A company has a gaming application that stores data in Amazon DynamoDB tables. A data engineer needs to ingest the game data into an Amazon OpenSearch Service cluster. Data updates must occur in near real time.
Which solution will meet these requirements?
A. Use AWS Step Functions to periodically export data from the Amazon DynamoDB tables to an Amazon S3 bucket. Use an AWS Lambda function to load the data into Amazon OpenSearch Service.
B. Configure an AWS Glue job to have a source of Amazon DynamoDB and a destination of Amazon OpenSearch Service to transfer data in near real time.
C. Use Amazon DynamoDB Streams to capture table changes. Use an AWS Lambda function to process and update the data in Amazon OpenSearch Service.
D. Use a custom OpenSearch plugin to sync data from the Amazon DynamoDB tables.
Show Answer
Correct Answer: C
Explanation: Amazon DynamoDB Streams provides near–real-time change data capture of item-level inserts, updates, and deletes. An AWS Lambda function can process these stream records and index or update documents in Amazon OpenSearch Service with minimal latency. The other options are batch-oriented, not near real time, or require unsupported/custom mechanisms.
Question 82
A company has an application that uses a microservice architecture. The company hosts the application on an Amazon Elastic Kubernetes Services (Amazon EKS) cluster.
The company wants to set up a robust monitoring system for the application. The company needs to analyze the logs from the EKS cluster and the application. The company needs to correlate the cluster's logs with the application's traces to identify points of failure in the whole application request flow.
Which combination of steps will meet these requirements with the LEAST development effort? (Choose two.)
A. Use FluentBit to collect logs. Use OpenTelemetry to collect traces.
B. Use Amazon CloudWatch to collect logs. Use Amazon Kinesis to collect traces.
C. Use Amazon CloudWatch to collect logs. Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) to collect traces.
D. Use Amazon OpenSearch to correlate the logs and traces.
E. Use AWS Glue to correlate the logs and traces.
Show Answer
Correct Answer: A, D
Explanation: Using Fluent Bit and OpenTelemetry provides native, low-effort log and trace collection for EKS workloads with minimal custom development. Amazon OpenSearch Service supports built-in log and trace correlation, enabling end-to-end request flow analysis and failure identification without building custom correlation pipelines. Other options introduce unnecessary streaming or ETL complexity.
Question 83
A company has a data lake in Amazon S3. The company uses AWS Glue to catalog data and AWS Glue Studio to implement data extract, transform, and load (ETL) pipelines.
The company needs to ensure that data quality issues are checked every time the pipelines run. A data engineer must enhance the existing pipelines to evaluate data quality rules based on predefined thresholds.
Which solution will meet these requirements with the LEAST implementation effort?
A. Add a new transform that is defined by a SQL query to each Glue ETL job. Use the SQL query to implement a ruleset that includes the data quality rules that need to be evaluated.
B. Add a new Evaluate Data Quality transform to each Glue ETL job. Use Data Quality Definition Language (DQDL) to implement a ruleset that includes the data quality rules that need to be evaluated.
C. Add a new custom transform to each Glue ETL job. Use the PyDeequ library to implement a ruleset that includes the data quality rules that need to be evaluated.
D. Add a new custom transform to each Glue ETL job. Use the Great Expectations library to implement a ruleset that includes the data quality rules that need to be evaluated.
Show Answer
Correct Answer: B
Explanation: AWS Glue provides a native **Evaluate Data Quality** transform in Glue Studio that is specifically designed for checking data quality on every job run. It uses **Data Quality Definition Language (DQDL)** to define rules and thresholds declaratively, integrates directly with existing Glue ETL jobs, and requires minimal additional code. The other options rely on custom SQL logic or external libraries (PyDeequ, Great Expectations), which increase development and maintenance effort. Therefore, option B meets the requirement with the least implementation effort.
Question 84
A company stores customer records in Amazon S3. The company must not delete or modify the customer record data for 7 years after each record is created. The root user also must not have the ability to delete or modify the data.
A data engineer wants to use S3 Object Lock to secure the data.
Which solution will meet these requirements?
