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: When AWS Lake Formation is enabled, data locations in Amazon S3 must be explicitly registered with Lake Formation before services such as AWS Glue crawlers can access them. If the S3 path is not registered, Lake Formation blocks access and the crawler fails with the error stating the location is not registered. Registering s3://examplepath in Lake Formation resolves the issue.
Question 36
A company maintains multiple extract, transform, and load (ETL) workflows that ingest data from the company's operational databases into an Amazon S3 based data lake. The ETL workflows use AWS Glue and Amazon EMR to process data.
The company wants to improve the existing architecture to provide automated orchestration and to require minimal manual effort.
Which solution will meet these requirements with the LEAST operational overhead?
A. AWS Glue workflows
B. AWS Step Functions tasks
C. AWS Lambda functions
D. Amazon Managed Workflows for Apache Airflow (Amazon MWAA) workflows
Show Answer
Correct Answer: B
Explanation: AWS Step Functions provides serverless, fully managed orchestration with native integrations for both AWS Glue jobs and Amazon EMR (including EMR Serverless). It enables automated ETL workflows across these services with minimal operational overhead. AWS Glue workflows can orchestrate only Glue components, not EMR. Amazon MWAA (Airflow) adds significant setup and maintenance overhead, and AWS Lambda is not suitable for orchestrating long-running ETL jobs.
Question 37
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 is designed to control column-level data visibility based on user or role without duplicating data. It allows masking logic such as showing only the last four characters for customer support users while allowing audit users to see the full value, and denying access to others. Row-level security does not mask column values, metadata security is unrelated, and creating a separate redacted table would duplicate data, which violates the requirement.
Question 37
A company uses Amazon S3 to store semi-structured data in a transactional data lake. Some of the data files are small, but other data files are tens of terabytes.
A data engineer must perform a change data capture (CDC) operation to identify changed data from the data source. The data source sends a full snapshot as a JSON file every day and ingests the changed data into the data lake.
Which solution will capture the changed data MOST cost-effectively?
A. Create an AWS Lambda function to identify the changes between the previous data and the current data. Configure the Lambda function to ingest the changes into the data lake.
B. Ingest the data into Amazon RDS for MySQL. Use AWS Database Migration Service (AWS DMS) to write the changed data to the data lake.
C. Use an open source data lake format to merge the data source with the S3 data lake to insert the new data and update the existing data.
D. Ingest the data into an Amazon Aurora MySQL DB instance that runs Aurora Serverless. Use AWS Database Migration Service (AWS DMS) to write the changed data to the data lake.
Show Answer
Correct Answer: C
Explanation: Using an open source transactional data lake format (such as Apache Iceberg, Delta Lake, or Hudi) allows efficient CDC-style upserts by comparing snapshots and merging changes directly in Amazon S3. This approach is designed for very large datasets (including tens of terabytes), avoids Lambda runtime and memory limits, and eliminates the cost and overhead of running and scaling relational databases or AWS DMS. It is the most cost-effective and scalable solution for snapshot-based CDC in a data lake.
Question 38
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: B
Explanation: Configuring S3 event notifications to an SQS queue and having Lambda process the queue provides reliable, scalable ingestion. SQS buffers concurrent object-created events, supports retries and failure handling, and decouples S3 from Lambda, which is ideal for processing many small CSV files. The other options add unnecessary components or lack the same level of reliability and control.
Question 38
A data engineer must orchestrate a series of Amazon Athena queries that will run every day. Each query can run for more than 15 minutes.
Which combination of steps will meet these requirements MOST cost-effectively? (Choose two.)
A. Use an AWS Lambda function and the Athena Boto3 client start_query_execution API call to invoke the Athena queries programmatically.
B. Create an AWS Step Functions workflow and add two states. Add the first state before the Lambda function. Configure the second state as a Wait state to periodically check whether the Athena query has finished using the Athena Boto3 get_query_execution API call. Configure the workflow to invoke the next query when the current query has finished running.
C. Use an AWS Glue Python shell job and the Athena Boto3 client start_query_execution API call to invoke the Athena queries programmatically.
D. Use an AWS Glue Python shell script to run a sleep timer that checks every 5 minutes to determine whether the current Athena query has finished running successfully. Configure the Python shell script to invoke the next query when the current query has finished running.
E. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate the Athena queries in AWS Batch.
Show Answer
Correct Answer: A, B
Explanation: Athena queries run asynchronously, so a short‑lived Lambda function can cost‑effectively start each query using start_query_execution without waiting for completion. Because individual queries can run longer than 15 minutes, AWS Step Functions are needed to orchestrate the workflow: a Wait state and polling with get_query_execution allow the state machine to track query completion and then trigger the next query. This avoids long‑running compute, minimizes cost, and is simpler and cheaper than Glue jobs or MWAA.
Question 39
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 the CSV and JSON files in Amazon S3 and querying them with Amazon Athena is the most cost-effective option. Athena is serverless, supports direct SQL queries on data in S3, and charges only per query scanned, eliminating the need to manage databases or clusters. AWS Glue crawlers can automatically catalog the data for SQL access. Other options involve higher ongoing costs (RDS), are not designed primarily for SQL analysis (DataBrew), or focus on BI visualization rather than ad-hoc SQL querying (QuickSight SPICE).
Question 39
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.
Show Answer
Correct Answer: B
Explanation: Amazon Redshift Data API generates events that can be monitored in Amazon EventBridge. By creating an EventBridge rule that matches Redshift Data API load or statement-completion events and targets an AWS Lambda function, the data engineer can automatically invoke the Lambda function when daily loads complete. This event-driven approach is serverless, decoupled, and purpose-built for tracking load status changes, making it more appropriate than chaining Lambdas, using CloudTrail, or inserting SQS unnecessarily.
Question 40
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: Queries are run by order date over growing S3 data. Partitioning by order date enables partition pruning so only relevant data is scanned, preventing performance degradation as data grows. Amazon Athena is serverless, scales automatically, and is pay-per-query, making it the most cost-effective option for weekly analytical queries. Redshift, EMR, and Aurora introduce higher fixed or operational costs and are unnecessary for this access pattern.
Question 40
A manufacturing company collects sensor data from its factory floor to monitor and enhance operational efficiency. The company uses Amazon Kinesis Data Streams to publish the data that the sensors collect to a data stream. Then Amazon Kinesis Data Firehose writes the data to an Amazon S3 bucket.
The company needs to display a real-time view of operational efficiency on a large screen in the manufacturing facility.
Which solution will meet these requirements with the LOWEST latency?
A. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to process the sensor data. Use a connector for Apache Flink to write data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard.
B. Configure the S3 bucket to send a notification to an AWS Lambda function when any new object is created. Use the Lambda function to publish the data to Amazon Aurora. Use Aurora as a source to create an Amazon QuickSight dashboard.
C. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to process the sensor data. Create a new Data Firehose delivery stream to publish data directly to an Amazon Timestream database. Use the Timestream database as a source to create an Amazon QuickSight dashboard.
D. Use AWS Glue bookmarks to read sensor data from the S3 bucket in real time. Publish the data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard.
Show Answer
Correct Answer: A
Explanation: Option A provides the lowest end-to-end latency. Amazon Managed Service for Apache Flink processes data directly from Kinesis Data Streams in real time, writes results to Amazon Timestream (a low-latency time-series database), and Grafana is designed for live, frequently refreshing operational dashboards. Other options add higher-latency components (S3 triggers, AWS Glue, or QuickSight refresh intervals) or rely on unsupported integrations (Firehose to Timestream), making them slower for real-time display.
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