91. A company has developed several AWS Glue extract, transform, and load (ETL) jobs to validate and transform data from Amazon S3. The ETL jobs load the data into Amazon RDS for MySQL in batches once every day. The ETL jobs use a DynamicFrame to read the S3 data.
The ETL jobs currently process all the data that is in the S3 bucket. However, the company wants the jobs to process only the daily incremental data.
Which solution will meet this requirement with the LEAST coding effort?
A. Create an ETL job that reads the S3 file status and logs the status in Amazon DynamoDB.
B. Enable job bookmarks for the ETL jobs to update the state after a run to keep track of previously processed data.
C. Enable job metrics for the ETL jobs to help keep track of processed objects in Amazon CloudWatch.
D. Configure the ETL jobs to delete processed objects from Amazon S3 after each run.
Answer
B
92. A company uses an Amazon Redshift provisioned cluster as its database. The Redshift cluster has five reserved ra3.4xlarge nodes and uses key distribution.
A data engineer notices that one of the nodes frequently has a CPU load over 90%. SQL Queries that run on the node are queued. The other four nodes usually have a CPU load under 15% during daily operations.
The data engineer wants to maintain the current number of compute nodes. The data engineer also wants to balance the load more evenly across all five compute nodes.
Which solution will meet these requirements?
A. Change the sort key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement.
B. Change the distribution key to the table column that has the largest dimension.
C. Upgrade the reserved node from ra3.4xlarge to ra3.16xlarge.
D. Change the primary key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement.
Answer
B
93. A security company stores IoT data that is in JSON format in an Amazon S3 bucket. The data structure can change when the company upgrades the IoT devices. The company wants to create a data catalog that includes the IoT data. The company’s analytics department will use the data catalog to index the data.
Which solution will meet these requirements MOST cost-effectively?
A. Create an AWS Glue Data Catalog. Configure an AWS Glue Schema Registry. Create a new AWS Glue workload to orchestrate the ingestion of the data that the analytics department will use into Amazon Redshift Serverless.
B. Create an Amazon Redshift provisioned cluster. Create an Amazon Redshift Spectrum database for the analytics department to explore the data that is in Amazon S3. Create Redshift stored procedures to load the data into Amazon Redshift.
C. Create an Amazon Athena workgroup. Explore the data that is in Amazon S3 by using Apache Spark through Athena. Provide the Athena workgroup schema and tables to the analytics department.
D. Create an AWS Glue Data Catalog. Configure an AWS Glue Schema Registry. Create AWS Lambda user defined functions (UDFs) by using the Amazon Redshift Data API. Create an AWS Step Functions job to orchestrate the ingestion of the data that the analytics department will use into Amazon Redshift Serverless.
Answer
A
94. A company stores petabytes of data in thousands of Amazon S3 buckets in the S3 Standard storage class. The data supports analytics workloads that have unpredictable and variable data access patterns.
The company does not access some data for months. However, the company must be able to retrieve all data within milliseconds. The company needs to optimize S3 storage costs.
Which solution will meet these requirements with the LEAST operational overhead?
A. Use S3 Storage Lens standard metrics to determine when to move objects to more cost-optimized storage classes. Create S3 Lifecycle policies for the S3 buckets to move objects to cost-optimized storage classes. Continue to refine the S3 Lifecycle policies in the future to optimize storage costs.
B. Use S3 Storage Lens activity metrics to identify S3 buckets that the company accesses infrequently. Configure S3 Lifecycle rules to move objects from S3 Standard to the S3 Standard-Infrequent Access (S3 Standard-IA) and S3 Glacier storage classes based on the age of the data.
C. Use S3 Intelligent-Tiering. Activate the Deep Archive Access tier.
D. Use S3 Intelligent-Tiering. Use the default access tier.
Answer
D
95. A company stores details about transactions in an Amazon S3 bucket. The company wants to log all writes to the S3 bucket into another S3 bucket that is in the same AWS Region.
Which solution will meet this requirement with the LEAST operational effort?
A. Configure an S3 Event Notifications rule for all activities on the transactions S3 bucket to invoke an AWS Lambda function. Program the Lambda function to write the event to Amazon Kinesis Data Firehose. Configure Kinesis Data Firehose to write the event to the logs S3 bucket.
B. Create a trail of management events in AWS CloudTraiL. Configure the trail to receive data from the transactions S3 bucket. Specify an empty prefix and write-only events. Specify the logs S3 bucket as the destination bucket.
