A company wants to ingest streaming data into an Amazon Redshift data warehouse from an Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster. A data engineer needs to develop a solution that provides low data access time and that optimizes storage costs.
Which solution will meet these requirements with the LEAST operational overhead?
A. Create an external schema that maps to the MSK cluster. Create a materialized view that references the external schema to consume the streaming data from the MSK topic.
B. Develop an AWS Glue streaming extract, transform, and load (ETL) job to process the incoming data from Amazon MSK. Load the data into Amazon S3. Use Amazon Redshift Spectrum to read the data from Amazon S3.
C. Create an external schema that maps to the streaming data source. Create a new Amazon Redshift table that references the external schema.
D. Create an Amazon S3 bucket. Ingest the data from Amazon MSK. Create an event-driven AWS Lambda function to load the data from the S3 bucket to a new Amazon Redshift table.
Show Answer
Correct Answer: A
Explanation: Amazon Redshift Streaming Ingestion supports directly consuming data from Amazon MSK by creating an external schema and a materialized view. This provides near-real-time access with low latency, avoids intermediate storage layers like Amazon S3 (optimizing storage costs), and minimizes operational overhead because no custom ETL jobs, Lambda functions, or orchestration are required. The materialized view manages ingestion and refresh automatically.
Question 41
A data engineer needs Amazon Athena queries to finish faster. The data engineer notices that all the files the Athena queries use are currently stored in uncompressed .csv format. The data engineer also notices that users perform most queries by selecting a specific column.
Which solution will MOST speed up the Athena query performance?
A. Change the data format from .csv to JSON format. Apply Snappy compression.
B. Compress the .csv files by using Snappy compression.
C. Change the data format from .csv to Apache Parquet. Apply Snappy compression.
D. Compress the .csv files by using gzip compression.
Show Answer
Correct Answer: C
Explanation: Amazon Athena performs best with columnar data formats. Converting CSV to Apache Parquet enables column pruning so Athena scans only the selected columns, which is especially beneficial when queries frequently target specific columns. Applying Snappy compression further reduces data scanned without significant decompression overhead. This combination provides the greatest performance improvement compared to compressing CSV or switching to JSON.
Question 42
A data engineer is using an AWS Glue ETL job to remove outdated customer records from a table that contains customer account information. The data engineer is using the following SQL command to remove customers that exist in a table named monthly_accounts_update table from the customer accounts table:
MERGE INTO accounts t USING monthly_accounts_update s
ON t.customer = s.customer -
WHEN MATCHED -
THEN DELETE -
What will happen when the data engineer runs the SQL command?
A. All customer records that exist in both the customer accounts table and the monthly_accounts_update table will be deleted from the accounts table.
B. Only customer records that are present in both tables will be retained in the customer accounts table.
C. The monthly_accounts_update table will be deleted.
D. No records will be deleted because the command syntax is not valid in AWS Glue.
Show Answer
Correct Answer: A
Explanation: In AWS Glue when using supported table formats such as Apache Iceberg, the MERGE INTO statement allows a WHEN MATCHED THEN DELETE clause. This operation deletes rows from the target table (accounts) that match rows in the source table (monthly_accounts_update) based on the join condition. Therefore, all customer records that exist in both tables will be deleted from the accounts table.
Question 42
A financial company wants to implement a data mesh. The data mesh must support centralized data governance, data analysis, and data access control. The company has decided to use AWS Glue for data catalogs and extract, transform, and load (ETL) operations.
Which combination of AWS services will implement a data mesh? (Choose two.)
A. Use Amazon Aurora for data storage. Use an Amazon Redshift provisioned cluster for data analysis.
B. Use Amazon S3 for data storage. Use Amazon Athena for data analysis.
C. Use AWS Glue DataBrew for centralized data governance and access control.
D. Use Amazon RDS for data storage. Use Amazon EMR for data analysis.
E. Use AWS Lake Formation for centralized data governance and access control.
Show Answer
Correct Answer: B, E
Explanation: A data mesh on AWS typically uses a data lake foundation with centralized governance and decentralized access. Amazon S3 is the standard storage layer for domain-owned data products, and Amazon Athena provides serverless SQL-based data analysis directly on S3. AWS Lake Formation integrates with the AWS Glue Data Catalog to provide centralized data governance and fine-grained access control (table-, column-, and row-level permissions), which is a core requirement. Other options either do not provide centralized governance (C) or are not aligned with a lake-based data mesh architecture (A, D).
