A data engineer has two datasets that contain sales information for multiple cities and states. One dataset is named reference, and the other dataset is named primary.
The data engineer needs a solution to determine whether a specific set of values in the city and state columns of the primary dataset exactly match the same specific values in the reference dataset. The data engineer wants to use Data Quality Definition Language (DQDL) rules in an AWS Glue Data Quality job.
Which rule will meet these requirements?
A. DatasetMatch "reference” “city->ref_city, state->ref_state” = 1.0
B. Referentiallntegrity “city,state” “reference.{ref_city,ref_state}” = 1.0
C. DatasetMatch “reference” “city->ref_city, state->ref_state” = 100
D. Referentialintegrity “city,state” "reference.{ref_city,ref_state}” = 100
Show Answer
Correct Answer: B
Explanation: The DQDL ReferentialIntegrity rule is used to compare a set of columns in the primary dataset against corresponding columns in a reference dataset. A threshold of 1.0 requires a complete (100%) match. DatasetMatch is intended for broader dataset comparison semantics, whereas this question specifically describes matching a set of column values against a reference dataset using the documented ReferentialIntegrity syntax.
Question 103
A company has as JSON file that contains personally identifiable information (PII) data and non-PII data. The company needs to make the data available for querying and analysis.
The non-PII data must be available to everyone in the company. The PII data must be available only to a limited group of employees.
Which solution will meet these requirements with the LEAST operational overhead?
A. Store the JSON file in an Amazon S3 bucket. Configure AWS Glue to split the file into one file that contains the PII data and one file that contains the non-PII data. Store the output files in separate S3 buckets. Grant the required access to the buckets based on the type of user.
B. Store the JSON file in an Amazon S3 bucket. Use Amazon Macie to identify PII data and to grant access based on the type of user.
C. Store the JSON file in an Amazon S3 bucket. Catalog the file schema in AWS Lake Formation. Use Lake Formation permissions to provide access to the required data based on the type of user.
D. Create two Amazon RDS PostgreSQL databases. Load the PII data and the non-PII data into the separate databases. Grant access to the databases based on the type of user.
Show Answer
Correct Answer: C
Explanation: AWS Lake Formation can govern access to data stored in Amazon S3 at the table, column, and row level through the AWS Glue Data Catalog. Cataloging the JSON schema and using Lake Formation permissions allows the company to expose non-PII fields broadly while restricting PII fields to authorized users without creating and maintaining duplicate datasets. Amazon Macie identifies and classifies PII but does not enforce user access permissions. Splitting files or loading separate databases adds more operational overhead.
Question 104
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.
Show Answer
Correct Answer: B
Explanation: AWS Glue Data Quality provides built-in data quality rules through Data Quality Definition Language (DQDL), including IsComplete for detecting null values and ReferentialIntegrity for validating relationships between datasets. Integrating these checks directly into AWS Glue ETL jobs before writing data to storage provides a managed, serverless solution with the least operational overhead compared with custom SQL or SageMaker Data Wrangler custom logic.
Question 105
A company built a data lake and a data warehouse on AWS. The company wants to implement a data catalog to enhance the current data storage solutions. The company wants to have the capability to add business metadata and glossary information to the data catalog for every asset.
Which solution will meet these requirements with the LEAST operational overhead?
A. Use AWS Glue Catalog. Create a user table for the business glossary. Use the AWS Glue API to change table properties to add business metadata. Create a web application to access the metadata.
B. Use an Apache Hive metastore. Create a user table for the business glossary. Use the ALTER TABLE command to change table properties to add business metadata. Create a web application to access the metadata.
C. Use Amazon DataZone. Create the business glossaries. Create metadata forms. Use the Amazon DataZone data portal to access the metadata.
D. Use Amazon OpenSearch Service. Create an index for the business glossary. Create a second index for the business metadata. Use the OpenSearch Service dashboard to access the metadata.
Show Answer
Correct Answer: C
Explanation: Amazon DataZone is a fully managed data governance and catalog service that natively supports business glossaries, custom metadata forms, and a data portal for discovering and managing data assets. It provides these capabilities without requiring custom applications, minimizing operational overhead. The other options require building and maintaining custom metadata storage and interfaces or using services not designed as business data catalogs.
Question 106
A company is using Amazon S3 to build a data lake. The company needs to replicate records from multiple source databases into Apache Parquet format.
Most of the source databases are hosted on Amazon RDS. However, one source database is an on-premises Microsoft SQL Server Enterprise instance. The company needs to implement a solution to replicate existing data from all source databases and all future changes to the target S3 data lake.
Which solution will meet these requirements MOST cost-effectively?
A. Use one AWS Glue job to replicate existing data. Use a second AWS Glue job to replicate future changes.
B. Use AWS Database Migration Service (AWS DMS) to replicate existing data. Use AWS Glue jobs to replicate future changes.
C. Use AWS Database Migration Service (AWS DMS) to replicate existing data and future changes.
D. Use AWS Glue jobs to replicate existing data. Use Amazon Kinesis Data Streams to replicate future changes.
Show Answer
Correct Answer: C
Explanation: AWS Database Migration Service (AWS DMS) supports full-load migration and ongoing change data capture (CDC) from Amazon RDS and on-premises Microsoft SQL Server to Amazon S3. DMS can write data to Amazon S3 in Apache Parquet format, making it suitable for building a data lake while handling both existing data and future changes in a single, cost-effective service.
