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AWS Certified Data Engineer - Associate DEA-C01
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Question 26
A data engineer is processing and analyzing multiple terabytes of raw data that is in Amazon S3. The data engineer needs to clean and prepare the data. Then the data engineer needs to load the data into Amazon Redshift for analytics.
The data engineer needs a solution that will give data analysts the ability to perform complex queries. The solution must eliminate the need to perform complex extract, transform, and load (ETL) processes or to manage infrastructure.
Which solution will meet these requirements with the LEAST operational overhead?
- A: Use Amazon EMR to prepare the data. Use AWS Step Functions to load the data into Amazon Redshift. Use Amazon QuickSight to run queries.
- B: Use AWS Glue DataBrew to prepare the data. Use AWS Glue to load the data into Amazon Redshift. Use Amazon Redshift to run queries.
- C: Use AWS Lambda to prepare the data. Use Amazon Kinesis Data Firehose to load the data into Amazon Redshift. Use Amazon Athena to run queries.
- D: Use AWS Glue to prepare the data. Use AWS Database Migration Service (AVVS DMS) to load the data into Amazon Redshift. Use Amazon Redshift Spectrum to run queries.
Question 27
A company uses an AWS Lambda function to transfer files from a legacy SFTP environment to Amazon S3 buckets. The Lambda function is VPC enabled to ensure that all communications between the Lambda function and other AVS services that are in the same VPC environment will occur over a secure network.
The Lambda function is able to connect to the SFTP environment successfully. However, when the Lambda function attempts to upload files to the S3 buckets, the Lambda function returns timeout errors. A data engineer must resolve the timeout issues in a secure way.
Which solution will meet these requirements in the MOST cost-effective way?
- A: Create a NAT gateway in the public subnet of the VPC. Route network traffic to the NAT gateway.
- B: Create a VPC gateway endpoint for Amazon S3. Route network traffic to the VPC gateway endpoint.
- C: Create a VPC interface endpoint for Amazon S3. Route network traffic to the VPC interface endpoint.
- D: Use a VPC internet gateway to connect to the internet. Route network traffic to the VPC internet gateway.
Question 28
A company reads data from customer databases that run on Amazon RDS. The databases contain many inconsistent fields. For example, a customer record field that iPnamed place_id in one database is named location_id in another database. The company needs to link customer records across different databases, even when customer record fields do not match.
Which solution will meet these requirements with the LEAST operational overhead?
- A: Create a provisioned Amazon EMR cluster to process and analyze data in the databases. Connect to the Apache Zeppelin notebook. Use the FindMatches transform to find duplicate records in the data.
- B: Create an AWS Glue crawler to craw the databases. Use the FindMatches transform to find duplicate records in the data. Evaluate and tune the transform by evaluating the performance and results.
- C: Create an AWS Glue crawler to craw the databases. Use Amazon SageMaker to construct Apache Spark ML pipelines to find duplicate records in the data.
- D: Create a provisioned Amazon EMR cluster to process and analyze data in the databases. Connect to the Apache Zeppelin notebook. Use an Apache Spark ML model to find duplicate records in the data. Evaluate and tune the model by evaluating the performance and results.
Question 29
A finance company receives data from third-party data providers and stores the data as objects in an Amazon S3 bucket.
The company ran an AWS Glue crawler on the objects to create a data catalog. The AWS Glue crawler created multiple tables. However, the company expected that the crawler would create only one table.
The company needs a solution that will ensure the AVS Glue crawler creates only one table.
Which combination of solutions will meet this requirement? (Choose two.)
- A: Ensure that the object format, compression type, and schema are the same for each object.
- B: Ensure that the object format and schema are the same for each object. Do not enforce consistency for the compression type of each object.
- C: Ensure that the schema is the same for each object. Do not enforce consistency for the file format and compression type of each object.
- D: Ensure that the structure of the prefix for each S3 object name is consistent.
- E: Ensure that all S3 object names follow a similar pattern.
Question 30
An application consumes messages from an Amazon Simple Queue Service (Amazon SQS) queue. The application experiences occasional downtime. As a result of the downtime, messages within the queue expire and are deleted after 1 day. The message deletions cause data loss for the application.
Which solutions will minimize data loss for the application? (Choose two.)
- A: Increase the message retention period
- B: Increase the visibility timeout.