C. Configure an S3 Event Notifications rule for all activities on the transactions S3 bucket to invoke an AWS Lambda function. Program the Lambda function to write the events to the logs S3 bucket.
D. Create a trail of data events in AWS CloudTraiL. Configure the trail to receive data from the transactions S3 bucket. Specify an empty prefix and write-only events. Specify the logs S3 bucket as the destination bucket.
Answer
D
96. A data engineer needs to maintain a central metadata repository that users access through Amazon EMR and Amazon Athena queries. The repository needs to provide the schema and properties of many tables. Some of the metadata is stored in Apache Hive. The data engineer needs to import the metadata from Hive into the central metadata repository.
Which solution will meet these requirements with the LEAST development effort?
A. Use Amazon EMR and Apache Ranger.
B. Use a Hive metastore on an EMR cluster.
C. Use the AWS Glue Data Catalog.
D. Use a metastore on an Amazon RDS for MySQL DB instance.
Answer
C
97. A company needs to build a data lake in AWS. The company must provide row-level data access and column-level data access to specific teams. The teams will access the data by using Amazon Athena, Amazon Redshift Spectrum, and Apache Hive from Amazon EMR.
Which solution will meet these requirements with the LEAST operational overhead?
A. Use Amazon S3 for data lake storage. Use S3 access policies to restrict data access by rows and columns. Provide data access through Amazon S3.
B. Use Amazon S3 for data lake storage. Use Apache Ranger through Amazon EMR to restrict data access by rows and columns. Provide data access by using Apache Pig.
C. Use Amazon Redshift for data lake storage. Use Redshift security policies to restrict data access by rows and columns. Provide data access by using Apache Spark and Amazon Athena federated queries.
D. Use Amazon S3 for data lake storage. Use AWS Lake Formation to restrict data access by rows and columns. Provide data access through AWS Lake Formation.
Answer
D
98. An airline company is collecting metrics about flight activities for analytics. The company is conducting a proof of concept (POC) test to show how analytics can provide insights that the company can use to increase on-time departures.
The POC test uses objects in Amazon S3 that contain the metrics in .csv format. The POC test uses Amazon Athena to query the data. The data is partitioned in the S3 bucket by date.
As the amount of data increases, the company wants to optimize the storage solution to improve query performance.
Which combination of solutions will meet these requirements? (Choose two.)
A. Add a randomized string to the beginning of the keys in Amazon S3 to get more throughput across partitions.
B. Use an S3 bucket that is in the same account that uses Athena to query the data.
C. Use an S3 bucket that is in the same AWS Region where the company runs Athena queries.
D. Preprocess the .csv data to JSON format by fetching only the document keys that the query requires.
E. Preprocess the .csv data to Apache Parquet format by fetching only the data blocks that are needed for predicates.
Answer
C, E
99. A company uses Amazon RDS for MySQL as the database for a critical application. The database workload is mostly writes, with a small number of reads.
A data engineer notices that the CPU utilization of the DB instance is very high. The high CPU utilization is slowing down the application. The data engineer must reduce the CPU utilization of the DB Instance.
Which actions should the data engineer take to meet this requirement? (Choose two.)
A. Use the Performance Insights feature of Amazon RDS to identify queries that have high CPU utilization. Optimize the problematic queries.
B. Modify the database schema to include additional tables and indexes.
C. Reboot the RDS DB instance once each week.
D. Upgrade to a larger instance size.
E. Implement caching to reduce the database query load.
Answer
A, D
100. A manufacturing company wants to collect data from sensors. A data engineer needs to implement a solution that ingests sensor data in near real time.
The solution must store the data to a persistent data store. The solution must store the data in nested JSON format. The company must have the ability to query from the data store with a latency of less than 10 milliseconds.
Which solution will meet these requirements with the LEAST operational overhead?
A. Use a self-hosted Apache Kafka cluster to capture the sensor data. Store the data in Amazon S3 for querying.
B. Use AWS Lambda to process the sensor data. Store the data in Amazon S3 for querying.
C. Use Amazon Kinesis Data Streams to capture the sensor data. Store the data in Amazon DynamoDB for querying.
D. Use Amazon Simple Queue Service (Amazon SQS) to buffer incoming sensor data. Use AWS Glue to store the data in Amazon RDS for querying.
Answer
C