A company wants to build a dimension table in an Amazon S3 bucket. The bucket contains historical data that includes 10 million records. The historical data is 1 TB in size.
A data engineer needs a solution to update changes for up to 10,000 records in the base table every day.
Which solution will meet this requirement with the LOWEST runtime?
A. Develop an Apache Spark job in Amazon EMR to read the historical data and the new changes into two Spark DataFrames. Use the Spark update method to update the base table.
B. Develop an AWS Glue Python job to read the historical data and new changes into two Pandas DataFrames. Use the Pandas update method to update the base table.
C. Develop an AWS Glue Apache Spark job to read the historical data and new changes into two Spark DataFrames. Use the Spark update method to update the base table.
D. Develop an Amazon EMR job to read new changes into Apache Spark DataFrames. Use the Apache Hudi framework to create the base table in Amazon S3. Use the Spark update method to update the base table.
Show Answer
Correct Answer: D
Explanation: Apache Hudi is designed for record-level upserts on large datasets stored in Amazon S3, avoiding full-table rewrites. Updating only about 10,000 records in a 1 TB, 10 million-record dataset is far more efficient with Hudi than rereading and rewriting the entire dataset. Running Spark on Amazon EMR with Hudi provides the lowest runtime among the options.
Question 92
A company receives marketing campaign data from a vendor. The company ingests the data into an Amazon S3 bucket every 40 to 60 minutes. The data is in CSV format. File sizes are between 100 KB and 300 KB.
A data engineer needs to set-up an extract, transform, and load (ETL) pipeline to upload the content of each file to Amazon Redshift.
Which solution will meet these requirements with the LEAST operational overhead?
A. Create an AWS Lambda function that connects to Amazon Redshift and runs a COPY command. Use Amazon EventBridge to invoke the Lambda function based on an Amazon S3 upload trigger.
B. Create an Amazon Data Firehose stream. Configure the stream to use an AWS Lambda function as a source to pull data from the S3 bucket. Set Amazon Redshift as the destination.
C. Use Amazon Redshift Spectrum to query the S3 bucket. Configure an AWS Glue Crawler for the S3 bucket to update metadata in an AWS Glue Data Catalog.
D. Creates an AWS Database Migration Service (AWS DMS) task. Specify an appropriate data schema to migrate. Specify the appropriate type of migration to use.
Show Answer
Correct Answer: A
Explanation: AWS Lambda triggered by Amazon S3 object creation events (typically via EventBridge or S3 event notifications) can execute an Amazon Redshift COPY command to load each CSV from S3. This is a simple, serverless, low-operational-overhead ETL pattern for small, periodic files. Amazon Data Firehose does not use S3 as a source in this way, Redshift Spectrum is for querying data in S3 rather than loading it into Redshift, and AWS DMS is intended for database migration/replication rather than ingesting CSV files from S3.
Question 93
A data engineer uses AWS Lake Formation to manage access to data that is stored in an Amazon S3 bucket. The data engineer configures an AWS Glue crawler to discover data at a specific file location in the bucket, s3://examplepath. The crawler execution fails with the following error: “The S3 location: s3://examplepath is not registered.”
The data engineer needs to resolve the error.
Which solution will meet this requirement?
A. Attach an appropriate IAM policy to the IAM role of the AWS Glue crawler to grant the crawler permission to read the S3 location.
B. Register the S3 location in Lake Formation to allow the crawler to access the data.
C. Create a new AWS Glue database. Assign the correct permissions to the database for the crawler.
D. Configure the S3 bucket policy to allow cross-account access.
Show Answer
Correct Answer: B
Explanation: AWS Lake Formation requires Amazon S3 data locations to be registered before Lake Formation-managed services such as AWS Glue crawlers can access them under Lake Formation permissions. The error explicitly indicates that the S3 location is not registered. Registering s3://examplepath in Lake Formation resolves the issue. IAM permissions alone, creating a new Glue database, or modifying the bucket policy do not address this specific registration error.
Question 94
A company stores sensitive data in an Amazon Redshift table. The company needs to give specific users the ability to access the sensitive data. The company must not create duplication in the data.
Customer support users must be able to see the last four characters of the sensitive data. Audit users must be able to see the full value of the sensitive data. No other users can have the ability to access the sensitive information.
Which solution will meet these requirements?
A. Create a dynamic data masking policy to allow access based on each user role. Create IAM roles that have specific access permissions. Attach the masking policy to the column that contains sensitive data.
B. Enable metadata security on the Redshift cluster. Create IAM users and IAM roles for the customer support users and the audit users. Grant the IAM users and IAM roles permissions to view the metadata in the Redshift cluster.
C. Create a row-level security policy to allow access based on each user role. Create IAM roles that have specific access permissions. Attach the security policy to the table.
D. Create an AWS Glue job to redact the sensitive data and to load the data into a new Redshift table.
Show Answer
Correct Answer: A
Explanation: Amazon Redshift Dynamic Data Masking (DDM) is designed to mask sensitive column values based on user or role while storing only a single copy of the data. A masking policy can reveal only the last four characters for customer support users, the full value for audit users, and deny or mask the value for all other users. Row-level security filters rows rather than masking column values, metadata security is unrelated to data visibility, and creating a second table duplicates data.
