A company is setting up a new Amazon SageMaker Unified Studio domain. Each of the company's business units needs isolated control over its own assets, projects, and metadata. Specific datasets must be shareable with other business units upon approval. The company also requires centralized user authentication and identity mapping.
Which solution will meet these requirements?
A. Configure each business unit as a domain unit with delegated ownership and fine-grained permissions policies. Give users the ability to share assets across domain units with explicit access control. Assign API keys to users for authentication to access the domain portal.
B. Configure business units as separate domain units with owner permissions. Restrict projects exclusively to owners to prevent data sharing between domains. Configure AWS IAM Identity Center for centralized authentication. Map user profiles to their respective domain units.
C. Configure business units to be represented as separate domains. Establish isolated environments with no shared administrative policies. Configure AWS IAM Identity Center for centralized authentication. Delegate administration at the domain level.
D. Configure each business unit as a separate domain unit to manage permissions on assets, projects, and metadata. Configure AWS IAM Identity Center for centralized authentication. Map user profiles to their respective domain units. Enable cross-business unit sharing through access requests. Instruct domain unit owners to approve or deny the requests.
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Correct Answer: D
Explanation: Domain units in Amazon SageMaker Unified Studio provide delegated ownership and isolated management of assets, projects, and metadata within a single domain. AWS IAM Identity Center provides centralized authentication and identity mapping. Cross-domain-unit asset sharing through access requests with owner approval satisfies the controlled sharing requirement. Options using API keys or separate domains do not meet the centralized governance and controlled sharing requirements as effectively.
Question 32
A global company currently uses Amazon Redshift to store data and Amazon Quick Suite (previously known as Amazon QuickSight) to generate reports.
A team of business analysts have varying levels of technical expertise. Some analysts lack SQL knowledge. All the analysts need to create new reports frequently. The company wants to use natural program language queries to create dashboards and reports more efficiently.
Which solution will meet these requirements with the LEAST operational effort?
A. Use Quick Suite dashboards that have zero-ETL access to Amazon Redshift.
B. Enable Amazon Q in Quick Suite. Generate Quick Suite dashboards and reports.
C. Integrate Tableau with Amazon Redshift to give Tableau direct access to the data.
D. Use Quick Suite dashboards that have federated query access to Amazon Redshift.
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Correct Answer: B
Explanation: Amazon Q in Quick Suite (QuickSight) enables business users to use natural language to generate insights, dashboards, and reports without requiring SQL expertise. This directly addresses the need for frequent report creation with the least operational effort. The other options provide different data access methods or third-party visualization but do not add natural language report generation.
Question 33
A global ecommerce company processes customer transactions, inventory updates, and user activity logs across multiple AWS services. The company needs a scalable, fully managed, and event-driven orchestration solution to coordinate complex extract, transform, and load (ETL) workflows. The solution must use AWS Glue and Amazon EMR to process data. The data will be stored in Amazon Redshift and Amazon S3. The solution must support dependency management, automated retries, and data pipeline monitoring.
Which solution will meet these requirements?
A. Use AWS Step Functions to define an express workflow that invokes the data transformation and loading tasks across Amazon EMR and AWS Glue.
B. Create AWS Lambda functions for each step of the workflow Configure Amazon EventBridge to invoke AWS Glue jobs. Configure the Lambda functions to process and move data through the pipeline.
C. Use Apache Airflow on Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to create Directed Acyclic Graphs (DAGs) to manage ETL workflows.
D. Create an AWS Lambda function that runs each step of the workflow. Create an Amazon EventBridge scheduled rule to invoke the function every day.
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Correct Answer: C
Explanation: Amazon MWAA (managed Apache Airflow) is designed for orchestrating complex ETL workflows with DAG-based dependency management, retries, scheduling, monitoring, and native integration with AWS Glue, Amazon EMR, Amazon Redshift, and Amazon S3. Step Functions can orchestrate workflows but Express Workflows are intended for high-volume, short-duration executions rather than complex, long-running ETL pipelines. Lambda/EventBridge-based options require custom orchestration and do not provide the same workflow management capabilities.