- C: Attach a dead-letter queue (DLQ) to the SQS queue.
- D: Use a delay queue to delay message delivery
- E: Reduce message processing time.
Question 31
A company is creating near real-time dashboards to visualize time series data. The company ingests data into Amazon Managed Streaming for Apache Kafka (Amazon MSK). A customized data pipeline consumes the data. The pipeline then writes data to Amazon Keyspaces (for Apache Cassandra), Amazon OpenSearch Service, and Apache Avro objects in Amazon S3.
Which solution will make the data available for the data visualizations with the LEAST latency?
- A: Create OpenSearch Dashboards by using the data from OpenSearch Service.
- B: Use Amazon Athena with an Apache Hive metastore to query the Avro objects in Amazon S3. Use Amazon Managed Grafana to connect to Athena and to create the dashboards.
- C: Use Amazon Athena to query the data from the Avro objects in Amazon S3. Configure Amazon Keyspaces as the data catalog. Connect Amazon QuickSight to Athena to create the dashboards.
- D: Use AWS Glue to catalog the data. Use S3 Select to query the Avro objects in Amazon S3. Connect Amazon QuickSight to the S3 bucket to create the dashboards.
Question 32
A data engineer maintains a materialized view that is based on an Amazon Redshift database. The view has a column named load_date that stores the date when each row was loaded.
The data engineer needs to reclaim database storage space by deleting all the rows from the materialized view.
Which command will reclaim the MOST database storage space?
- A: DELETE FROM materialized_view_name where 1=1
- B: TRUNCATE materialized_view_name
- C: VACUUM table_name where load_date<=current_date materializedview
- D: DELETE FROM materialized_view_name where load_date<=current_date
Question 33
A media company wants to use Amazon OpenSearch Service to analyze rea-time data about popular musical artists and songs. The company expects to ingest millions of new data events every day. The new data events will arrive through an Amazon Kinesis data stream. The company must transform the data and then ingest the data into the OpenSearch Service domain.
Which method should the company use to ingest the data with the LEAST operational overhead?
- A: Use Amazon Kinesis Data Firehose and an AWS Lambda function to transform the data and deliver the transformed data to OpenSearch Service.
- B: Use a Logstash pipeline that has prebuilt filters to transform the data and deliver the transformed data to OpenSearch Service.
- C: Use an AWS Lambda function to call the Amazon Kinesis Agent to transform the data and deliver the transformed data OpenSearch Service.
- D: Use the Kinesis Client Library (KCL) to transform the data and deliver the transformed data to OpenSearch Service.
Question 34
A company stores customer data tables that include customer addresses in an AWS Lake Formation data lake. To comply with new regulations, the company must ensure that users cannot access data for customers who are in Canada.
The company needs a solution that will prevent user access to rows for customers who are in Canada.
Which solution will meet this requirement with the LEAST operational effort?
- A: Set a row-level filter to prevent user access to a row where the country is Canada.
- B: Create an IAM role that restricts user access to an address where the country is Canada.
- C: Set a column-level filter to prevent user access to a row where the country is Canada.
- D: Apply a tag to all rows where Canada is the country. Prevent user access where the tag is equal to “Canada”.
Question 35
A company stores daily records of the financial performance of investment portfolios in .csv format in an Amazon S3 bucket. A data engineer uses AWS Glue crawlers to crawl the S3 data.
The data engineer must make the S3 data accessible daily in the AWS Glue Data Catalog.
Which solution will meet these requirements?
- A: Create an IAM role that includes the AmazonS3FullAccess policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler's data store. Create a daily schedule to run the crawler. Configure the output destination to a new path in the existing S3 bucket.
- B: Create an IAM role that includes the AWSGlueServiceRole policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler's data store. Create a daily schedule to run the crawler. Specify a database name for the output.
- C: Create an IAM role that includes the AmazonS3FullAccess policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler's data store. Allocate data processing units (DPUs) to run the crawler every day. Specify a database name for the output.
- D: Create an IAM role that includes the AWSGlueServiceRole policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler's data store. Allocate data processing units (DPUs) to run the crawler every day. Configure the output destination to a new path in the existing S3 bucket.
Question 36
A company has implemented a lake house architecture in Amazon Redshift. The company needs to give users the ability to authenticate into Redshift query editor by using a third-party identity provider (IdP).
