CCNA Data Ingestion and Transformation Questions

Option D is correct because increasing the number of workers (DPUs) allows parallel processing, reducing runtime. Option A is wrong because the job type (Spark vs Python) affects resource usage but increasing workers is more direct. Option B is wrong because using Spark in Glue (which is default) already offers parallelism.

Option C is wrong because using a larger instance type for RDS may improve read throughput but is not a Glue optimization and could increase database cost.

Option B is correct because AWS Glue is a serverless ETL service that can transform data for Redshift. Option A is wrong because Lambda can process events but is not ideal for large-scale ETL. Option C is wrong because Athena is a query service, not for transformation.

Option D is wrong because EMR is not serverless.

Option B is correct because S3 Event Notifications can send to SQS, which decouples the producer and consumer, allowing Lambda to process at its own pace. Option A (increasing timeout) may not be enough. Option C (Step Functions) adds complexity.

Option D (SNS) does not buffer.

Option D is correct because enabling incremental checkpointing in Flink reduces the amount of data written per checkpoint by only writing changes since the last checkpoint. Option A is wrong because reducing parallelism may increase load per operator. Option B is wrong because decreasing checkpoint interval increases frequency, not size.

Option C is wrong because state TTL does not directly reduce checkpoint size.

Option B is correct because converting data to Parquet format and compressing it reduces storage space and improves query performance in Athena. Option A is wrong because storing as ORC is also good but Parquet is more common with Athena. Option C is wrong because increasing buffer size delays delivery.

Option D is wrong because enabling S3 server access logs adds cost and does not help query performance.

Option B is correct because Kinesis Data Streams can handle high throughput real-time data from many sources. Option A (SQS) is for message queuing, not streaming analytics. Option C (Firehose) could be used but lacks the ability to have multiple consumers for real-time processing.

Option D (IoT Core) is specifically for IoT but not as general-purpose.

Option A is correct. Increasing the number of workers (DPUs) adds parallelism without changing logic. Option B is wrong because increasing worker type is more expensive than adding workers.

Option C is wrong because a different engine may not be compatible. Option D is wrong because Redshift Spectrum is for querying S3, not for ETL memory.

Option A is correct because Kinesis Data Firehose can buffer and batch incoming data, invoke a Lambda function for transformation, and load directly into Redshift. Option B is wrong because KCL requires custom application management. Option C is wrong because Glue is batch-oriented.

Option D is wrong because Direct Connect is for dedicated network connections.

Option C is correct because using the exchange ID as the partition key ensures all trades from the same exchange go to the same shard, preserving order. Option A is wrong because increasing shard count would further spread data and break ordering. Option B is wrong because using a single shard would preserve order but reduce throughput due to shard limits.

Option D is wrong because implementing a custom sequencer is complex and unnecessary.

Insufficient Parallelism or KPU allocation leads to OOM. Option A is correct. Option B is wrong because checkpointing actually helps.

Option C is wrong because Flink can handle 10 MB/s with proper resources. Option D is wrong because data format does not cause OOM.

Options A, B, and D are correct. A: Increasing the number of DPUs provides more memory. B: Using a larger worker type (e.g., G.1X or G.2X) increases memory per worker.

D: Optimizing the transformation logic to reduce memory usage helps. C: Using Spark's 'coalesce' reduces partitions but may not solve memory issues. E: Using S3 Select pushes down filtering but does not address memory.

Option A is correct because AWS Snowball Edge is designed for large data transfers with limited network. Option B is wrong because Direct Connect may not be feasible if the connection is slow. Option C is wrong because DataSync is for smaller volumes and needs network.

Option D is wrong because S3 Transfer Acceleration also requires a network connection.

Option D is correct because AWS Glue supports Python shell jobs and allows installing additional Python modules via --additional-python-modules or by providing a .whl file in S3 and referencing it. Option A is wrong because Glue does not support installing packages via pip at runtime. Option B is wrong because creating a custom Docker image is for Glue for Ray or for ETL jobs with custom environment, but it's more complex than needed.