A. Enable governance mode on the S3 bucket. Use a default retention period of 7 years.
B. Enable compliance mode on the S3 bucket. Use a default retention period of 7 years.
C. Place a legal hold on individual objects in the S3 bucket. Set the retention period to 7 years.
D. Set the retention period for individual objects in the S3 bucket to 7 years.
Show Answer
Correct Answer: B
Explanation: S3 Object Lock in compliance mode prevents objects from being deleted or overwritten by any user, including the root user, for the duration of the retention period. The retention mode cannot be changed or shortened, which satisfies the requirement that data must not be modified or deleted for 7 years and that even the root user must not have that ability.
Question 85
A retail company uses an Amazon Redshift data warehouse and an Amazon S3 bucket. The company ingests retail order data into the S3 bucket every day.
The company stores all order data at a single path within the S3 bucket. The data has more than 100 columns. The company ingests the order data from a third-party application that generates more than 30 files in CSV format every day. Each CSV file is between 50 and 70 MB in size.
The company uses Amazon Redshift Spectrum to run queries that select sets of columns. Users aggregate metrics based on daily orders. Recently, users have reported that the performance of the queries has degraded. A data engineer must resolve the performance issues for the queries.
Which combination of steps will meet this requirement with LEAST developmental effort? (Choose two.)
A. Configure the third-party application to create the files in a columnar format.
B. Develop an AWS Glue ETL job to convert the multiple daily CSV files to one file for each day.
C. Partition the order data in the S3 bucket based on order date.
D. Configure the third-party application to create the files in JSON format.
E. Load the JSON data into the Amazon Redshift table in a SUPER type column.
Show Answer
Correct Answer: A, C
Explanation: Amazon Redshift Spectrum performs best when data is stored in columnar formats (such as Parquet or ORC) because queries often select only a subset of columns. Converting the data at the source to a columnar format avoids additional ETL development and immediately reduces I/O. Partitioning the S3 data by order date allows Spectrum to prune partitions so queries that aggregate daily orders scan far less data. Together, these steps significantly improve query performance with minimal development effort compared to building and maintaining ETL jobs.
Question 86
A data engineer needs to use Amazon Neptune to develop graph applications.
Which programming languages should the engineer use to develop the graph applications? (Choose two.)
A. Gremlin
B. SQL
C. ANSI SQL
D. SPARQL
E. Spark SQL
Show Answer
Correct Answer: A, D
Explanation: Amazon Neptune supports two graph models and their corresponding query languages: the property graph model queried with Gremlin, and the RDF graph model queried with SPARQL. SQL, ANSI SQL, and Spark SQL are not used to query Neptune graph data.
Question 87
A company is building a data stream processing application. The application runs in an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. The application stores processed data in an Amazon DynamoDB table.
The company needs the application containers in the EKS cluster to have secure access to the DynamoDB table. The company does not want to embed AWS credentials in the containers.
Which solution will meet these requirements?
A. Store the AWS credentials in an Amazon S3 bucket. Grant the EKS containers access to the S3 bucket to retrieve the credentials.
B. Attach an IAM role to the EKS worker nodes, Grant the IAM role access to DynamoDUse the IAM role to set up IAM roles service accounts (IRSA) functionality.
C. Create an IAM user that has an access key to access the DynamoDB table. Use environment variables in the EKS containers to store the IAM user access key data.
D. Create an IAM user that has an access key to access the DynamoDB table. Use Kubernetes secrets that are mounted in a volume of the EKS duster nodes to store the user access key data.
Show Answer
Correct Answer: B
Explanation: The requirement is secure access to DynamoDB from EKS pods without embedding AWS credentials. Using IAM Roles for Service Accounts (IRSA) allows Kubernetes service accounts to assume an IAM role via OIDC, providing temporary credentials directly to the containers. This is the recommended and most secure approach. The other options involve storing long-term credentials (S3, environment variables, or Kubernetes secrets), which violates best practices.
Question 89
A company stores customer data that contains personally identifiable information (PII) in an Amazon Redshift cluster. The company's marketing, claims, and analytics teams need to be able to access the customer data.
The marketing team should have access to obfuscated claim information but should have full access to customer contact information. The claims team should have access to customer information for each claim that the team processes. The analytics team should have access only to obfuscated PII data.
Which solution will enforce these data access requirements with the LEAST administrative overhead?
A. Create a separate Redshift cluster for each team. Load only the required data for each team. Restrict access to clusters based on the teams.
B. Create views that include required fields for each of the data requirements. Grant the teams access only to the view that each team requires.
