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, which is designed for efficient, parallel data extraction. An AWS Glue job can connect to Redshift, run the UNLOAD command, and be scheduled to run weekly. This approach cleanly supports exporting a specific subset of data and delivering it to a vendor’s S3 bucket, including cross-account access if required. Other options misuse Redshift features (COPY loads data into Redshift; data sharing and Spectrum are not meant for exporting data to S3).
Question 31
A data engineer must build an extract, transform, and load (ETL) pipeline to process and load data from 10 source systems into 10 tables that are in an Amazon Redshift database. All the source systems generate .csv, JSON, or Apache Parquet files every 15 minutes. The source systems all deliver files into one Amazon S3 bucket. The file sizes range from 10 MB to 20 GB. The ETL pipeline must function correctly despite changes to the data schema.
Which data pipeline solutions will meet these requirements? (Choose two.)
A. Use an Amazon EventBridge rule to run an AWS Glue job every 15 minutes. Configure the AWS Glue job to process and load the data into the Amazon Redshift tables.
B. Use an Amazon EventBridge rule to invoke an AWS Glue workflow job every 15 minutes. Configure the AWS Glue workflow to have an on-demand trigger that runs an AWS Glue crawler and then runs an AWS Glue job when the crawler finishes running successfully. Configure the AWS Glue job to process and load the data into the Amazon Redshift tables.
C. Configure an AWS Lambda function to invoke an AWS Glue crawler when a file is loaded into the S3 bucket. Configure an AWS Glue job to process and load the data into the Amazon Redshift tables. Create a second Lambda function to run the AWS Glue job. Create an Amazon EventBridge rule to invoke the second Lambda function when the AWS Glue crawler finishes running successfully.
D. Configure an AWS Lambda function to invoke an AWS Glue workflow when a file is loaded into the S3 bucket. Configure the AWS Glue workflow to have an on-demand trigger that runs an AWS Glue crawler and then runs an AWS Glue job when the crawler finishes running successfully. Configure the AWS Glue job to process and load the data into the Amazon Redshift tables.
E. Configure an AWS Lambda function to invoke an AWS Glue job when a file is loaded into the S3 bucket. Configure the AWS Glue job to read the files from the S3 bucket into an Apache Spark DataFrame. Configure the AWS Glue job to also put smaller partitions of the DataFrame into an Amazon Kinesis Data Firehose delivery stream. Configure the delivery stream to load data into the Amazon Redshift tables.
Show Answer
Correct Answer: B, D
Explanation: The key requirement is that the ETL pipeline must handle schema changes automatically while ingesting files of varying formats and sizes into Amazon Redshift.
Option B is correct because an EventBridge schedule runs an AWS Glue workflow every 15 minutes, and the workflow explicitly runs a Glue crawler before the Glue job. The crawler updates the Data Catalog to reflect schema changes, ensuring the downstream Glue job can process evolving schemas reliably.
Option D is also correct because it triggers the same Glue workflow (crawler followed by job) based on S3 file arrival. This event-driven approach still supports schema evolution through the crawler and scales to large file sizes using Glue and Spark.
Option A is incorrect because it runs only a Glue job without a crawler, so schema changes are not automatically detected. Options C and E introduce unnecessary complexity or inappropriate services (Lambda orchestration overhead or Kinesis Firehose limitations) and are not optimal for large files and schema evolution.
Question 32
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 provides a serverless and cost‑effective way to extract data from Amazon S3, perform transformations, and integrate data quality checks. AWS Glue Data Quality is natively integrated with Glue ETL jobs, avoiding extra services and operational overhead. Storing the processed, structured data in Amazon RDS for MySQL satisfies the requirement for a relational database for future queries while keeping costs lower than more complex multi‑service architectures.
Question 32
A data engineer needs to build an extract, transform, and load (ETL) job. The ETL job will process daily incoming .csv files that users upload to an Amazon S3 bucket. The size of each S3 object is less than 100 MB.
Which solution will meet these requirements MOST cost-effectively?
A. Write a custom Python application. Host the application on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster.
B. Write a PySpark ETL script. Host the script on an Amazon EMR cluster.
C. Write an AWS Glue PySpark job. Use Apache Spark to transform the data.
D. Write an AWS Glue Python shell job. Use pandas to transform the data.
Show Answer
Correct Answer: D
Explanation: The files are small (<100 MB) and arrive daily, so a lightweight, managed ETL option is most cost-effective. AWS Glue Python shell jobs can run with a fractional DPU (as low as 0.0625 DPU), avoiding the overhead and minimum resource requirements of Spark, EMR, or EKS. Using pandas is sufficient for CSV transformations at this scale, making this the cheapest suitable solution.
Question 33
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 indicates Spark executors are running out of local disk, commonly caused by large shuffle files from data skew. Using the Spark UI and AWS Glue metrics is the most effective way to identify skewed partitions and excessive shuffle usage. Enabling --write-shuffle-files-to-s3 offloads shuffle and spill files from local disks to Amazon S3, directly resolving the disk space issue, and salting reduces skew at the source. Both steps address root cause at lower cost than scaling worker size or count.
Question 33
A company stores datasets in JSON format and .csv format in an Amazon S3 bucket. The company has Amazon RDS for Microsoft SQL Server databases, Amazon DynamoDB tables that are in provisioned capacity mode, and an Amazon Redshift cluster. A data engineering team must develop a solution that will give data scientists the ability to query all data sources by using syntax similar to SQL.
Which solution will meet these requirements with the LEAST operational overhead?
A. Use AWS Glue to crawl the data sources. Store metadata in the AWS Glue Data Catalog. Use Amazon Athena to query the data. Use SQL for structured data sources. Use PartiQL for data that is stored in JSON format.
