A company uses Amazon Redshift as a data warehouse solution. One of the datasets that the company stores in Amazon Redshift contains data for a vendor.
Recently, the vendor asked the company to transfer the vendor’s data into the vendor’s Amazon S3 bucket once each week.
Which solution will meet this requirement?
A. Create an AWS Lambda function to connect to the Redshift data warehouse. Configure the Lambda function to use the Redshift COPY command to copy the required data to the vendor’s S3 bucket on a schedule.
B. Create an AWS Glue job to connect to the Redshift data warehouse. Configure the AWS Glue job to use the Redshift UNLOAD command to load the required data to the vendor’s S3 bucket on a schedule.
C. Use the Amazon Redshift data sharing feature. Set the vendor’s S3 bucket as the destination. Configure the source to be as a custom SQL query that selects the required data.
D. Configure Amazon Redshift Spectrum to use the vendor’s S3 bucket a destination, Enable data querying in both directions.
Show Answer
Correct Answer: B
Explanation: Amazon Redshift exports data to Amazon S3 by using the UNLOAD command, not COPY. AWS Glue can connect to Amazon Redshift, execute the UNLOAD operation on a schedule, and write the results to an Amazon S3 bucket, including a cross-account bucket with appropriate permissions. Redshift data sharing is for sharing Redshift data between Redshift consumers, not S3, and Redshift Spectrum queries data in S3 rather than exporting Redshift tables to S3.
Question 89
A company is setting up a data pipeline in AWS. The pipeline extracts client data from Amazon S3 buckets, performs quality checks, and transforms the data. The pipeline stores the processed data in a relational database. The company will use the processed data for future queries.
Which solution will meet these requirements MOST cost-effectively?
A. Use AWS Glue ETL to extract the data from the S3 buckets and perform the transformations. Use AWS Glue Data Quality to enforce suggested quality rules. Load the data and the quality check results into an Amazon RDS for MySQL instance.
B. Use AWS Glue Studio to extract the data from the S3 buckets. Use AWS Glue DataBrew to perform the transformations and quality checks. Load the processed data into an Amazon RDS for MySQL instance. Load the quality check results into a new S3 bucket.
C. Use AWS Glue ETL to extract the data from the S3 buckets and perform the transformations. Use AWS Glue DataBrew to perform quality checks. Load the processed data and the quality check results into a new S3 bucket.
D. Use AWS Glue Studio to extract the data from the S3 buckets. Use AWS Glue DataBrew to perform the transformations and quality checks. Load the processed data and quality check results into an Amazon RDS for MySQL instance.
Show Answer
Correct Answer: A
Explanation: AWS Glue ETL is the appropriate serverless service to extract and transform data from Amazon S3. AWS Glue Data Quality integrates with Glue ETL to perform automated data quality validation efficiently. Because the requirement is to store processed data in a relational database for future queries, loading the transformed data (and, if desired, quality results) into Amazon RDS for MySQL satisfies the storage requirement. The alternatives either rely on DataBrew for production ETL/quality workflows or store the processed data only in Amazon S3 instead of a relational database.
Question 90
A data engineer develops an AWS Glue Apache Spark ETL job to perform transformations on a dataset. When the data engineer runs the job, the job returns an error that reads, “No space left on device.”
The data engineer needs to identify the source of the error and provide a solution.
Which combinations of steps will meet this requirement MOST cost-effectively? (Choose two.)
A. Scale out the workers vertically to address data skewness.
B. Use the Spark UI and AWS Glue metrics to monitor data skew in the Spark executors.
C. Scale out the number of workers horizontally to address data skewness.
D. Enable the --write-shuffie-files-to-s3 job parameter. Use the salting technique.
E. Use error logs in Amazon CloudWatch to monitor data skew.
Show Answer
Correct Answer: B, D
Explanation: The error 'No space left on device' in AWS Glue Spark jobs is commonly caused by local disk exhaustion during shuffle operations, often exacerbated by data skew. Use the Spark UI and AWS Glue metrics to identify skewed partitions and executors (B). Then enable the --write-shuffle-files-to-s3 job parameter to offload shuffle spill files to Amazon S3, and use salting to mitigate skew by distributing hot keys more evenly (D). Scaling workers (A or C) increases cost and may mask rather than resolve the underlying issue, while CloudWatch error logs (E) report failures but are not the best tool to diagnose skew.
Question 91
A company wants to build a dimension table in an Amazon S3 bucket. The bucket contains historical data that includes 10 million records. The historical data is 1 TB in size.
A data engineer needs a solution to update changes for up to 10,000 records in the base table every day.
Which solution will meet this requirement with the LOWEST runtime?
