CCNA Data Ingestion and Transformation Questions

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Option A is wrong because Amazon S3 Transfer Acceleration is a feature that speeds up uploads to S3 over long distances using edge locations, but it does not capture or stream CDC events from a database. Option B is wrong because Amazon Athena is an interactive query service for analyzing data in S3 using SQL, not a tool for capturing or streaming database changes. Option D is wrong because Amazon Kinesis Data Streams is a real-time streaming service that can ingest data, but it cannot directly capture CDC events from an RDS for PostgreSQL database without additional configuration or a separate CDC connector.

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Option A is wrong because changing the target format to JSON would not reduce memory usage; JSON is typically larger than Parquet and would increase memory consumption during serialization, not decrease it. Option B is wrong because splitting the DMS task into multiple smaller tasks would increase complexity and overhead, potentially causing additional memory pressure from multiple connections and task management, and does not directly resolve the insufficient memory error. Option C is wrong because using CDC only without full load ignores the fact that the task is already failing during continuous replication (CDC phase), and the full load may have already completed; disabling full load does not address the memory issue in CDC processing.

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Option A is wrong because Amazon Kinesis Data Analytics is for running real-time SQL or Apache Flink analytics on streaming data, not for ingesting and storing raw data into S3. Option B is wrong because Amazon SQS is a message queue service for decoupling application components, not designed for high-throughput streaming ingestion or direct delivery to S3 with format conversion. Option C is wrong because while Kinesis Data Streams with a Lambda consumer can ingest data and write to S3, it requires custom code in the Lambda function for batching, format conversion, and error handling, adding latency and complexity compared to Firehose's managed delivery.

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Option B is wrong because AWS Glue ETL jobs with JDBC connection would pull data via full table scans, placing significant load on the Oracle database and lacking native CDC capabilities, which violates the requirement to minimize source impact. Option C is wrong because Amazon Kinesis Data Firehose cannot use Oracle as a direct source; it ingests from streaming sources like Kinesis Data Streams, not relational databases via JDBC. Option D is wrong because AWS Data Pipeline with SQLActivity uses a polling-based approach that repeatedly queries the source, causing unnecessary overhead, and does not support CDC or optimized large-volume transfers like DMS.

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Option B (AWS Glue) is wrong because AWS Glue is a serverless ETL service primarily used for batch processing and cataloging data in data lakes, not for real-time streaming ingestion or continuous delivery to S3. Option C (Amazon EMR) is wrong because Amazon EMR is a cluster-based platform for running big data frameworks like Apache Spark and Hadoop, which requires provisioning and managing clusters, leading to higher operational overhead than a fully managed service. Option D (Amazon Kinesis Data Analytics) is wrong because it is designed for real-time analytics on streaming data using SQL or Apache Flink, but it does not natively deliver transformed data to S3; it would require additional services to handle the output.

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Option A is wrong because AWS Database Migration Service (DMS) is designed for database migrations and continuous replication, not for ingesting data from Kafka into S3. Option B is wrong because AWS Lambda with Kafka trigger is event-driven and not suitable for high-volume, continuous streaming of 10 TB/day due to concurrency limits and lack of managed checkpointing for large-scale Kafka ingestion. Option D is wrong because Amazon Kinesis Data Streams is a separate streaming service, not a managed Kafka connector; it cannot directly connect to an on-premises Kafka cluster as a connector.

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Option B is wrong because Amazon Kinesis Data Firehose does not natively support a JDBC connector for Oracle; it ingests data from sources like Kinesis Data Streams, AWS IoT, or custom HTTP endpoints, and using a JDBC connector would require custom code and add complexity without guaranteeing sub-minute latency for CDC. Option C is wrong because AWS Glue performing a full table export daily cannot meet the near real-time requirement of less than 1 minute latency; it is a batch-oriented service designed for periodic ETL jobs, not continuous change capture. Option D is wrong because AWS DataSync is designed for one-time or scheduled bulk data transfers of files or objects, not for capturing incremental database changes from Oracle; it syncs data at the file level, not the row-level CDC needed for a database.

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Option A is wrong because increasing the number of shards applies to Kinesis Data Streams, not to Kinesis Data Firehose, which uses a delivery stream abstraction and does not have shards; shard management is irrelevant to Firehose's buffering behavior. Option B is wrong because increasing the buffer size to 128 MB would actually increase the amount of data held before delivery, potentially worsening latency rather than reducing it, as Firehose waits to fill the buffer before flushing. Option D is wrong because 'buffering hints' is not a valid configuration in Kinesis Data Firehose; the correct parameters are buffer size and buffer interval, and enabling hints does not exist as a feature.

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Option A (Unnest) is wrong because AWS Glue does not have a built-in transform named 'Unnest'; this is a Spark SQL function (e.g., `explode`) but not a named Glue transform, and it would require manual handling of multiple nesting levels. Option B (ResolveChoice) is wrong because it is used to resolve schema ambiguities (e.g., when a column has mixed types like string and int) and does not flatten nested structures. Option D (Map) is wrong because it applies a function to each record in a DynamicFrame for row-wise transformations, but it does not inherently flatten nested arrays or objects—you would need to write custom logic to handle the nesting.

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Option A is wrong because AWS Transfer Family is used for managed file transfers over SFTP/FTPS/FTP, not for bulk data migration from Hadoop clusters; it would still be limited by the 1 Gbps network and would not complete 50 TB within a week. Option C is wrong because AWS Glue is a serverless ETL service for data preparation and cataloging, not a data transfer or migration tool; it cannot move 50 TB from on-premises to S3. Option D is wrong because AWS DataSync is optimized for online data transfer over the network, but with a 1 Gbps link, transferring 50 TB would take approximately 5 days under perfect conditions, leaving no margin for error or real-world overhead, making it unreliable for a one-week deadline.