B. Use AWS Glue to crawl the data sources. Store metadata in the AWS Glue Data Catalog. Use Redshift Spectrum to query the data. Use SQL for structured data sources. Use PartiQL for data that is stored in JSON format.
C. Use AWS Glue to crawl the data sources. Store metadata in the AWS Glue Data Catalog. Use AWS Glue jobs to transform data that is in JSON format to Apache Parquet or .csv format. Store the transformed data in an S3 bucket. Use Amazon Athena to query the original and transformed data from the S3 bucket.
D. Use AWS Lake Formation to create a data lake. Use Lake Formation jobs to transform the data from all data sources to Apache Parquet format. Store the transformed data in an S3 bucket. Use Amazon Athena or Redshift Spectrum to query the data.
Show Answer
Correct Answer: A
Explanation: The requirement is a SQL-like way to query all data sources with the least operational overhead. AWS Glue crawlers plus the Glue Data Catalog provide centralized metadata with minimal management. Amazon Athena is fully serverless and can directly query JSON and CSV data in S3 using SQL, and can also query RDS, DynamoDB, and Redshift through federated connectors, avoiding data movement or cluster management. The other options add operational overhead through Redshift cluster dependency (B), data transformation jobs (C), or Lake Formation setup and ETL (D).
Question 34
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: The requirement is to update only ~10,000 records per day in a very large (1 TB, 10 million records) dataset stored in Amazon S3 with the lowest runtime. Apache Hudi is purpose-built for this exact use case: it supports efficient record-level upserts on S3 using indexing and incremental writes, avoiding full table rewrites. Running Hudi on Amazon EMR with Spark provides scalable, high-performance processing. The other options require reading and rewriting the entire dataset (Spark or Pandas), which would result in significantly higher runtimes, and Pandas cannot handle 1 TB of data efficiently.
Question 34
A company currently stores all of its data in Amazon S3 by using the S3 Standard storage class.
A data engineer examined data access patterns to identify trends. During the first 6 months, most data files are accessed several times each day. Between 6 months and 2 years, most data files are accessed once or twice each month. After 2 years, data files are accessed only once or twice each year.
The data engineer needs to use an S3 Lifecycle policy to develop new data storage rules. The new storage solution must continue to provide high availability.
Which solution will meet these requirements in the MOST cost-effective way?
A. Transition objects to S3 One Zone-Infrequent Access (S3 One Zone-IA) after 6 months. Transfer objects to S3 Glacier Flexible Retrieval after 2 years.
B. Transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 months. Transfer objects to S3 Glacier Flexible Retrieval after 2 years.
C. Transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 months. Transfer objects to S3 Glacier Deep Archive after 2 years.
D. Transition objects to S3 One Zone-Infrequent Access (S3 One Zone-IA) after 6 months. Transfer objects to S3 Glacier Deep Archive after 2 years.
Show Answer
Correct Answer: C
Explanation: After 6 months, access drops to monthly, so S3 Standard-IA provides multi-AZ high availability at lower cost than S3 Standard. After 2 years, access is only once or twice per year, and there is no retrieval-time requirement; S3 Glacier Deep Archive offers the lowest storage cost while still providing high availability (durability and availability comparable to other Glacier classes). One Zone-IA is excluded because it does not provide the same high availability. Therefore, transitioning to Standard-IA and then Glacier Deep Archive is the most cost-effective solution that meets the requirements.
Question 35
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: Using an S3 event notification (via EventBridge) to trigger a Lambda function that issues a Redshift COPY command is the simplest and lowest-overhead solution. COPY is natively optimized for loading CSV data from S3 into Redshift, and Lambda is sufficient given the small file sizes and low arrival frequency. The other options add unnecessary components (Firehose, Glue/Spectrum, or DMS) that increase complexity and operational overhead without added benefit for this use case.
Question 35
A data engineer must use AWS services to ingest a dataset into an Amazon S3 data lake. The data engineer profiles the dataset and discovers that the dataset contains personally identifiable information (PII). The data engineer must implement a solution to profile the dataset and obfuscate the PII.
Which solution will meet this requirement with the LEAST operational effort?
A. Use an Amazon Kinesis Data Firehose delivery stream to process the dataset. Create an AWS Lambda transform function to identify the PII. Use an AWS SDK to obfuscate the PII. Set the S3 data lake as the target for the delivery stream.
B. Use the Detect PII transform in AWS Glue Studio to identify the PII. Obfuscate the PII. Use an AWS Step Functions state machine to orchestrate a data pipeline to ingest the data into the S3 data lake.
C. Use the Detect PII transform in AWS Glue Studio to identify the PII. Create a rule in AWS Glue Data Quality to obfuscate the PII. Use an AWS Step Functions state machine to orchestrate a data pipeline to ingest the data into the S3 data lake.
D. Ingest the dataset into Amazon DynamoDB. Create an AWS Lambda function to identify and obfuscate the PII in the DynamoDB table and to transform the data. Use the same Lambda function to ingest the data into the S3 data lake.
Show Answer
Correct Answer: B
Explanation: AWS Glue Studio provides a built-in Detect PII transform that can both profile data for sensitive fields and apply actions such as masking or obfuscation with minimal configuration. This is a managed, low-code capability specifically designed for PII handling, requiring less custom code and fewer moving parts than Lambda- or SDK-based solutions. Adding AWS Glue Data Quality rules (option C) introduces extra configuration and operational overhead without being necessary for obfuscation. Kinesis/Lambda or DynamoDB-based approaches require more custom development and ongoing maintenance. Therefore, option B meets the requirement with the least operational effort.
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