Option C is wrong because installing the library on every node is not supported; Glue manages the environment.

Options A, C, and D are correct. Kinesis Data Streams ingests the data. Kinesis Data Analytics performs real-time transformations and aggregations.

Lambda can be used to write the aggregated results to DynamoDB. Option B is wrong because Kinesis Data Firehose is for delivery to S3 or Redshift, not DynamoDB. Option E is wrong because S3 is not needed for this pipeline.

Increasing the number of DPUs improves performance but increases cost; however, it's a common approach. Partitioning the source table helps parallel reads. Option A is correct.

Option B is correct. Option C is wrong because reducing worker type would slow performance. Option D is wrong because DynamicFrame is recommended for Glue.

Option E is wrong because AWS Glue does not support auto-scaling by default without additional configuration.

Option D is correct because Relationalize is specifically designed to flatten nested JSON into relational tables. Option A (Map) applies a function to each record. Option B (Filter) removes records.

Option C (Join) combines datasets.

Option B is correct because increasing the DMS replication instance size provides more memory and CPU for processing changes. Option A is wrong because disabling validation reduces overhead but not latency due to resource constraints. Option C is wrong because the source has low write load, so batch size is not the bottleneck.

Option D is wrong because the source is PostgreSQL, not Oracle.

Option B (Use Kinesis Data Firehose as a consumer with retries and buffer settings) is correct because Firehose can buffer data and retry on failures, handling spikes. Option A (Increase shard count) is costly and requires manual scaling. Option C (Use Kinesis Client Library with exponential backoff) is a good practice but still requires custom code; Firehose is more managed.

Option D (Send data to SQS and then to Kinesis) adds complexity and cost.

Option D is correct because Amazon Kinesis Data Analytics for Apache Flink is a serverless service that can run Apache Flink applications to perform real-time transformations on streaming data. Option A (AWS Lambda) can be used but is limited by execution time and is not optimized for complex streaming transformations. Option B (Amazon Kinesis Data Firehose) can transform data using Lambda functions but is more suited for loading data.

Option C (Amazon EMR) is for batch processing, not real-time streaming.

Option C is correct because API Gateway ingests HTTP POST, sends to Kinesis Data Streams for real-time consumption, and Firehose delivers to S3. Option A (SQS) is not ideal for real-time streaming. Option B (DynamoDB Streams) is for database changes.

Option D (Lambda + S3) lacks real-time analytics capability.

Option A is correct because using columnar formats like Parquet improves performance and reduces data scanned. Option B is correct because partitioning reduces data processed. Option D is correct because increasing the number of DPUs can speed up the job, but must be balanced with cost.

Option C is wrong because S3 is already the source; moving to HDFS is not relevant. Option E is wrong because reducing DPUs would increase runtime.

The command lists objects larger than 10 MB. To automate copying, a script using AWS CLI with the --query parameter can filter and copy. Using S3 Batch Operations allows performing actions on a list of objects.

The correct approach is to use AWS CLI with a script that iterates over the filtered list and uses aws s3 cp. S3 replication is for continuous sync, not one-time copy. Lambda with S3 events triggers only on new objects, not existing ones.

S3 Inventory provides metadata but not direct copy.

Amazon Kinesis Data Streams is designed for real-time streaming data ingestion. Option A is wrong because S3 is for object storage, not real-time streaming. Option B is wrong because Lambda is compute, not an ingestion endpoint.

Option D is wrong because Glue is for ETL jobs.

Correct options: A and C. Kinesis Data Firehose can ingest streaming data and perform basic transformations. AWS Glue can be triggered by S3 events for near real-time transformation.

Option B (Amazon Athena) is a query service, not ingestion. Option D (Amazon SQS) is a message queue. Option E (AWS Step Functions) is for orchestration.

A is correct because enabling replication.factor ensures data durability in MSK. C is correct because enabling exactly-once semantics in MirrorMaker prevents duplicates. E is correct because setting offsets.topic.replication.factor ensures consumer offsets are replicated.

