A company has a data pipeline that uses an Amazon RDS instance, AWS Glue jobs, and an Amazon S3 bucket. The RDS instance and AWS Glue jobs run in a private subnet of a VPC and in the same security group.
A user made a change to the security group that prevents the AWS Glue jobs from connecting to the RDS instance. After the change, the security group contains a single rule that allows inbound SSH traffic from a specific IP address.
The company must resolve the connectivity issue.
Which solution will meet this requirement?
A. Add an inbound rule that allows all TCP traffic on all TCP ports. Set the security group as the source.
B. Add an inbound rule that allows all TCP traffic on all UDP ports. Set the private IP address of the RDS instance as the source.
C. Add an inbound rule that allows all TCP traffic on all TCP ports. Set the DNS name of the RDS instance as the source.
D. Replace the source of the existing SSH rule with the private IP address of the RDS instance. Create an outbound rule with the same source, destination, and protocol as the inbound SSH rule.
Show Answer
Correct Answer: A
Explanation: AWS Glue jobs and the Amazon RDS instance are in the same VPC and the same security group. For resources in the same security group to communicate, the security group must allow inbound traffic from itself. Adding an inbound rule that allows TCP traffic with the security group as the source enables Glue to connect to RDS on the required database port. The other options are invalid because security groups cannot use DNS names as sources, use incorrect protocols, or do not permit the necessary database traffic.
Question 16
An ecommerce company collects daily customer transaction logs in CSV format and stores the logs in Amazon S3. The company uses Amazon Athena to scan a subset of attributes from the logs on the same day the company receives each log.
Query times are increasing because of increasing transaction volume. The company wants to improve query performance.
Which solution will meet these requirements with the SHORTEST query times?
A. Convert the CSV logs into multiple ORC files for better parallelism in Athena. Partition by date in Amazon S3. Use columnar pushdown filters.
B. Convert the CSV logs to JSON. Partition by date in Amazon S3. Use Athena with dynamic filtering to reduce data scans.
C. Convert the CSV logs to Avro. Partition by date in Amazon S3. Use Athena with projection-based partitioning.
D. Convert the CSV logs to a single Apache Parquet file for each day Partition the data by date in Amazon S3. Use Athena with predicate pushdown filters.
Show Answer
Correct Answer: A
Explanation: Athena achieves the shortest query times when it can minimize data scanned and maximize parallelism. Converting CSV to a columnar format is key. Option A uses ORC, a columnar format with efficient compression and column pruning, and explicitly creates multiple files, which allows Athena to parallelize reads across many workers. Partitioning by date further reduces scanned data. Option D uses Parquet but restricts each day to a single file, which limits parallelism and can significantly slow queries as data volume grows. JSON (B) is row-based and inefficient, and Avro (C) is also row-based and slower for selective column queries.
Question 17
A data engineer notices slow query performance on a highly partitioned table that is in Amazon Athena. The table contains daily data for the previous 5 years, partitioned by date.
The data engineer wants to improve query performance and to automate partition management.
Which solution will meet these requirements?
A. Use an AWS Lambda function that runs daily. Configure the function to manually create new partitions in AWS Glue for each day’s data.
B. Use partition projection in Athena. Configure the table properties by using a date range from 5 years ago to the present.
C. Reduce the number of partitions by changing the partitioning schema from daily to monthly granularity.
D. Increase the processing capacity of Athena queries by allocating more compute resources.
Show Answer
Correct Answer: B
Explanation: Partition projection in Amazon Athena eliminates the need to store and manage individual partition metadata in AWS Glue. By defining a date range (from 5 years ago to the present), Athena can infer partitions at query time, which significantly reduces query planning overhead on highly partitioned tables and automates partition management. This directly improves performance and removes manual operational tasks.
Question 17
A company must retain specific data for 1 year. A data engineer observes that one of the company’s Amazon S3 buckets contains millions of objects that are older than 3 years. Versioning is enabled on the bucket.
To reduce costs, the data engineer implements an S3 Lifecycle rule to expire objects after 365 days. The new S3 Lifecycle rule causes the object count to double instead of decrease.
Which additional step must the data engineer take to permanently delete the old objects?
A. Disable versioning on the S3 bucket.
B. Use an AWS Lambda function to run a Python job to identify and delete objects that are older than 365 days.
C. Suspend versioning on the S3 bucket.
D. Add an additional S3 Lifecycle rule to delete the current and expired versions of objects that are older than 365 days.
