Amazon DATA-ENGINEER-ASSOCIATE Online Practice Questions and Exam Preparation

Amazon DATA-ENGINEER-ASSOCIATE Online Questions & Answers

• Question 11:

  How to separate query processes and access to query history for users/teams in the same AWS account using Amazon Athena?

  A. Create an S3 bucket per use case with bucket policies
  B. Create an Athena workgroup per use case with tagged IAM policies
  C. Create an IAM role per use case and associate with Athena
  D. Use AWS Glue Data Catalog resource policies for individual users
  B. Create an Athena workgroup per use case with tagged IAM policies

• Question 12:

  A company stores petabytes of data in thousands of Amazon S3 buckets in the S3 Standard storage class.

  The data supports analytics workloads that have unpredictable and variable data access patterns.

  The company does not access some data for months. However, the company must be able to retrieve all data within milliseconds. The company needs to optimize S3 storage costs.

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

  A. Use S3 Storage Lens standard metrics to determine when to move objects to more cost-optimized storage classes. Create S3 Lifecycle policies for the S3 buckets to move objects to cost-optimized storage classes. Continue to refine the S3 Lifecycle policies in the future to optimize storage costs.
  B. Use S3 Storage Lens activity metrics to identify S3 buckets that the company accesses infrequently. Configure S3 Lifecycle rules to move objects from S3 Standard to the S3 Standard-Infrequent Access (S3 Standard-IA) and S3 Glacier storage classes based on the age of the data.
  C. Use S3 Intelligent-Tiering. Activate the Deep Archive Access tier.
  D. Use S3 Intelligent-Tiering. Use the default access tier.
  D. Use S3 Intelligent-Tiering. Use the default access tier.

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  Explanation

  S3 Intelligent-Tiering is a storage class that automatically moves objects between four access tiers based on the changing access patterns. The default access tier consists of two tiers: Frequent Access and Infrequent Access. Objects in the Frequent Access tier have the same performance and availability as S3 Standard, while objects in the Infrequent Access tier have the same performance and availability as S3 Standard-IA. S3 Intelligent-Tiering monitors the access patterns of each object and moves them between the tiers accordingly, without any operational overhead or retrieval fees. This solution can optimize S3 storage costs for data with unpredictable and variable access patterns, while ensuring millisecond latency for data retrieval. The other solutions are not optimal or relevant for this requirement. Using S3 Storage Lens standard metrics and activity metrics can provide insights into the storage usage and access patterns, but they do not automate the data movement between storage classes. Creating S3 Lifecycle policies for the S3 buckets can move objects to more cost-optimized storage classes, but they require manual configuration and maintenance, and they may incur retrieval fees for data that is accessed unexpectedly. Activating the Deep Archive Access tier for S3 Intelligent-Tiering can further reduce the storage costs for data that is rarely accessed, but it also increases the retrieval time to 12 hours, which does not meet the requirement of millisecond latency.

• Question 13:

  A company has an application that uses an Amazon API Gateway REST API and an AWS Lambda function to retrieve data from an Amazon DynamoDB instance. Users recently reported intermittent high latency in the application's response times. A data engineer finds that the Lambda function experiences frequent throttling when the company's other Lambda functions experience increased invocations.

  The company wants to ensure the API's Lambda function operates without being affected by other Lambda functions.

  Which solution will meet this requirement MOST cost-effectively?

  A. Increase the number of read capacity unit (RCU) in DynamoDB.
  B. Configure provisioned concurrency for the Lambda function.
  C. Configure reserved concurrency for the Lambda function.
  D. Increase the Lambda function timeout and allocated memory.
  C. Configure reserved concurrency for the Lambda function.

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  Explanation

  Reserved concurrency ensures that a specific Lambda function has a set number of concurrent executions that cannot be throttled due to other Lambda functions consuming shared concurrency limits. By configuring reserved concurrency, the API's Lambda function will always have the necessary resources to execute without being affected by other functions, thereby preventing throttling and ensuring consistent performance.

• Question 14:

  A company needs a solution to automatically ingest data from an Amazon S3 bucket to an Amazon Redshift table every day. The company must obfuscate sensitive data in Amazon Redshift. Users must not be able to view the sensitive data as plaintext.