B is wrong because auto.create.topics.enable should be true to allow topic creation. D is wrong because SSL is optional and not required for exactly-once.

Option D is correct because increasing the parallelism of the Kinesis Data Analytics application (e.g., by increasing the number of in-application streams or ParallelismPerKPU) allows it to consume from the stream faster, reducing latency. Option A is wrong because 5 KB is well below the 1 MB/s shard limit, so increasing shards is unnecessary. Option B is wrong because enhanced fan-out is for consumers that need low latency, but does not help if the application is CPU-bound.

Option C is wrong because increasing record TTL does not affect processing speed.

Option C is correct because AWS Glue can connect to RDS and Redshift, and schedule jobs. Option A (DMS) is for ongoing replication, not necessarily for daily batch. Option B (Athena) queries S3.

Option D (EMR) is for big data processing but overkill for simple transfer.

Options A and D are correct because AWS DMS can perform full load and ongoing replication, and S3 is the target. Option B (Kinesis Data Streams) is for real-time streaming, not database migration. Option C (Glue) can be used but often requires more custom code than DMS.

Option E (Athena) is a query service, not data movement.

Option B and Option D are correct. AWS DMS can continuously replicate changes from Oracle to S3, and Amazon Kinesis Data Streams can provide low-latency streaming for near-real-time data. Option A is for batch file transfer, not real-time.

Option C is for ETL, not replication. Option E is for event-driven processing but not direct database replication.

Option A (Amazon Kinesis Data Firehose) is correct because it can capture streaming data and deliver it to S3 with minimal latency (typically 60 seconds). Option B (AWS Glue) is a batch ETL service, not real-time. Option C (Amazon SQS) is a message queue, not designed for direct S3 delivery.

Option D (Amazon RDS) is a relational database.

Option A is correct because AWS DMS CDC requires Oracle transaction logs to be stored in S3 or a custom S3 bucket for replication. Option B (CloudWatch Logs) is for monitoring, not storage. Option C (Amazon RDS) is a database, not storage.

Option D (Amazon EBS) is block storage attached to EC2, not suitable for DMS CDC.

Option B is correct because Kinesis Data Streams supports automatic scaling using the 'On-demand' capacity mode, which adjusts shards based on traffic. Option A is wrong because Auto Scaling groups are for EC2, not Kinesis. Option C is wrong because CloudWatch Alarms can trigger auto scaling, but scaling a Kinesis stream via API requires custom code; on-demand mode is simpler.

Option D is wrong because Lambda does not natively scale the stream.

Options B, C, and D are correct. Option B: AWS DMS can migrate data from on-premises databases to S3. Option C: AWS Glue can transform the data.

Option D: AWS Glue Data Catalog can store metadata. Option A is wrong because Kinesis Data Streams is for real-time streaming, not batch ingestion. Option E is wrong because Athena is for querying, not for building the ingestion pipeline.

Option C is correct because the network bandwidth is the bottleneck for transferring 50 TB over a 1 Gbps link; it will take approximately 4.7 days to transfer if bandwidth is fully utilized, but file metadata operations and number of files also matter. Option A is wrong because DataSync can handle large files efficiently. Option B is wrong because the NFS server performance is usually not the bottleneck compared to network.

Option D is wrong because S3 object storage can accept data at high throughput.

Option C is correct. Kinesis Firehose can buffer failed records and retry; you can also increase the buffer size or interval to reduce S3 PUT frequency. Option A is wrong because disabling error logging is not a solution.

Option B is wrong because S3 Transfer Acceleration is for speed, not throttling. Option D is wrong because DynamoDB is not the target.

The correct answer is to increase the number of workers. The job processes only 500 MB, so increasing worker count (DPUs) will improve parallelism. Option B is incorrect because the job processes only one hour's data, and repartitioning would add overhead.

Option C is incorrect because using Spark SQL does not inherently improve performance. Option D is incorrect because switching to a Python shell would not handle the transformation efficiently. Option A directly adds resources to speed up the job.

