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".
A. Set a row-level filter to prevent user access to a row where the country is Canada.
Question 62:
A logistics company receives sensor events from thousands of trucks. Several analytics applications must read the same stream at the same time without competing for read throughput. Each application needs dedicated read throughput from every shard.
Which solution should a data engineer use?
A. Use Amazon Kinesis Data Streams consumers with enhanced fan-out. B. Use Amazon SQS standard queues with one queue shared by all analytics applications. C. Use Amazon Kinesis Data Firehose with an Amazon S3 destination for every application. D. Use AWS DMS full-load tasks to copy the events into each application database.
A. Use Amazon Kinesis Data Streams consumers with enhanced fan-out.
Explanation
Enhanced fan-out gives registered Kinesis Data Streams consumers dedicated read throughput per shard and avoids contention among consumers. A shared SQS queue distributes messages among consumers instead of letting each application read every event. Kinesis Data Firehose is designed for delivery to destinations, not for multiple independent low-latency stream consumers. AWS DMS is a database migration and replication service, not a real-time event stream fan-out service.
Question 63:
A company uses an Amazon Redshift provisioned cluster as its database. The Redshift cluster has five reserved ra3.4xlarge nodes and uses key distribution.
A data engineer notices that one of the nodes frequently has a CPU load over 90%. SQL Queries that run on the node are queued. The other four nodes usually have a CPU load under 15% during daily operations.
The data engineer wants to maintain the current number of compute nodes. The data engineer also wants to balance the load more evenly across all five compute nodes.
Which solution will meet these requirements?
A. Change the sort key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement. B. Change the distribution key to the table column that has the largest dimension. C. Upgrade the reserved node from ra3.4xlarqe to ra3.16xlarqe. D. Change the primary key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement.
B. Change the distribution key to the table column that has the largest dimension.
Explanation
Changing the distribution key to the table column that has the largest dimension will help to balance the load more evenly across all five compute nodes. The distribution key determines how the rows of a table are distributed among the slices of the cluster. If the distribution key is not chosen wisely, it can cause data skew, meaning some slices will have more data than others, resulting in uneven CPU load and query performance. By choosing the table column that has the largest dimension, meaning the column that has the most distinct values, as the distribution key, the data engineer can ensure that the rows are distributed more uniformly across the slices, reducing data skew and improving query performance. The other options are not solutions that will meet the requirements.
Option A, changing the sort key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement, will not affect the data distribution or the CPU load. The sort key determines the order in which the rows of a table are stored on disk, which can improve the performance of range-restricted queries, but not the load balancing.
Option C, upgrading the reserved node from ra3.4xlarge to ra3.16xlarge, will not maintain the current number of compute nodes, as it will increase the cost and the capacity of the cluster. Option D, changing the primary key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement, will not affect the data distribution or the CPU load either. The primary key is a constraint that enforces the uniqueness of the rows in a table, but it does not influence the data layout or the query optimization.
References:
Choosing a data distribution style Choosing a data sort key Working with primary keys
Question 64:
A company has an Amazon S3 based data lake. The data lake contains datasets that belong to multiple departments. The data lake ingests millions of customer records each day.
A data engineer needs to design an access and storage solution that allows departments to access only the subset of the company's dataset that each department requires. The solution must follow the principle of least privilege.
Which solution will meet these requirements with the LEAST operational effort?
A. Define IAM policies and IAM roles for each department. Specify the S3 access paths from the data lake that each team can access. B. Set up Amazon Redshift and Amazon Redshift Spectrum as the primary entry points for the data lake. Define an IAM role that Amazon Redshift can assume. Configure the IAM role to grant access to the data that is in Amazon S3. C. Set up AWS Lake Formation. Assign LF-Tags to AWS Glue Data Catalog resources. Enable Lake Formation tag-based access control (LF-TBAC). D. Deploy an Amazon RDS for PostgreSQL database that has the aws_s3 extension installed. Configure AWS Step Functions events to invoke an AWS Lambda function to sync the data lake with the database.
C. Set up AWS Lake Formation. Assign LF-Tags to AWS Glue Data Catalog resources. Enable Lake Formation tag-based access control (LF-TBAC).
Question 65:
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 acce 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.
A. Examine the AWS Glue job and stage details in the Spark UI.
Explanation
AWS Glue provides a built-in Spark UI (acceible from the Glue console's job run details) that exposes per-stage metrics, such as task counts, shuffle read/write sizes, and execution times, for each Spark stage.
This is the most direct way to drill down into the individual stages when troubleshooting a failing Glue Spark job.
Question 66:
A data engineer configures a large number of AWS Glue jobs that all start up around the same time. All the jobs run for less than 1 hour in the same subnet of the same VPC. All the AWS Glue jobs run on a G1 .X worker type. Some of the jobs occasionally fail with the following error: "The specified subnet does not have enough free addresses to satisfy the request".
What is the likely root cause of the error?
A. There are not enough IP addresses in the subnet. B. The G1 .X worker type cannot access the subnet. C. AWS Glue does not have the correct IAM permissions to add additional IP addresses to the subnet. D. There are not enough IP addresses in the VPC.
A. There are not enough IP addresses in the subnet.
Explanation
Each AWS Glue worker requires an elastic network interface (ENI) and consumes an IP address in the subnet. When a large number of Glue jobs start simultaneously, the subnet can run out of available IP addresses. This causes the error "The specified subnet does not have enough free addresses." The root cause is insufficient IPs in the subnet, not the VPC overall.