A. Develop an Apache Spark job in Amazon EMR to read the historical data and the new changes into two Spark DataFrames. Use the Spark update method to update the base table.
B. Develop an AWS Glue Python job to read the historical data and new changes into two Pandas DataFrames. Use the Pandas update method to update the base table.
C. Develop an AWS Glue Apache Spark job to read the historical data and new changes into two Spark DataFrames. Use the Spark update method to update the base table.
D. Develop an Amazon EMR job to read new changes into Apache Spark DataFrames. Use the Apache Hudi framework to create the base table in Amazon S3. Use the Spark update method to update the base table.
Show Answer
Correct Answer: D
Explanation: Apache Hudi is designed for record-level upserts on large datasets stored in Amazon S3, avoiding full-table rewrites. Updating only about 10,000 records in a 1 TB, 10 million-record dataset is far more efficient with Hudi than rereading and rewriting the entire dataset. Running Spark on Amazon EMR with Hudi provides the lowest runtime among the options.
Question 92
A company receives marketing campaign data from a vendor. The company ingests the data into an Amazon S3 bucket every 40 to 60 minutes. The data is in CSV format. File sizes are between 100 KB and 300 KB.
A data engineer needs to set-up an extract, transform, and load (ETL) pipeline to upload the content of each file to Amazon Redshift.
Which solution will meet these requirements with the LEAST operational overhead?
A. Create an AWS Lambda function that connects to Amazon Redshift and runs a COPY command. Use Amazon EventBridge to invoke the Lambda function based on an Amazon S3 upload trigger.
B. Create an Amazon Data Firehose stream. Configure the stream to use an AWS Lambda function as a source to pull data from the S3 bucket. Set Amazon Redshift as the destination.
C. Use Amazon Redshift Spectrum to query the S3 bucket. Configure an AWS Glue Crawler for the S3 bucket to update metadata in an AWS Glue Data Catalog.
D. Creates an AWS Database Migration Service (AWS DMS) task. Specify an appropriate data schema to migrate. Specify the appropriate type of migration to use.
Show Answer
Correct Answer: A
Explanation: AWS Lambda triggered by Amazon S3 object creation events (typically via EventBridge or S3 event notifications) can execute an Amazon Redshift COPY command to load each CSV from S3. This is a simple, serverless, low-operational-overhead ETL pattern for small, periodic files. Amazon Data Firehose does not use S3 as a source in this way, Redshift Spectrum is for querying data in S3 rather than loading it into Redshift, and AWS DMS is intended for database migration/replication rather than ingesting CSV files from S3.
Question 93
A data engineer uses AWS Lake Formation to manage access to data that is stored in an Amazon S3 bucket. The data engineer configures an AWS Glue crawler to discover data at a specific file location in the bucket, s3://examplepath. The crawler execution fails with the following error: “The S3 location: s3://examplepath is not registered.”
The data engineer needs to resolve the error.
Which solution will meet this requirement?
A. Attach an appropriate IAM policy to the IAM role of the AWS Glue crawler to grant the crawler permission to read the S3 location.
B. Register the S3 location in Lake Formation to allow the crawler to access the data.
C. Create a new AWS Glue database. Assign the correct permissions to the database for the crawler.
D. Configure the S3 bucket policy to allow cross-account access.
Show Answer
Correct Answer: B
Explanation: AWS Lake Formation requires Amazon S3 data locations to be registered before Lake Formation-managed services such as AWS Glue crawlers can access them under Lake Formation permissions. The error explicitly indicates that the S3 location is not registered. Registering s3://examplepath in Lake Formation resolves the issue. IAM permissions alone, creating a new Glue database, or modifying the bucket policy do not address this specific registration error.
Question 94
A company stores sensitive data in an Amazon Redshift table. The company needs to give specific users the ability to access the sensitive data. The company must not create duplication in the data.
Customer support users must be able to see the last four characters of the sensitive data. Audit users must be able to see the full value of the sensitive data. No other users can have the ability to access the sensitive information.
Which solution will meet these requirements?
A. Create a dynamic data masking policy to allow access based on each user role. Create IAM roles that have specific access permissions. Attach the masking policy to the column that contains sensitive data.
B. Enable metadata security on the Redshift cluster. Create IAM users and IAM roles for the customer support users and the audit users. Grant the IAM users and IAM roles permissions to view the metadata in the Redshift cluster.
C. Create a row-level security policy to allow access based on each user role. Create IAM roles that have specific access permissions. Attach the security policy to the table.