Option C is correct: Amazon Kinesis Agent for Amazon CloudWatch can tail log files, compress, and send to CloudWatch Logs, which can then be exported to S3. Option A (AWS DataSync) is for bulk transfers, not streaming. Option B (AWS CLI) is manual.

Option D (S3 sync) is not real-time.

Option A is correct because with 1 KB records and 64 MB buffer, it takes ~64,000 records to fill the buffer. At 1 GB/min (~1,000,000 records/min), the buffer fills in ~3.84 seconds, so the 300-second interval is not the bottleneck. However, if the S3 bucket has a PUT request rate limit, Firehose may retry and cause delays.

Option B is incorrect because 64 MB is default, not too small. Option C is incorrect because 300 seconds is actually long, but data volume is high enough to trigger delivery sooner. Option D is incorrect because compression would reduce size, not cause delays.

Option A is correct because AWS Glue can connect to various sources via JDBC and perform ETL transformations. Option B (AppFlow) is for ingesting SaaS data but does not have built-in transformation capabilities; it can only perform simple field mappings. Option C (DMS) is for database migrations, not SaaS.

Option D (Data Pipeline) is older and less flexible.

Option A (Kinesis Data Firehose) can buffer and invoke Lambda for transformation, delivering to S3. Option E (Lambda) can perform the transformation. Option B (SQS) is for decoupling, not streaming to S3.

Option C (Kinesis Data Streams) requires custom consumer. Option D (Glue) is batch-oriented.

Option C is correct because Glue expects a partition structure (e.g., year=2024/month=01/day=01) to automatically discover partitions. The current key has no partition markers. Option A is wrong because Glue can read application/octet-stream.

Option B is wrong because storage class is STANDARD. Option D is wrong because metadata is irrelevant to Glue catalog.

Option A is correct because DMS can use the source engine's native CDC capabilities (e.g., Oracle GoldenGate, MySQL binlog) or log-based CDC if available. Option B is wrong because Glue is not for real-time CDC from databases. Option C is wrong because Kinesis is for streaming data, not for pulling from databases.

Option D is wrong because Redshift is a target, not a replication tool.

Option C is correct because DMS supports target table preparation mode to convert data to Parquet format. Option A is wrong because S3 lifecycle policies do not convert formats. Option B is wrong because DMS does not support Lambda transformations natively.

Option D is wrong because switching to full load only would stop continuous replication.

Option A is correct because increasing the Lambda timeout and memory reduces timeouts while remaining cost-effective. Option B is wrong because sending to a dead-letter queue (DLQ) loses data. Option C is wrong because decreasing batch size may increase costs.

Option D is wrong because using Kinesis Data Analytics adds unnecessary complexity and cost.

Option C is correct because using a cluster with managed scaling and a larger initial core node count can reduce the time waiting for resource allocation. Option A (spot instances) may cause interruptions. Option B (reserved instances) is for long-term use, not transient clusters.

Option D (increasing instance size) may reduce processing time but not idle time.

Options A, C, and D are correct. Enable binary logging for CDC, use a bookmark to track processed records, and create a job bookmark in Glue. Option B is wrong because querying entire table daily is inefficient.

Option E is wrong because Kinesis Data Streams is for real-time, not batch CDC.

Option B is correct because AWS Transfer Family supports SFTP and can automatically transfer files to S3, with no need to manage servers. Option A is wrong because AWS DataSync requires an agent on-premises. Option C is wrong because S3 Transfer Acceleration is for speeding up transfers to S3, not for ingesting from SFTP.

Option D is wrong because Kinesis Data Firehose is for streaming data, not file-based SFTP transfers.

Increasing the number of DPUs (Data Processing Units) allocated to the Glue job provides more parallelism and memory, which can significantly speed up processing. Using G.1X worker type with more memory can also help. Using S3 Select is for filtering within S3, not for DynamoDB.

Changing to JSON format may reduce performance due to larger file size. Reducing batch size could slow down the job.