Question 44
A company has an on-premises PostgreSQL database that contains customer data. The company wants to migrate the customer data to an Amazon Redshift data warehouse. The company has established a VPN connection between the on-premises database and AWS.
The on-premises database is continuously updated. The company must ensure that the data in Amazon Redshift is updated as quickly as possible.
Which solution will meet these requirements?
A. Use the pg_dump utility to generate a backup of the PostgreSQL database. Use the AWS Schema Conversion Tool (AWS SCT) to upload the backup to Amazon Redshift. Set up a cron job to perform a backup. Upload the backup to Amazon Redshift every night.
B. Create an AWS Database Migration Service (AWS DMS) full-load task. Set Amazon Redshift as the target. Configure the task to use the change data capture (CDC) feature.
C. Use the pg_dump utility to generate a backup of the PostgreSQL database. Upload the backup to an Amazon S3 bucket. Use the COPY command to import the data into Amazon Redshift.
D. Create an AWS Database Migration Service (AWS DMS) full-load task. Set Amazon Redshift as the target. Configure the task to perform a full load of the database to Amazon Redshift every night.
Show Answer
Correct Answer: B
Explanation: The database is continuously updated and the data in Amazon Redshift must be kept as current as possible. AWS Database Migration Service (AWS DMS) supports an initial full load followed by ongoing change data capture (CDC), which replicates inserts, updates, and deletes from the on‑premises PostgreSQL database to Amazon Redshift in near real time over the existing VPN connection. The other options rely on periodic backups and batch loads, which cannot keep Redshift updated quickly enough.
Question 45
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 requirement is to verify that combinations of city and state values in the primary dataset exist exactly in the reference dataset. In AWS Glue Data Quality, this is done with the ReferentialIntegrity rule, which checks whether values (or composite keys) in one dataset are present in another. ReferentialIntegrity rules use a compliance threshold between 0 and 1, where 1.0 means all records must match. DatasetMatch is used to compare entire datasets for similarity, not to enforce key-based existence checks. Therefore, the correct rule is ReferentialIntegrity with a threshold of 1.0, as shown in option B.
Question 46
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: Using Amazon S3 as a data lake and AWS Lake Formation to catalog the JSON schema allows fine-grained, column-level access control over PII versus non-PII data with minimal operational overhead. Lake Formation integrates natively with S3 and analytics services (such as Athena) and avoids duplicating or transforming data. Other options require additional processing (Glue ETL), inappropriate tooling (Macie for discovery, not access control), or significantly higher operational overhead (RDS).
Question 47
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 provides a built-in Data Quality transform using Data Quality Definition Language (DQDL) with serverless execution. The IsComplete rule natively checks for null values, and the ReferentialIntegrity rule validates foreign key relationships before data is written to storage. This integrates directly into Glue ETL pipelines without custom code or additional services, resulting in the least operational overhead compared to custom SQL logic or SageMaker-based solutions.
Question 48
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 business metadata via metadata forms, asset discovery, and a built-in data portal. It integrates with AWS data lakes and warehouses and requires minimal custom development, resulting in the least operational overhead compared to building or managing catalogs with Glue, Hive, or OpenSearch.
Question 49
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 supports both full load and ongoing change data capture (CDC) from Amazon RDS databases and on‑premises Microsoft SQL Server. DMS can continuously replicate existing data and future changes directly into Amazon S3 and natively output data in Apache Parquet format, which is ideal for a data lake. Using a single managed service for both batch replication and CDC is simpler and more cost‑effective than combining Glue jobs, Kinesis, or multiple services.
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