Question 107
A company uses AWS Key Management Service (AWS KMS) to encrypt an Amazon Redshift cluster. The company wants to configure a cross-Region snapshot of the Redshift cluster as part of disaster recovery (DR) strategy.
A data engineer needs to use the AWS CLI to create the cross-Region snapshot.
Which combination of steps will meet these requirements? (Choose two.)
A. Create a KMS key and configure a snapshot copy grant in the source AWS Region.
B. In the source AWS Region, enable snapshot copying. Specify the name of the snapshot copy grant that is created in the destination AWS Region.
C. In the source AWS Region, enable snapshot copying. Specify the name of the snapshot copy grant that is created in the source AWS Region.
D. Create a KMS key and configure a snapshot copy grant in the destination AWS Region.
E. Convert the cluster to a Multi-AZ deployment.
Show Answer
Correct Answer: B, D
Explanation: For cross-Region snapshot copy of an AWS KMS-encrypted Amazon Redshift cluster, you must create a KMS key in the destination Region and create a snapshot copy grant there. Then, when enabling snapshot copy from the source Region, specify the snapshot copy grant that exists in the destination Region so Redshift can encrypt copied snapshots with the destination Region KMS key.
Sources:
https://aws.amazon.com/blogs/big-data/implement-disaster-recovery-with-amazon-redshift
Question 108
A company has an application that uses an Amazon API Gateway REST API and an AWS Lambda function to retrieve data from an Amazon DynamoDB instance. Users recently reported intermittent high latency in the application's response times. A data engineer finds that the Lambda function experiences frequent throttling when the company's other Lambda functions experience increased invocations.
The company wants to ensure the API's Lambda function operate without being affected by other Lambda functions.
Which solution will meet this requirement MOST cost-effectively?
A. Increase the number of read capacity unit (RCU) in DynamoDB.
B. Configure provisioned concurrency for the Lambda function.
C. Configure reserved concurrency for the Lambda function.
D. Increase the Lambda function timeout and allocated memory.
Show Answer
Correct Answer: C
Explanation: Reserved concurrency guarantees a dedicated portion of the account's Lambda concurrency pool for this specific function, preventing it from being throttled when other Lambda functions experience traffic spikes. It directly addresses contention with other functions and does so without the additional cost of provisioned concurrency. Increasing DynamoDB capacity does not resolve Lambda throttling, and increasing timeout or memory does not isolate concurrency.
Question 109
A company has a data processing pipeline that includes several dozen steps. The data processing pipeline needs to send alerts in real time when a step fails or succeeds. The data processing pipeline uses a combination of Amazon S3 buckets, AWS Lambda functions, and AWS Step Functions state machines.
A data engineer needs to create a solution to monitor the entire pipeline.
Which solution will meet these requirements?
A. Configure the Step Functions state machines to store notifications in an Amazon S3 bucket when the state machines finish running. Enable S3 event notifications on the S3 bucket.
B. Configure the AWS Lambda functions to store notifications in an Amazon S3 bucket when the state machines finish running. Enable S3 event notifications on the S3 bucket.
C. Use AWS CloudTrail to send a message to an Amazon Simple Notification Service (Amazon SNS) topic that sends notifications when a state machine fails to run or succeeds to run.
D. Configure an Amazon EventBridge rule to react when the execution status of a state machine changes. Configure the rule to send a message to an Amazon Simple Notification Service (Amazon SNS) topic that sends notifications.
Show Answer
Correct Answer: D
Explanation: AWS Step Functions emits execution status change events (such as SUCCEEDED and FAILED) to Amazon EventBridge. An EventBridge rule can match these state machine execution status changes in near real time and forward them to an Amazon SNS topic for alerts. The S3-based options add unnecessary indirection and only cover custom writes, while CloudTrail is for API activity and is not the appropriate mechanism for execution status notifications.
Question 110
A data engineer uses Amazon Kinesis Data Streams to ingest and process records that contain user behavior data from an application every day.
The data engineer notices that the data stream is experiencing throttling because hot shards receive much more data than other shards in the data stream.
How should the data engineer resolve the throttling issue?
A. Use a random partition key to distribute the ingested records.
B. Increase the number of shards in the data stream. Distribute the records across the shards.
C. Limit the number of records that are sent each second by the producer to match the capacity of the stream.
D. Decrease the size of the records that the producer sends to match the capacity of the stream.
Show Answer
Correct Answer: B
Explanation: Hot shards indicate uneven load across shards. The option explicitly combines increasing shard capacity with distributing records across shards, addressing both capacity and distribution. A purely random partition key can improve distribution but may not be appropriate where partition-key-based ordering/grouping is required, and the exam option does not mention increasing capacity. B is the most complete answer.
Sources:
https://aws.amazon.com/blogs/big-data/optimize-write-throughput-for-amazon-kinesis-data-streams
https://www.amazonaws.cn/en/kinesis/data-streams/faqs
Question 111
A company is designing a serverless data processing workflow in AWS Step Functions that involves multiple steps. The processing workflow ingests data from an external API, transforms the data by using multiple AWS Lambda functions, and loads the transformed data into Amazon DynamoDB.
The company needs the workflow to perform specific steps based on the content of the incoming data.
Which Step Functions state type should the company use to meet this requirement?
A. Parallel
B. Choice
C. Task
D. Map
Show Answer
Correct Answer: B
Explanation: Use a Choice state to add conditional branching based on the content of the incoming data. Choice evaluates input against rules and directs the workflow to different subsequent states. Parallel runs branches concurrently, Task performs a unit of work such as invoking Lambda, and Map iterates over items in a collection.
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