Question 67:
A company runs a multi-tenant Amazon EMR cluster on Amazon EC2 instances. Multiple teams perform interactive query analyses and data transformations on the data in the EMR cluster. The teams can access the cluster only through EMR Studio workspaces and EMR steps.
The teams need to use EMR steps to run Apache Spark jobs to fetch data from an Amazon DynamoDB table. The DynamoDB table contains confidential data that must be accessible to only one specific team.
The company needs to ensure that only the appropriate team can accessthe confidential data in the EMR cluster.
Which solution will meet these requirements?
A. Set up runtime roles for EMR steps. Most Voted B. Set up AWS Lake Formation permissions. C. Set up IAM roles for EMR File System (EMRFS) requests. D. Set up a DynamoDB resource-based policy.
A. Set up runtime roles for EMR steps. Most Voted
Question 68:
A company builds a new data pipeline to process data for business intelligence reports. Users have noticed that data is missing from the reports.
A data engineer needs to add a data quality check for columns that contain null values and for referential integrity at a stage before the data is added to storage.
Which solution will meet these requirements with the LEAST operational overhead?
A. Use Amazon SageMaker Data Wrangler to create a Data Quality and Insights report. B. Use AWS Glue ETL jobs to perform a data quality evaluation transform on the data. Use an IsComplete rule on the requested columns. Use a ReferentialItegrity rule for each join. C. Use AWS Glue ETL jobs to perform a SQL transform on the data to determine whether requested column contain null values. Use a second SQL transform to check referential integrity. D. Use Amazon SageMaker Data Wrangler and a custom Python transform to create custom rules to check for null values and referential integrity.
B. Use AWS Glue ETL jobs to perform a data quality evaluation transform on the data. Use an IsComplete rule on the requested columns. Use a ReferentialItegrity rule for each join.
Explanation
AWS Glue's built-in data quality evaluation transform lets you declaratively apply DQDL rules, like IsComplete for null checks and ReferentialIntegrity for joins, directly in your ETL job. This requires minimal custom code and no separate reporting or profiling infrastructure, giving you pre-load validations with the least operational overhead.
Question 69:
A company uses AWS Glue Data Catalog to index data that is uploaded to an Amazon S3 bucket every day. The company uses a daily batch processes in an extract, transform, and load (ETL) pipeline to upload data from external sources into the S3 bucket.
The company runs a daily report on the S3 data. Some days, the company runs the report before all the daily data has been uploaded to the S3 bucket. A data engineer must be able to send a message that identifies any incomplete data to an existing Amazon Simple Notification Service (Amazon SNS) topic.
Which solution will meet this requirement with the LEAST operational overhead?
A. Create data quality checks for the source datasets that the daily reports use. Create a new AWS managed Apache Airflow cluster. Run the data quality checks by using Airflow tasks that run data quality queries on the columns data type and the presence of null values. Configure Airflow Directed Acyclic Graphs (DAGs) to send an email notification that informs the data engineer about the incomplete datasets to the SNS topic. B. Create data quality checks on the source datasets that the daily reports use. Create a new Amazon EMR cluster. Use Apache Spark SQL to create Apache Spark jobs in the EMR cluster that run data quality queries on the columns data type and the presence of null values. Orchestrate the ETL pipeline by using an AWS Step Functions workflow. Configure the workflow to send an email notification that informs the data engineer about the incomplete datasets to the SNS topic. C. Create data quality checks on the source datasets that the daily reports use. Create data quality actions by using AWS Glue workflows to confirm the completeness and consistency of the datasets. Configure the data quality actions to create an event inAmazon EventBridge if a dataset is incomplete. Configure EventBridge to send the event that informs the data engineer about the incomplete datasets to the Amazon SNS topic. D. Create AWS Lambda functions that run data quality queries on the columns data type and the presence of null values. Orchestrate the ETL pipeline by using an AWS Step Functions workflow that runs the Lambda functions. Configure the Step Functions workflow to send an email notification that informs the data engineer about the incomplete datasets to the SNS topic.
C. Create data quality checks on the source datasets that the daily reports use. Create data quality actions by using AWS Glue workflows to confirm the completeness and consistency of the datasets. Configure the data quality actions to create an event inAmazon EventBridge if a dataset is incomplete. Configure EventBridge to send the event that informs the data engineer about the incomplete datasets to the Amazon SNS topic.
Explanation
AWS Glue workflows are designed to orchestrate the ETL pipeline, and you can create data quality checks to ensure the uploaded datasets are complete before running reports. If there is an issue with the data, AWS Glue workflows can trigger an Amazon EventBridge event that sends a message to an SNS topic .
AWS Glue Workflows:
AWS Glue workflows allow users to automate and monitor complex ETL processes. You can include data quality actions to check for null values, data types, and other consistency checks.
In the event of incomplete data, an EventBridge event can be generated to notify via SNS.
Question 70:
A data engineer has an AWS SAM template for a serverless data pipeline. The template defines an S3 bucket, a Lambda function, and the S3 event that invokes the function when CSV files arrive. The engineer wants to build and deploy the pipeline through AWS SAM.
Which command sequence should the engineer use?
A. Run sam build, then run sam deploy --guided. B. Run aws s3 sync, then manually create the Lambda trigger in the console. C. Run git diff, then run git commit. D. Run MSCK REPAIR TABLE, then run ALTER TABLE DROP PARTITION.
A. Run sam build, then run sam deploy --guided.
Explanation
AWS SAM commonly builds serverless application artifacts with sam build and then deploys the stack with sam deploy --guided. Manually creating triggers outside the template undermines repeatable infrastructure as code. Git commands manage source control rather than deployment. Athena partition commands are unrelated to serverless pipeline deployment.
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