A data engineer must set up the authentication mechanism.
What is the first step the data engineer should take to meet this requirement?
- A: Register the third-party IdP as an identity provider in the configuration settings of the Redshift cluster.
- B: Register the third-party IdP as an identity provider from within Amazon Redshift.
- C: Register the third-party IdP as an identity provider for AVS Secrets Manager. Configure Amazon Redshift to use Secrets Manager to manage user credentials.
- D: Register the third-party IdP as an identity provider for AWS Certificate Manager (ACM). Configure Amazon Redshift to use ACM to manage user credentials.
Question 37
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 Re instances to compute optimized EC2 instances.
- D: Reduce the scaling cooldown period for the provisioned EMR cluster.
Question 38
A company uploads .csv files to an Amazon S3 bucket. The company’s data platform team has set up an AWS Glue crawler to perform data discovery and to create the tables and schemas.
An AWS Glue job writes processed data from the tables to an Amazon Redshift database. The AWS Glue job handles column mapping and creates the Amazon Redshift tables in the Redshift database appropriately.
If the company reruns the AWS Glue job for any reason, duplicate records are introduced into the Amazon Redshift tables. The company needs a solution that will update the Redshift tables without duplicates.
Which solution will meet these requirements?
- A: Modify the AWS Glue job to copy the rows into a staging Redshift table. Add SQL commands to update the existing rows with new values from the staging Redshift table.
- B: Modify the AWS Glue job to load the previously inserted data into a MySQL database. Perform an upsert operation in the MySQL database. Copy the results to the Amazon Redshift tables.
- C: Use Apache Spark’s DataFrame dropDuplicates() API to eliminate duplicates. Write the data to the Redshift tables.
- D: Use the AWS Glue ResolveChoice built-in transform to select the value of the column from the most recent record.
Question 39
A company is using Amazon Redshift to build a data warehouse solution. The company is loading hundreds of files into a fact table that is in a Redshift cluster.
The company wants the data warehouse solution to achieve the greatest possible throughput. The solution must use cluster resources optimally when the company loads data into the fact table.
Which solution will meet these requirements?
- A: Use multiple COPY commands to load the data into the Redshift cluster.
- B: Use S3DistCp to load multiple files into Hadoop Distributed File System (HDFS). Use an HDFS connector to ingest the data into the Redshift cluster.
- C: Use a number of INSERT statements equal to the number of Redshift cluster nodes. Load the data in parallel into each node.
- D: Use a single COPY command to load the data into the Redshift cluster.
Question 40
A company ingests data from multiple data sources and stores the data in an Amazon S3 bucket. An AWS Glue extract, transform, and load (ETL) job transforms the data and writes the transformed data to an Amazon S3 based data lake. The company uses Amazon Athena to query the data that is in the data lake.
The company needs to identify matching records even when the records do not have a common unique identifier.
Which solution will meet this requirement?
- A: Use Amazon Macie pattern matching as part of the ETL job.
- B: Train and use the AWS Glue PySpark Filter class in the ETL job.
- C: Partition tables and use the ETL job to partition the data on a unique identifier.
- D: Train and use the AWS Lake Formation FindMatches transform in the ETL job.
Question 41
A data engineer is using an AWS Glue crawler to catalog data that is in an Amazon S3 bucket. The S3 bucket contains both .csv and json files. The data engineer configured the crawler to exclude the .json files from the catalog.
When the data engineer runs queries in Amazon Athena, the queries also process the excluded .json files. The data engineer wants to resolve this issue. The data engineer needs a solution that will not affect access requirements for the .csv files in the source S3 bucket.
Which solution will meet this requirement with the SHORTEST query times?
- A: Adjust the AWS Glue crawler settings to ensure that the AWS Glue crawler also excludes .json files.
- B: Use the Athena console to ensure the Athena queries also exclude the .json files.
- C: Relocate the .json files to a different path within the S3 bucket.
- D: Use S3 bucket policies to block access to the .json files.
Question 42
A data engineer set up an AWS Lambda function to read an object that is stored in an Amazon S3 bucket. The object is encrypted by an AWS KMS key.
The data engineer configured the Lambda function’s execution role to access the S3 bucket. However, the Lambda function encountered an error and failed to retrieve the content of the object.
What is the likely cause of the error?