D. Create an AWS Glue job to redact the sensitive data and to load the data into a new Redshift table.
Show Answer
Correct Answer: A
Explanation: Amazon Redshift Dynamic Data Masking (DDM) is designed to mask sensitive column values based on user or role while storing only a single copy of the data. A masking policy can reveal only the last four characters for customer support users, the full value for audit users, and deny or mask the value for all other users. Row-level security filters rows rather than masking column values, metadata security is unrelated to data visibility, and creating a second table duplicates data.
Question 95
A company is building a new application that ingests CSV files into Amazon Redshift. The company has developed the frontend for the application.
The files are stored in an Amazon S3 bucket. Files are no larger than 5 MB.
A data engineer is developing the extract, transform, and load (ETL) pipeline for the CSV files. The data engineer configured a Redshift cluster and an AWS Lambda function that copies the data out of the files into the Redshift cluster.
Which additional steps should the data engineer perform to meet these requirements?
A. Configure the bucket to send S3 event notifications to Amazon EventBridge. Configure an EventBridge rule that matches S3 new object created events. Set the Lambda function as the target.
B. Configure the $3 bucket to send S3 event notifications to an Amazon Simple Queue Service (Amazon SQS) queue. Configure the Lambda function to process the queue.
C. Configure AWS Database Migration Service (AWS DMS) to stream new S3 objects to a data stream in Amazon Kinesis Data Streams. Set the Lambda function as the target of the data stream.
D. Configure an Amazon EventBridge rule that matches S3 new object created events. Set an Amazon Simple Queue Service (Amazon SQS) queue as the target of the rule. Configure the Lambda function to process the queue.
Show Answer
Correct Answer: A
Explanation: For a simple event-driven pipeline where new CSV files in Amazon S3 should invoke an existing Lambda function that loads Amazon Redshift, the most direct architecture is S3 object-created events routed through Amazon EventBridge to Lambda. The other options add unnecessary components (SQS), misuse AWS DMS (which does not stream new S3 objects into Kinesis in this way), or introduce extra indirection without a stated requirement for buffering or decoupling.
Sources:
https://www.pass4success.com/amazon/discussions/exam-amazon-dea-c01-topic-1-question-26-discussion
Question 96
A company has several new datasets in CSV and JSON formats. A data engineer needs to make the data available to a team of data analysts who will analyze the data by using SQL queries.
Which solution will meet these requirements in the MOST cost-effective way?
A. Create an Amazon RDS MySQL cluster. Use AWS Glue to transform and load the CSV and JSON files into database tables. Provide the data analysts access to the MySQL cluster.
B. Create an AWS Glue DataBrew project that contains the new data. Make the DataBrew project available to the data analysts.
C. Store the data in an Amazon S3 bucket. Use an AWS Glue crawler to catalog the S3 bucket as tables. Create an Amazon Athena workgroup that has a data usage threshold. Grant the data analysts access to the Athena workgroup.
D. Load the data into Super-fast, Parallel, In-memory Calculation Engine (SPICE) in Amazon QuickSight. Allow the data analysts to create analyses and dashboards in QuickSight.
Show Answer
Correct Answer: C
Explanation: Storing CSV and JSON data in Amazon S3, cataloging it with an AWS Glue crawler, and querying it with Amazon Athena is the most cost-effective serverless solution. Athena allows analysts to run standard SQL directly against data in S3 without provisioning databases, while the Glue Data Catalog provides table metadata. Athena workgroups can enforce data usage controls and cost thresholds. RDS requires ongoing infrastructure costs and ETL loading, DataBrew is for data preparation rather than SQL analytics, and QuickSight SPICE is for BI dashboards rather than general SQL querying.
Question 97
A data engineer needs to optimize the performance of a data pipeline that handles retail orders. Data about the orders is ingested daily into an Amazon S3 bucket.
The data engineer runs queries once each week to extract metrics from the orders data based the order date for multiple date ranges. The data engineer needs an optimization solution that ensures the query performance will not degrade when the volume of data increases.
Which solution will meet this requirement MOST cost-effectively?
A. Partition the data based on order date. Use Amazon Athena to query the data.
B. Partition the data based on order date. Use Amazon Redshift to query the data.
C. Partition the data based on load date. Use Amazon EMR to query the data.
D. Partition the data based on load date. Use Amazon Aurora to query the data.
Show Answer
Correct Answer: A
Explanation: Partitioning the S3 data by order date aligns with the query predicate, allowing Athena to prune partitions and scan only the relevant data for each date range. Athena is serverless and pay-per-query, making it the most cost-effective option for weekly analytics while maintaining performance as data volume grows. Partitioning by load date does not match the access pattern, and Redshift or Aurora would incur higher ongoing infrastructure costs for this workload.
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