C. Create a separate Amazon Redshift database role for each team. Define masking policies that apply for each team separately. Attach appropriate masking policies to each team role.
D. Move the customer data to an Amazon S3 bucket. Use AWS Lake Formation to create a data lake. Use fine-grained security capabilities to grant each team appropriate permissions to access the data.
Show Answer
Correct Answer: C
Explanation: Amazon Redshift supports built-in dynamic data masking and row-level security that can be applied through database roles. By creating a role per team and attaching masking policies, the company can obfuscate PII differently for marketing and analytics while still allowing full access where required. Row-level security policies can restrict the claims team to only the claims they process. This approach avoids maintaining multiple clusters or many custom views and therefore provides the required access control with the least administrative overhead.
Question 90
A gaming company uses Amazon Kinesis Data Streams to collect clickstream data. The company uses Amazon Data Firehose delivery streams to store the data in JSON format in Amazon S3. Data scientists at the company use Amazon Athena to query the most recent data to obtain business insights.
The company wants to reduce Athena costs but does not want to recreate the data pipeline.
Which solution will meet these requirements with the LEAST management effort?
A. Change the Firehose output format to Apache Parquet. Provide a custom S3 object YYYYMMDD prefix expression and specify a large buffer size. For the existing data, create an AWS Glue extract, transform, and load (ETL) job. Configure the ETL job to combine small JSON files, convert the JSON files to large Parquet files, and add the YYYYMMDD prefix. Use the ALTER TABLE ADD PARTITION statement to reflect the partition on the existing Athena table.
B. Create an Apache Spark job that combines JSON files and converts the JSON files to Apache Parquet files. Launch an Amazon EMR ephemeral cluster every day to run the Spark job to create new Parquet files in a different S3 location. Use the ALTER TABLE SET LOCATION statement to reflect the new S3 location on the existing Athena table.
C. Create a Kinesis data stream as a delivery destination for Firehose. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to run Apache Flink on the Kinesis data stream. Use Flink to aggregate the data and save the data to Amazon S3 in Apache Parquet format with a custom S3 object YYYYMMDD prefix. Use the ALTER TABLE ADD PARTITION statement to reflect the partition on the existing Athena table.
D. Integrate an AWS Lambda function with Firehose to convert source records to Apache Parquet and write them to Amazon S3. In parallel, run an AWS Glue extract, transform, and load (ETL) job to combine the JSON files and convert the JSON files to large Parquet files. Create a custom S3 object YYYYMMDD prefix. Use the ALTER TABLE ADD PARTITION statement to reflect the partition on the existing Athena table.
Show Answer
Correct Answer: A
Explanation: The goal is to reduce Athena query costs (fewer scans, columnar format, partitioning) without recreating the pipeline and with the least management effort. Amazon Data Firehose natively supports conversion from JSON to Apache Parquet and writing directly to S3, so changing the Firehose output format and prefix is a low-effort change that avoids adding new streaming or compute components. Parquet and date-based prefixes significantly reduce Athena scan costs. For historical JSON data, a one-time AWS Glue ETL job is the managed, low-ops way to compact small files, convert to Parquet, and add partitions. The other options introduce additional systems (EMR, Flink, Lambda) that increase operational complexity and management overhead.
Question 91
A company uses Amazon Redshift as its data warehouse. Data encoding is applied to the existing tables of the data warehouse. A data engineer discovers that the compression encoding applied to some of the tables is not the best fit for the data.
The data engineer needs to improve the data encoding for the tables that have sub-optimal encoding.
Which solution will meet this requirement?
A. Run the ANALYZE command against the identified tables. Manually update the compression encoding of columns based on the output of the command.
B. Run the ANALYZE COMPRESSION command against the identified tables. Manually update the compression encoding of columns based on the output of the command.
C. Run the VACUUM REINDEX command against the identified tables.
D. Run the VACUUM RECLUSTER command against the identified tables.
Show Answer
Correct Answer: B
Explanation: ANALYZE COMPRESSION evaluates the actual data in Redshift tables and recommends the most efficient compression encoding for each column based on data distribution. Redshift does not automatically change encodings, so the engineer must manually apply the recommended encodings. ANALYZE alone only updates statistics, and VACUUM commands address sorting, reclaiming space, or reindexing—not compression encoding.
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