Question 34
A company needs to implement real-time analytics for a retail shopping platform. The company wants to capture clickstream data, process the data, and load the data into Amazon Redshift for analysis. The solution must handle hundreds of megabytes of data every second.
Which solution will meet these requirements with the LEAST query latency for analytics?
A. Use Amazon Data Firehose to capture the data. Store the data in an Amazon S3 bucket. Use the COPY command to load data into Amazon Redshift.
B. Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) to capture the data. Use Amazon EMR to process the data. Use federated queries to access data in Amazon Redshift.
C. Use Amazon Kinesis Data Streams to capture the data. Use Amazon Redshift streaming ingestion to load data directly into materialized views.
D. Use Amazon DynamoDB Streams to capture the data. Use AWS Glue to process the data. Use a zero-ETL integration to load the data into Amazon Redshift.
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Correct Answer: C
Explanation: Amazon Kinesis Data Streams combined with Amazon Redshift streaming ingestion provides near real-time ingestion directly into Redshift materialized views, minimizing latency for analytics. Firehose to S3 with COPY introduces batch latency, MSK with federated queries does not directly load into Redshift for lowest-latency analytics, and DynamoDB Streams is not appropriate for high-volume clickstream ingestion and zero-ETL does not apply in this scenario.
Question 35
A company uses an Amazon Redshift cluster to manage data, including vendor sales data. The company wants to store a copy of the vendor data in an Amazon S3 bucket.
A data engineer sets up an AWS Glue job to upload the data to the S3 bucket data on a schedule. The data engineer set up a network connection to allow private traffic between Amazon Redshift and Amazon S3.
What is the next step required to meet this requirement?
A. Create an IAM role that has permission to write to the S3 bucket. Associate the IAM role with the Amazon Redshift cluster.
B. Add the S3 bucket to an AWS Glue Data Catalog. Configure Amazon Redshift Spectrum to access the Data Catalog.
C. Enable the Amazon Redshift data sharing feature. Set the S3 bucket as a target bucket for data sharing.
D. Store login credentials for Amazon Redshift in AWS Secrets Manager. Add a reference to the secret to the Glue job configuration.
Show Answer
Correct Answer: A
Explanation: An AWS Glue job that exports data from Amazon Redshift to Amazon S3 requires appropriate IAM permissions to write to the destination S3 bucket. Associating an IAM role with the Redshift cluster allows Redshift operations such as UNLOAD to access the bucket. The other options are unrelated: the Glue Data Catalog and Redshift Spectrum are for querying external data, Redshift data sharing does not export to S3, and Secrets Manager is for storing database credentials, not S3 write authorization.
Question 36
A retail company needs to implement a solution to capture data updates from multiple Amazon Aurora MySQL databases. The company needs to make the updates available for analytics in near real time. The solution must be serverless and require minimal maintenance.
Which solution will meet these requirements with the LEAST operational overhead?
A. Set up AWS Database Migration Service (AWS DMS) tasks that perform schema conversions for each database. Load the changes into Amazon Redshift Serverless.
B. Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) Connect with Debezium connectors to load data into Amazon Redshift Serverless.
C. Use AWS Database Migration Service (AWS DMS) to set up binary log replication to Amazon Kinesis Data Streams. Load the data into Amazon Redshift Serverless after schema conversion.
D. Use Aurora zero-ETL integrations with Amazon Redshift Serverless for each database to load Aurora MySQL changes in Amazon Redshift Serverless.
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Correct Answer: D
Explanation: Aurora zero-ETL integration with Amazon Redshift Serverless is purpose-built to replicate near real-time changes from Aurora MySQL into Redshift without building or managing ETL pipelines. It is fully managed, serverless, supports multiple Aurora databases via separate integrations, and has the least operational overhead compared with AWS DMS or MSK-based CDC solutions.
Question 37
A healthcare company stores patient records in an on-premises MySQL database. The company creates an application to access the MySQL database. The company must enforce security protocols to protect the patient records. The company currently rotates database credentials every 30 days to minimize the risk of unauthorized access.