Question 95
A company is building a new application that ingests CSV files into Amazon Redshift. The company has developed the frontend for the application.
The files are stored in an Amazon S3 bucket. Files are no larger than 5 MB.
A data engineer is developing the extract, transform, and load (ETL) pipeline for the CSV files. The data engineer configured a Redshift cluster and an AWS Lambda function that copies the data out of the files into the Redshift cluster.
Which additional steps should the data engineer perform to meet these requirements?
A. Configure the bucket to send S3 event notifications to Amazon EventBridge. Configure an EventBridge rule that matches S3 new object created events. Set the Lambda function as the target.
B. Configure the $3 bucket to send S3 event notifications to an Amazon Simple Queue Service (Amazon SQS) queue. Configure the Lambda function to process the queue.
C. Configure AWS Database Migration Service (AWS DMS) to stream new S3 objects to a data stream in Amazon Kinesis Data Streams. Set the Lambda function as the target of the data stream.
D. Configure an Amazon EventBridge rule that matches S3 new object created events. Set an Amazon Simple Queue Service (Amazon SQS) queue as the target of the rule. Configure the Lambda function to process the queue.
Show Answer
Correct Answer: A
Explanation: For a simple event-driven pipeline where new CSV files in Amazon S3 should invoke an existing Lambda function that loads Amazon Redshift, the most direct architecture is S3 object-created events routed through Amazon EventBridge to Lambda. The other options add unnecessary components (SQS), misuse AWS DMS (which does not stream new S3 objects into Kinesis in this way), or introduce extra indirection without a stated requirement for buffering or decoupling.
Sources:
https://www.pass4success.com/amazon/discussions/exam-amazon-dea-c01-topic-1-question-26-discussion
Question 96
A company has several new datasets in CSV and JSON formats. A data engineer needs to make the data available to a team of data analysts who will analyze the data by using SQL queries.
Which solution will meet these requirements in the MOST cost-effective way?
A. Create an Amazon RDS MySQL cluster. Use AWS Glue to transform and load the CSV and JSON files into database tables. Provide the data analysts access to the MySQL cluster.
B. Create an AWS Glue DataBrew project that contains the new data. Make the DataBrew project available to the data analysts.
C. Store the data in an Amazon S3 bucket. Use an AWS Glue crawler to catalog the S3 bucket as tables. Create an Amazon Athena workgroup that has a data usage threshold. Grant the data analysts access to the Athena workgroup.
D. Load the data into Super-fast, Parallel, In-memory Calculation Engine (SPICE) in Amazon QuickSight. Allow the data analysts to create analyses and dashboards in QuickSight.
Show Answer
Correct Answer: C
Explanation: Storing CSV and JSON data in Amazon S3, cataloging it with an AWS Glue crawler, and querying it with Amazon Athena is the most cost-effective serverless solution. Athena allows analysts to run standard SQL directly against data in S3 without provisioning databases, while the Glue Data Catalog provides table metadata. Athena workgroups can enforce data usage controls and cost thresholds. RDS requires ongoing infrastructure costs and ETL loading, DataBrew is for data preparation rather than SQL analytics, and QuickSight SPICE is for BI dashboards rather than general SQL querying.
Question 97
A data engineer needs to optimize the performance of a data pipeline that handles retail orders. Data about the orders is ingested daily into an Amazon S3 bucket.
The data engineer runs queries once each week to extract metrics from the orders data based the order date for multiple date ranges. The data engineer needs an optimization solution that ensures the query performance will not degrade when the volume of data increases.
Which solution will meet this requirement MOST cost-effectively?
A. Partition the data based on order date. Use Amazon Athena to query the data.
B. Partition the data based on order date. Use Amazon Redshift to query the data.
C. Partition the data based on load date. Use Amazon EMR to query the data.
D. Partition the data based on load date. Use Amazon Aurora to query the data.
Show Answer
Correct Answer: A
Explanation: Partitioning the S3 data by order date aligns with the query predicate, allowing Athena to prune partitions and scan only the relevant data for each date range. Athena is serverless and pay-per-query, making it the most cost-effective option for weekly analytics while maintaining performance as data volume grows. Partitioning by load date does not match the access pattern, and Redshift or Aurora would incur higher ongoing infrastructure costs for this workload.
Question 98
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 Amazon MSK through an external schema and materialized views. The materialized view consumes and incrementally refreshes streaming data with low latency, avoiding intermediate ETL pipelines and minimizing operational overhead. The other options introduce additional services (Glue, S3, Lambda) or describe an unsupported pattern (a Redshift table directly referencing an external schema).
Question 99
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: A MERGE statement with WHEN MATCHED THEN DELETE deletes rows from the target table that have matching keys in the source table. Assuming AWS Glue supports MERGE for the underlying table format (such as Apache Iceberg), the effect is to delete matching customer records from the accounts table.
Question 101
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: AWS Database Migration Service (AWS DMS) supports migrating from PostgreSQL to Amazon Redshift using an initial full load followed by change data capture (CDC). CDC continuously replicates ongoing changes from the source database, keeping Amazon Redshift updated with minimal latency over the existing VPN connection. The other options rely on periodic backups and nightly loads, which do not meet the requirement to update the data as quickly as possible.
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