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

  A. Enable Amazon Macie for the company's AWS account. Configure a sensitive data discovery job for the S3 bucket. Configure an AWS Glue job to load the data into Amazon Redshift.
  B. Enable Amazon Macie for the company's AWS account. Configure an AWS Glue job to flag and obfuscate data columns based on the Macie analysis. Load the data into Amazon Redshift.
  C. Configure an AWS Glue job to read the data from the S3 bucket, detect and obfuscate sensitive data, and load the data into Amazon Redshift.
  D. Configure an AWS Glue job to load the data into Amazon Redshift. Use Amazon Redshift to detect the sensitive data, create masking policies, and apply dynamic masking.
  D. Configure an AWS Glue job to load the data into Amazon Redshift. Use Amazon Redshift to detect the sensitive data, create masking policies, and apply dynamic masking.

• Question 15:

  A company currently uses a provisioned Amazon EMR cluster that includes general purpose Amazon EC2 instances. The EMR cluster uses EMR managed scaling between one to five task nodes for the company's long-running Apache Spark extract, transform, and load (ETL) job. The company runs the ETL job every day.

  When the company runs the ETL job, the EMR cluster quickly scales up to five nodes. The EMR cluster often reaches maximum CPU usage, but the memory usage remains under 30%.

  The company wants to modify the EMR cluster configuration to reduce the EMR costs to run the daily ETL job.

  Which solution will meet these requirements MOST cost-effectively?

  A. Increase the maximum number of task nodes for EMR managed scaling to 10.
  B. Change the task node type from general purpose EC2 instances to memory optimized EC2 instances.
  C. Switch the task node type from general purpose EC2 instances to compute optimized EC2 instances.
  D. Reduce the scaling cooldown period for the provisioned EMR cluster.
  C. Switch the task node type from general purpose EC2 instances to compute optimized EC2 instances.

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  Explanation

  The company's Apache Spark ETL job on Amazon EMR uses high CPU but low memory, meaning that compute-optimized EC2 instances would be the most cost-effective choice. These instances are designed for high-performance compute applications, where CPU usage is high, but memory needs are minimal, which is exactly the case here.

  Compute Optimized Instances:

  Compute-optimized instances, such as the C5 series, provide a higher ratio of CPU to memory, which is more suitable for jobs with high CPU usage and relatively low memory consumption. Switching from general-purpose EC2 instances to compute-optimized instances canreduce costs while improving performance, as these instances are optimized for workloads like Spark jobs that perform a lot of computation.

• Question 16:

  A data engineer at a large company needs to create centralized datasets that are optimized for Amazon Redshift performance. The company has multiple downstream teams that use their own AWS accounts and dedicated Amazon Redshift clusters with RA3 nodes. All downstream teams need access to the centralized datasets.

  Which solution will provide immediate access to the datasets and maintain the current Amazon Redshift performance?

  A. Copy the datasets to an Amazon S3 bucket by using the UNLOAD command. Register the table definitions in a dedicated AWS Glue Data Catalog schema. Share the schema with the other AWS accounts by using AWS Lake Formation. Use Amazon Redshift Spectrum to access the data.
  B. Create a daily extract, transform, and load (ETL) job to unload the data to an Amazon S3 staging area. Instruct the teams to copy the data into their Amazon Redshift clusters.
  C. Set up Amazon Redshift data sharing between the Amazon Redshift producer clusters and the consumer clusters to provide access to the centralized datasets.
  D. Set up an AWS DataSync job that automatically syncs the data between the Amazon Redshift producer clusters and the consumer clusters.
  C. Set up Amazon Redshift data sharing between the Amazon Redshift producer clusters and the consumer clusters to provide access to the centralized datasets.

• Question 17:

  A company is creating a new data pipeline to populate a data lake. A data analyst needs to prepare and standardize the data before a data engineering team can perform advanced data transformations. The data analyst needs a solution to process the data that does not require writing new code.