- A: The data engineer misconfigured the permissions of the S3 bucket. The Lambda function could not access the object.
- B: The Lambda function is using an outdated SDK version, which caused the read failure.
- C: The S3 bucket is located in a different AWS Region than the Region where the data engineer works. Latency issues caused the Lambda function to encounter an error.
- D: The Lambda function’s execution role does not have the necessary permissions to access the KMS key that can decrypt the S3 object.
Question 43
A data engineer has implemented data quality rules in 1,000 AWS Glue Data Catalog tables. Because of a recent change in business requirements, the data engineer must edit the data quality rules.
How should the data engineer meet this requirement with the LEAST operational overhead?
- A: Create a pipeline in AWS Glue ETL to edit the rules for each of the 1,000 Data Catalog tables. Use an AWS Lambda function to call the corresponding AWS Glue job for each Data Catalog table.
- B: Create an AWS Lambda function that makes an API call to AWS Glue Data Quality to make the edits.
- C: Create an Amazon EMR cluster. Run a pipeline on Amazon EMR that edits the rules for each Data Catalog table. Use an AWS Lambda function to run the EMR pipeline.
- D: Use the AWS Management Console to edit the rules within the Data Catalog.
Question 44
Two developers are working on separate application releases. The developers have created feature branches named Branch A and Branch B by using a GitHub repository’s master branch as the source.
The developer for Branch A deployed code to the production system. The code for Branch B will merge into a master branch in the following week’s scheduled application release.
Which command should the developer for Branch B run before the developer raises a pull request to the master branch?
- A: git diff branchB master git commit -m
- B: git pull master
- C: git rebase master
- D: git fetch -b master
Question 45
A company stores employee data in Amazon Resdshift. A table names Employee uses columns named Region ID, Department ID, and Role ID as a compound sort key.
Which queries will MOST increase the speed of query by using a compound sort key of the table? (Choose two.)
- A: Select *from Employee where Region ID=’North America’;
- B: Select *from Employee where Region ID=’North America’ and Department ID=20;
- C: Select *from Employee where Department ID=20 and Region ID=’North America’;
- D: Select *from Employee where Role ID=50;
- E: Select *from Employee where Region ID=’North America’ and Role ID=50;
Question 46
A company loads transaction data for each day into Amazon Redshift tables at the end of each day. The company wants to have the ability to track which tables have been loaded and which tables still need to be loaded.
A data engineer wants to store the load statuses of Redshift tables in an Amazon DynamoDB table. The data engineer creates an AWS Lambda function to publish the details of the load statuses to DynamoDB.
How should the data engineer invoke the Lambda function to write load statuses to the DynamoDB table?
- A: Use a second Lambda function to invoke the first Lambda function based on Amazon CloudWatch events.
- B: Use the Amazon Redshift Data API to publish an event to Amazon EventBridge. Configure an EventBridge rule to invoke the Lambda function.
- C: Use the Amazon Redshift Data API to publish a message to an Amazon Simple Queue Service (Amazon SQS) queue. Configure the SQS queue to invoke the Lambda function.
- D: Use a second Lambda function to invoke the first Lambda function based on AWS CloudTrail events.
Question 47
A company receives test results from testing facilities that are located around the world. The company stores the test results in millions of 1 KB JSON files in an Amazon S3 bucket. A data engineer needs to process the files, convert them into Apache Parquet format, and load them into Amazon Redshift tables. The data engineer uses AWS Glue to process the files, AWS Step Functions to orchestrate the processes, and Amazon EventBridge to schedule jobs.
The company recently added more testing facilities. The time required to process files is increasing. The data engineer must reduce the data processing time.
Which solution will MOST reduce the data processing time?
- A: Use AWS Lambda to group the raw input files into larger files. Write the larger files back to Amazon S3. Use AWS Glue to process the files. Load the files into the Amazon Redshift tables.
- B: Use the AWS Glue dynamic frame file-grouping option to ingest the raw input files. Process the files. Load the files into the Amazon Redshift tables.
- C: Use the Amazon Redshift COPY command to move the raw input files from Amazon S3 directly into the Amazon Redshift tables. Process the files in Amazon Redshift.
- D: Use Amazon EMR instead of AWS Glue to group the raw input files. Process the files in Amazon EMR. Load the files into the Amazon Redshift tables.
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