in the notebook to read the CSV file and flatten the nested structures.
Using PySpark in an Athena notebook with Apache Spark is an effective way to read and process complex data formats, such as a CSV with nested structures. The PySpark script can be used to read the data and programmatically flatten the nested structures, simplifying the dataset for further analysis.
Option A is incorrect. A Spark SQL CREATE TABLE statement creates a new table but doesn’t inherently flatten complex nested structures in a CSV file.
Option C is incorrect. While AWS Glue can preprocess and flatten data, this task can be directly accomplished within the Athena notebook using Spark, making it more efficient for ad-hoc analysis.
Option D is incorrect. Converting CSV data to JSON format does not address the need to flatten nested structures and is not a step typically performed in an Athena notebook with Apache Spark for this purpose.
Question 7:
A financial institution is required to retain customer transaction records for seven years and make them available for auditing during this period. What is the most suitable AWS service or feature for managing these records?
A. Use Amazon S3 with lifecycle policies to transition records to Amazon S3 Glacier after one year.
B. Store the records in Amazon RDS and regularly create manual backups.
C. Configure Amazon Redshift to store transaction records and archive them using Spectrum.
D. Use Amazon EBS volumes to store records and manually move older data to an S3 bucket.
Answer: A
Explanation:
Correct answer: A. Use Amazon S3 with lifecycle policies to transition records to Amazon S3 Glacier after one year.
This approach allows the financial institution to store transaction records in Amazon S3, making them available for auditing. Using S3 lifecycle policies to transition records to Amazon S3 Glacier after one year ensures long-term retention while reducing storage costs.
Option B is incorrect. Regularly creating manual backups in Amazon RDS may not be the most efficient way to manage long-term retention.
Option C is incorrect. While Amazon Redshift is suitable for data warehousing, using Spectrum for archiving transaction records is not a common use case.
Option D is incorrect. Using Amazon EBS volumes for long-term retention and manually moving data to an S3 bucket is not a recommended approach for managing data lifecycle and compliance requirements.
Question 8:
A company hosts a critical database on Amazon RDS for MySQL to support its e-commerce platform. To ensure data resiliency and availability, which AWS service or feature should the company implement?
A. Use Amazon S3 to regularly back up the database.
B. Enable Multi-AZ (Availability Zone) deployment for the Amazon RDS instance.
C. Store database backups in Amazon Redshift for fast recovery.
D. Use Amazon Elastic Block Store (EBS) snapshots for backup.
Answer: B
Explanation:
Correct answer: B. Enable Multi-AZ (Availability Zone) deployment for the Amazon RDS instance.
Enabling Multi-AZ deployment for Amazon RDS ensures high availability and data resiliency by replicating the database across multiple Availability Zones.
Option A is incorrect. While Amazon S3 can be used for backup storage, it doesn’t provide Multi-AZ redundancy for RDS instances.
Option C is incorrect. Amazon Redshift is a data warehousing service, and storing database backups in it is not a common practice for e-commerce platforms.
Option D is incorrect. Amazon EBS snapshots are not a direct solution for ensuring the high availability of an Amazon RDS database.
Question 9:
A company is using Amazon EMR for big data processing and has integrated it with AWS Lake Formation for enhanced data security and governance. They want to ensure that specific data analysts have access to only certain datasets managed by Lake Formation. How should they configure permissions to achieve this?
A. Implement Network Access Control Lists (NACLs) to restrict EMR cluster access to specific datasets.
B. Grant permissions using IAM policies attached to the data analysts‘ IAM roles for accessing specific datasets in Lake Formation.
C. Use Lake Formation to define and grant fine-grained data access permissions for the data analysts on the specific datasets.
D. Store datasets in separate Amazon S3 buckets and use S3 bucket policies to control access by the data analysts.
Answer: C
Explanation:
Correct answer: C. Use Lake Formation to define and grant fine-grained data access permissions for the data analysts on the specific datasets.
Lake Formation allows for fine-grained access control to datasets. By using Lake Formation, the company can define and grant specific data access permissions to individual data analysts or groups, ensuring they have access only to the datasets necessary for their tasks. This integrates well with Amazon EMR for managing data access within the big data processing environment.
Option A is incorrect. NACLs are used for network-level traffic control and are not suitable for managing data access permissions within Lake Formation and EMR.
Option B is incorrect. While IAM policies are critical for overall AWS security, Lake Formation provides more granular control for data access within its managed datasets, which is more suitable in this scenario.
Option D is incorrect. Separating datasets into different S3 buckets and using bucket policies is less efficient and granular compared to using Lake Formation for dataset-level permission management in conjunction with EMR.
Question 10:
An AWS data pipeline using Amazon Redshift is experiencing slower query response times than usual. What is the first action the data engineer should take to identify the cause of the slowdown?
A. Review the Amazon Redshift query execution plans to identify inefficient queries.
B. Increase the size or number of nodes in the Amazon Redshift cluster.
C. Check the CPU utilization metrics in Amazon CloudWatch for the Redshift cluster.
D. Examine network bandwidth usage to see if there‘s congestion impacting Redshift performance.
Answer: A
Explanation:
Correct answer: A. Review the Amazon Redshift query execution plans to identify inefficient queries.
The first step in addressing slow query performance in Amazon Redshift is to examine the query execution plans. This can reveal inefficient queries or suboptimal query plans, which are common causes of performance issues in databases. Identifying and optimizing these queries is often the most direct way to improve performance.
Option B is incorrect. Increasing the size or number of nodes might improve performance but should be considered after examining query efficiency, as it involves additional cost and complexity.
Option C is incorrect. While CPU utilization is an important metric, it’s more effective first to look at query execution plans to understand specific query performance issues.
Option D is incorrect. Network bandwidth could affect performance, but slow query response times are more commonly related to query efficiency and execution plans in Redshift.
For a full set of 880 questions. Go to
https://skillcertpro.com/product/aws-data-engineer-associate-dea-c01-exam-questions/
SkillCertPro offers detailed explanations to each question which helps to understand the concepts better.
It is recommended to score above 85% in SkillCertPro exams before attempting a real exam.
SkillCertPro updates exam questions every 2 weeks.
You will get life time access and life time free updates
SkillCertPro assures 100% pass guarantee in first attempt.