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

  A. Use Python and Pandas in an AWS Glue Studio notebook. Ensure that the data engineers add additional transformations to complete the pipeline.
  B. Use Amazon SageMaker Canvas and SageMaker Data Wrangler to write to a new dataset. Ensure that the data engineers add additional transformations to complete the pipeline by using AWS Glue.
  C. Use AWS Glue Studio with data preparation recipe transformations. Ensure that the data engineers add additional transformations to complete the pipeline.
  D. Create a document that includes the data preparation rules. Ensure that the data engineers implement the rules in AWS Glue.
  C. Use AWS Glue Studio with data preparation recipe transformations. Ensure that the data engineers add additional transformations to complete the pipeline.

• Question 18:

  A company has three independent applications that consume the same Kinesis data stream. The applications must not compete for read throughput, and one application needs to replay data that arrived during the past 7 days.

  Which Kinesis Data Streams features should the data engineer configure? (Choose two.)

  A. Enhanced fan-out consumers
  B. A stream retention period of at least 7 days
  C. S3 Lifecycle expiration after 7 days
  D. A single shared-throughput consumer for all applications
  E. AWS DMS full load plus CDC
  A. Enhanced fan-out consumers
  B. A stream retention period of at least 7 days

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  Explanation

  Enhanced fan-out gives each registered stream consumer dedicated read throughput. Increasing stream retention keeps records available long enough for replay. S3 Lifecycle does not affect stream records. A single shared-throughput consumer creates contention and does not let each application independently consume all events. AWS DMS is for database migration and replication.

• Question 19:

  A data engineer needs to create an Amazon Athena table based on a subset of data from an existing Athena table named cities_world. The cities_world table contains cities that are located around the world.

  The data engineer must create a new table named cities_us to contain only the cities from cities_world that are located in the US.

  Which SQL statement should the data engineer use to meet this requirement?

  A. INSERT INTO cities_usa (city,state) SELECT city, state FROM cities_world WHERE country='usa';
  B. MOVE city, state FROM cities_world TO cities_usa WHERE country='usa';
  C. INSERT INTO cities_usa SELECT city, state FROM cities_world WHERE country='usa';
  D. UPDATE cities_usa SET (city, state) = (SELECT city, state FROM cities_world WHERE country='usa');
  A. INSERT INTO cities_usa (city,state) SELECT city, state FROM cities_world WHERE country='usa';

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  Explanation

  To create a new table named cities_usa in Amazon Athena based on a subset of data from the existing cities_world table, you should use an INSERT INTO statement combined with a SELECT statement to filter only the records where the country is 'usa'. The correct SQL syntax would be:

  Option A: INSERT INTO cities_usa (city, state) SELECT city, state FROM cities_world WHERE country='usa';This statement inserts only the cities and states where the country column has a value of 'usa' from the cities_world table into the cities_usa table. This is a correct approach to create a new table with data filtered from an existing table in Athena.

  Options B, C, and Dare incorrect due to syntax errors or incorrect SQL usage (e.g., the MOVE command or the use of UPDATE in a non-relevant context).

• Question 20:

  A company wants to migrate a data warehouse from Teradata to Amazon Redshift.

  Which solution will meet this requirement with the LEAST operational effort?

  A. Use AWS Database Migration Service (AWS DMS) Schema Conversion to migrate the schema. Use AWS DMS to migrate the data.
  B. Use the AWS Schema Conversion Tool (AWS SCT) to migrate the schema. Use AWS Database Migration Service (AWS DMS) to migrate the data.
  C. Use AWS Database Migration Service (AWS DMS) to migrate the data. Use automatic schema conversion.
  D. Manually export the schema de nition from Teradata. Apply the schema to the Amazon Redshift database. Use AWS Database Migration Service (AWS DMS) to migrate the data.
  B. Use the AWS Schema Conversion Tool (AWS SCT) to migrate the schema. Use AWS Database Migration Service (AWS DMS) to migrate the data.

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  Explanation

  This solution provides the least operational effort for migrating a data warehouse from Teradata to Amazon Redshift by using AWS services that are specifically designed for schema and data migration:

  AWS Schema Conversion Tool (SCT) is used to convert and migrate the schema from Teradata to Amazon Redshift, handling schema differences automatically and reducing manual work.

  AWS Database Migration Service (DMS) is then used to transfer the data from Teradata to Redshift, allowing for efficient data migration.