Show Answer
Correct Answer: D
Explanation: With S3 versioning enabled, an expiration rule on the current version creates delete markers but leaves previous (noncurrent) versions intact, which can increase object count. To permanently remove the old data and reduce storage costs, the engineer must add a lifecycle rule that deletes noncurrent object versions (NoncurrentVersionExpiration) older than 365 days. Disabling or suspending versioning does not remove existing versions, and a Lambda job is unnecessary.
Question 18
A company uses AWS Glue Apache Spark jobs to handle extract, transform, and load (ETL) workloads. The company has enabled logging and monitoring for all AWS Glue jobs.
One of the AWS Glue jobs begins to fail. A data engineer investigates the error and wants to examine metrics for all individual stages within the job.
How can the data engineer access the stage metrics?
A. Examine the AWS Glue job and stage details in the Spark UI.
B. Examine the AWS Glue job and stage metrics in Amazon CloudWatch.
C. Examine the AWS Glue job and stage logs in AWS CloudTrail logs.
D. Examine the AWS Glue job and stage details by using the run insights feature on the job.
Show Answer
Correct Answer: A
Explanation: AWS Glue Spark jobs expose the Apache Spark UI, which provides detailed metrics at the stage and task level (execution time, shuffle, I/O, failures). CloudWatch provides job-level metrics, CloudTrail is for API auditing, and Run Insights summarizes runs rather than showing per-stage Spark metrics.
Question 18
Two data engineering teams use separate AWS accounts. Both teams request access to the same datashare in an Amazon Redshift cluster that is in a third AWS account. The datashare is named salesshare.
A data engineer must use the Amazon Redshift SQL interface to grant both data engineering teams' access to the datashare.
Which command or commands will meet this requirement?
A. GRANT USAGE ON DATASHARE salesshare TO ACCOUNTS ‘ ’ AND ‘ ’;
B. GRANT USAGE ON DATASHARE salesshare TO NAMESPACES ‘ ’ AND ‘ ’;
C. GRANT USAGE ON DATASHARE salesshare TO ACCOUNT ‘ ’; GRANT USAGE ON DATASHARE salesshare TO ACCOUNT ‘ ’;
D. GRANT USAGE ON DATASHARE salesshare TO NAMESPACE ‘ ’; GRANT USAGE ON DATASHARE salesshare TO NAMESPACE ‘ ’;
Show Answer
Correct Answer: C
Explanation: Amazon Redshift datashare access can be granted cross-account by specifying the AWS account ID. The GRANT USAGE ON DATASHARE syntax supports ACCOUNT (singular), not ACCOUNTS, and each account is granted separately. Options A and B use invalid plural keywords. Option D uses NAMESPACE, which is optional but not required when the requirement is simply to grant access to two separate AWS accounts. Therefore, issuing two GRANT statements to ACCOUNT is the correct and generally applicable solution.
Question 19
A company uses AWS Glue jobs to implement several data pipelines. The pipelines are critical to the company.
The company needs to implement a monitoring mechanism that will alert stakeholders if the pipelines fail.
Which solution will meet these requirements with the LEAST operational overhead?
A. Create an Amazon EventBridge rule to match AWS Glue job failure events. Configure the rule to target an AWS Lambda function to process events. Configure the function to send notifications to an Amazon Simple Notification Service (Amazon SNS) topic.
B. Configure an Amazon CloudWatch Logs log group for the AWS Glue jobs. Create an Amazon EventBridge rule to match new log creation events in the log group. Configure the rule to target an AWS Lambda function that reads the logs and sends notifications to an Amazon Simple Notification Service (Amazon SNS) topic if AWS Glue job failure logs are present.
C. Create an Amazon EventBridge rule to match AWS Glue job failure events. Define an Amazon CloudWatch metric based on the EventBridge rule. Set up a CloudWatch alarm based on the metric to send notifications to an Amazon Simple Notification Service (Amazon SNS) topic.
D. Configure an Amazon CloudWatch Logs log group for the AWS Glue jobs. Create an Amazon EventBridge rule to match new log creation events in the log group. Configure the rule to send notifications to an Amazon Simple Notification Service (Amazon SNS) topic.