The company wants a solution that does require the company to modify the application code for each credential rotation.
Which solution will meet this requirement with the LEAST operational overhead?
A. Assign an IAM role access permissions to the database. Configure the application to obtain temporary credentials through the IAM role.
B. Use AWS Key Management Service (AWS KMS) to generate encryption keys. Configure automatic key rotation. Store the encrypted credentials in an Amazon DynamoDB table.
C. Use AWS Secrets Manager to automatically rotate credentials. Allow the application to retrieve the credentials by using API calls.
D. Store credentials in an encrypted Amazon S3 bucket. Rotate the credentials every month by using an S3 Lifecycle policy. Use bucket policies to control access.
Show Answer
Correct Answer: C
Explanation: AWS Secrets Manager is designed to securely store and automatically rotate database credentials. The application retrieves the current secret through the Secrets Manager API, so credential rotation does not require updating the application for each rotation. The other options do not fit: IAM roles cannot directly authenticate to a generic on-premises MySQL database, KMS manages encryption keys rather than credential rotation, and S3 Lifecycle policies do not rotate credentials.
Question 38
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.
Show Answer
Correct Answer: C
Explanation: AWS Lake Formation with LF-Tags and Lake Formation tag-based access control (LF-TBAC) is purpose-built for managing fine-grained, scalable permissions across shared data lakes. It enables least-privilege access to subsets of data with much lower operational effort than maintaining IAM policies per department. IAM-only approaches become difficult to manage at scale, while Redshift Spectrum and RDS do not address the access-control requirement as efficiently for an S3 data lake.
Question 39
A company needs to store and analyze a large amount of IoT sensor data. The company needs to retain the data indefinitely. The company analyzes the data in an Amazon Redshift cluster.
Which solution will meet these requirements MOST cost-effectively?
A. Store the data in an Amazon S3 bucket in JSON format. Configure auto-copy data ingestion from the S3 bucket to the Redshift cluster.
B. Store the data in an Amazon S3 bucket in Apache Parquet format. Configure query access through Amazon Redshift Spectrum.
C. Store the data in an Amazon S3 bucket in JSON format. Configure query access through Amazon Redshift Spectrum.
D. Store the data in an Amazon S3 bucket in Apache Parquet format. Configure auto-copy data ingestion from the S3 bucket to the Redshift cluster.
Show Answer
Correct Answer: B
Explanation: Storing long-term IoT data in Amazon S3 is the lowest-cost storage option. Apache Parquet is a compressed columnar format that reduces storage size and minimizes data scanned during analytics. Amazon Redshift Spectrum queries the data directly in S3, avoiding the need to load and retain all historical data inside the Redshift cluster, making it the most cost-effective solution for indefinite retention and analysis.
Question 40
A data engineer is designing a log table for an application that requires continuous ingestion. The application must provide dependable API-based access to specific records from other applications. The application must handle more than 4,000 concurrent write operations and 6,500 read operations every second.
Which solution will meet these requirements?
A. Create an Amazon Redshift table with the KEY distribution style. Use the Amazon Redshift Data API to perform all read and write operations.
B. Store the log files in an Amazon S3 Standard bucket. Register the schema in AWS Glue Data Catalog. Create an external Redshift table that points to the AWS Glue schema. Use the table to perform Amazon Redshift Spectrum read operations.
C. Create an Amazon Redshift table with the EVEN distribution style. Use the Amazon Redshift Java Database Connectivity (JDBC) connector to establish a database connection. Use the database connection to perform all read and write operations.
D. Create an Amazon DynamoDB table that has provisioned capacity to meet the application's capacity needs. Use the DynamoDB table to perform all read and write operations by using DynamoDB APIs.
Show Answer
Correct Answer: D
Explanation: Amazon DynamoDB is designed for high-throughput, low-latency workloads with continuous ingestion and API-based access to individual records. It can be provisioned to support thousands of concurrent reads and writes per second. Amazon Redshift is a data warehouse optimized for analytical queries rather than high-concurrency OLTP-style read/write access, and Redshift Spectrum is read-only for data stored in Amazon S3.
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