Option D is correct because Kinesis Data Firehose can use an AWS Lambda function for data transformation, and converting to Parquet can be done via a Lambda function. Option A is wrong because Firehose can deliver to S3 directly. Option B is wrong because Firehose can transform without a separate Glue job.

Option C is wrong because Firehose does not natively support Parquet conversion without a transformation.

Option A is correct because increasing DPUs and using the S3 list implementation can parallelize reading. Option B is wrong because Parquet is more efficient, but the bottleneck may be parallelism. Option C is wrong because JDBC connections are for databases, not S3.

Option D is wrong because Redshift Spectrum queries data in place, but the job is an ETL, not querying.

Option A is correct because Kinesis Data Firehose can be used as a HTTP endpoint (via API Gateway or directly with Firehose API), automatically buffers data, converts to Parquet, and writes to S3 with partitioning by date. This handles spikes without custom code. Option B (Lambda + SQS) adds complexity and still faces cold starts.

Option C (EMR) is overkill. Option D (Kinesis Data Streams + Lambda) still requires Lambda and has cold start issues.

Option B is correct because Amazon AppFlow is a fully managed integration service that supports SaaS sources, schema discovery, and data transformation. Option A (AWS Glue) is for ETL but not for SaaS ingestion directly. Option C (Amazon Kinesis) is for streaming data.

Option D (AWS Data Pipeline) is not fully managed for SaaS.

The error indicates that the data type detected by Athena's schema inference does not match the actual data. Since the Lambda function converts timestamps to Unix epoch (a number), Athena may infer the column as a string due to some records not being converted properly. Option A is wrong because partitions do not cause this error.

Option B is wrong because S3 permissions would cause a different error. Option D is wrong because data format is likely compatible.

Options A and D are correct. Set 'BufferIntervalInSeconds' to 60 seconds to flush every minute, and 'BufferSizeInMBs' to 1 MB to ensure small files are delivered quickly. Option B is wrong because enabling S3 compression does not affect delivery frequency.

Option C is wrong because converting to Parquet is a transformation, not a delivery trigger. Option E is wrong because enabling error logging does not reduce latency.

The correct answer is to use column pruning and predicate pushdown. Reading only necessary columns and filtering early reduces data scanned and processing time. Option B (increasing worker count) increases cost and may not be needed.

Option C (switching to CSV) would increase data size and slow performance. Option D (using a single large file) reduces parallelism and may harm performance.

Option B is correct because the Glue job needs permissions to access the Data Catalog. The IAM role associated with the job must have 'glue:GetTable' permission. Option A is wrong because the error is about the table not existing, not about the S3 path.

Option C is wrong because the script is correct syntactically. Option D is wrong because the job bookmark setting does not affect table discovery.

Option A is correct because the error message indicates the GlueServiceRole does not have s3:PutObject permission on the bucket. Option B is wrong because the error is about permissions, not the file name. Option C is wrong because Parquet format is not the issue.

Option D is wrong because the bucket exists (error shows it in the ARN).

Option B is correct because Lambda has a default timeout of 3 seconds, but can be increased to 15 minutes. If the function times out at 15 seconds, the timeout is set to 15 seconds. Increasing it resolves the issue.

Option A is wrong because 10 MB is within Lambda limits. Option C is wrong because memory may need increase but timeout is the immediate cause. Option D is wrong because VPC configuration can cause delays but not specifically timeout.

Amazon Kinesis Data Streams is designed for real-time streaming data ingestion from many sources. Option A is wrong because S3 is for object storage, not real-time streaming. Option B is wrong because Lambda processes data but is not a primary ingestion service.

Option D is wrong because RDS is a relational database, not for streaming ingestion.

Option A is correct because AWS Database Migration Service (DMS) supports ongoing replication from MySQL to S3 with change data capture (CDC), enabling incremental updates. Option B is wrong because AWS Glue can do batch jobs but does not natively support CDC as seamlessly as DMS. Option C is wrong because Amazon Kinesis is for streaming data, not database snapshots.

Option D is wrong because Amazon EMR is overkill for simple database ingestion.