Show Answer
Correct Answer: C
Explanation: AWS Glue emits native job state change events (including failures) to Amazon EventBridge. Creating a rule for failure events and turning it into a CloudWatch metric with an alarm allows direct notification via SNS without custom code or log parsing. This fully managed approach avoids Lambda functions and log analysis, resulting in the least operational overhead compared to the other options.
Question 19
A company needs a solution to store and query product data that has variable attributes. The solution must support unpredictable and high-volume queries with single-digit millisecond latency, even during sudden traffic spikes. The solution must retrieve items by a primary identifier named Product ID. The solution must allow flexible queries by secondary attributes named Category and Brand.
Which solution will meet these requirements?
A. Use an Amazon DynamoDB table with on-demand capacity to store product data. Store products by primary key. Use global secondary indexes (GSIs) to store secondary attributes.
B. Use Amazon Aurora with a Multi-AZ deployment to store product data. Use read replicas. Create indexes for primary and secondary attributes.
C. Use an Amazon OpenSearch Serverless cluster with dynamic scaling to store product data. Index product data by primary and secondary attributes.
D. Use Amazon ElastiCache (Redis OSS) and Amazon S3 to store product data. Use Amazon Athena to run flexible secondary attribute queries.
Show Answer
Correct Answer: A
Explanation: Amazon DynamoDB with on-demand capacity is designed for unpredictable, high-volume workloads and provides consistent single-digit millisecond latency. Using Product ID as the partition key supports efficient primary lookups, and global secondary indexes (GSIs) on Category and Brand enable flexible secondary-attribute queries. The other options do not reliably meet the latency and scalability requirements under sudden traffic spikes.
Question 20
A data engineer is troubleshooting an AWS Glue workflow that occasionally fails. The engineer determines that the failures are a result of data quality issues. A business reporting team needs to receive an email notification any time the workflow fails in the future.
Which solution will meet this requirement?
A. Create an Amazon Simple Notification Service (Amazon SNS) FIFO topic. Subscribe the team’s email account to the SNS topic. Create an AWS Lambda function that initiates when the AWS Glue job state changes to FAILED. Set the SNS topic as the target.
B. Create an Amazon Simple Notification Service (Amazon SNS) standard topic. Subscribe the team’s email account to the SNS topic. Create an Amazon EventBridge rule that triggers when the AWS Glue job state changes to FAILED. Set the SNS topic as the target.
C. Create an Amazon Simple Queue Service (Amazon SQS) FIFO queue. Subscribe the team’s email account to the SQS queue. Create an AWS Config rule that triggers when the AWS Glue job state changes to FAILED. Set the SQS queue as the target.
D. Create an Amazon Simple Queue Service (Amazon SQS) standard queue. Subscribe the team’s email account to the SQS queue. Create an Amazon EventBridge rule that triggers when the AWS Glue job state changes to FAILESet the SQS queue as the target.
Show Answer
Correct Answer: B
Explanation: Amazon EventBridge can natively detect AWS Glue job state changes, including FAILED events, without custom code. An EventBridge rule can directly target an Amazon SNS standard topic, and SNS supports email subscriptions for notifications. This approach is fully serverless, low maintenance, and purpose-built for event-driven alerts. FIFO topics, Lambda, SQS, or AWS Config are unnecessary or inappropriate for simple email notifications on Glue job failures.
Question 20
A data engineer needs to deploy a complex pipeline. The stages of the pipeline must be able to run a script. The data engineer must use only fully managed and serverless services in the pipeline.
Which solution will meet these requirements?
A. Deploy AWS Glue jobs and workflows. Use AWS Glue to run the jobs and workflows on a schedule.
B. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to build and schedule the pipeline.
C. Deploy the script to Amazon EC2 instances. Use Amazon EventBridge to run the script on a schedule.
D. Use Aws Glue DataBrew to build the pipeline. Use Amazon EventBridge to run the pipeline on a schedule.
Show Answer
Correct Answer: A
Explanation: AWS Glue jobs can run arbitrary scripts (for example, Spark or Python shell jobs), and AWS Glue Workflows can orchestrate complex, multi-stage pipelines. Glue is fully managed and serverless, requiring no infrastructure management. Amazon MWAA is managed but not fully serverless, EC2 is not serverless, and Glue DataBrew is focused on data preparation rather than complex scripted pipelines.
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