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Option A is wrong because Amazon S3 and Amazon Athena are designed for querying data at rest, not for real-time ingestion or handling schema changes from a relational database to Redshift. Option C is wrong because AWS Glue and Amazon Redshift COPY are batch-oriented; COPY loads static files from S3 and does not support real-time CDC or automatic schema evolution from a live source. Option D is wrong because Amazon Kinesis Data Streams and AWS Lambda are suited for streaming data from applications or IoT devices, not for capturing CDC from a relational database, and Lambda would require custom code to manage schema changes and load into Redshift.

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Option A is wrong because scheduling a Glue crawler after each ETL job updates the Data Catalog based on the output data, but it does not automatically adapt the ETL job's transformation logic to new columns; it only updates metadata post-hoc, and the job itself would still need manual changes to process new columns. Option B is wrong because schema-on-read with Parquet allows flexible reading of data with varying schemas, but it does not automatically update the Data Catalog or the target table schema; the Glue job would still need explicit handling to write new columns to the target. Option D is wrong because partition indexes are used to optimize query performance on partitioned data, not to detect or adapt to schema changes; they have no mechanism for automatic column detection.

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Option A is wrong because decreasing the batch size in the S3 sink would increase the number of small writes, causing more overhead and potentially higher latency, not lower. Option B is wrong because the bottleneck is parallelism, not compute resources; a larger instance type would not resolve the mismatch between shard count and parallelism, and could even increase cost without benefit. Option D is wrong because increasing the checkpointing interval reduces checkpointing overhead but does not address the fundamental parallelism bottleneck; it may actually increase recovery time and does not improve throughput during peak hours.

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Option A is wrong because Kinesis Data Firehose is a fully managed delivery service that buffers and loads data into destinations like S3 or Redshift; it does not increase the read throughput for a consumer that is falling behind, and it removes the ability for custom consumers to process records in real time. Option C is wrong because increasing the retention period (default 24 hours, max 365 days) only keeps records longer in the stream; it does not increase the ingestion or consumption rate, so the consumer will still lag. Option D is wrong because changing the partition key to a constant value would cause all records to go to a single shard, drastically reducing throughput and making the lag worse, as the other shard would be idle.

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Option A is wrong because Kinesis Data Firehose is a delivery service that buffers and loads data into destinations like S3 or Redshift; it does not support real-time SQL analytics or reduce iterator age, and using it as a source would break the existing KDA SQL application. Option B is wrong because reducing the retention period from the default (24 hours or more) to 24 hours does not improve processing rate; it only causes data to expire sooner, potentially losing unprocessed records and increasing the risk of data loss without addressing the throughput bottleneck. Option C is wrong because increasing the shard count to 20 would double the stream's ingestion capacity, but the KDA application with parallelism of 1 would still process only one shard at a time, leaving the other 19 shards unprocessed and worsening the iterator age; the bottleneck is the application's parallelism, not the stream's shard count.

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Option B is wrong because 30 seconds would imply a buffer fill rate of ~0.167 MB/s, which does not match the given 2 MB/s arrival rate. Option C is wrong because 60 seconds is the buffer interval, but the buffer size threshold is reached much sooner at 2.5 seconds, making the interval the active trigger only if data arrival is slower. Option D is wrong because 10 seconds would correspond to a buffer size of 20 MB (2 MB/s * 10 s), which is not the configured 5 MB buffer size.

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Option B is wrong because Amazon Kinesis Data Streams is a real-time streaming service for ingesting high-throughput data from applications or devices, not designed for continuous database replication with CDC from relational databases. Option C is wrong because AWS Snowball is a physical data transfer device for offline, bulk data migration, not suitable for continuous, low-latency replication. Option D is wrong because AWS Glue is a serverless ETL service primarily for batch data transformation and cataloging, not for continuous replication with minimal latency from live databases.

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Option A is wrong because increasing Lambda memory also increases CPU allocation, which can speed up individual function execution, but it does not address the bottleneck of limited shard-level parallelism; the function is falling behind due to insufficient concurrent processing capacity, not per-invocation performance. Option B is wrong because increasing the Lambda timeout only allows the function to run longer before being terminated, but it does not improve throughput; if the function is already timing out, extending the timeout may mask the issue but does not increase the rate at which records are consumed. Option C is wrong because Kinesis Data Firehose is a delivery stream that buffers and loads data to destinations like S3 or Redshift; it does not support real-time per-record processing with custom logic like Lambda, and switching to Firehose would lose the ability to transform or react to each record individually, which is likely required for clickstream processing.

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Option A is wrong because AWS Lambda has a maximum invocation duration of 15 minutes and a payload limit of 6 MB for synchronous invocations, making it unsuitable for sustained 100 MB/s bursts without complex sharding and throttling risks. Option C is wrong because AWS Glue streaming ETL jobs reading from Amazon MSK require managing Kafka clusters and do not natively provide exactly-once semantics to S3 without additional checkpointing logic, and MSK adds operational overhead. Option D is wrong because a Kinesis Client Library (KCL) application requires provisioning and managing compute resources (e.g., EC2 or ECS) to process records, and writing directly to S3 from a custom application does not guarantee exactly-once semantics without idempotency handling, unlike Firehose's built-in buffering and retry mechanism.

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Option A is wrong because increasing the number of workers to 20 with G.1X type adds more total memory but does not address the per-worker memory limit; the shuffle phase still runs on individual executors and may exceed the 16 GB limit, plus it increases cost more than changing worker type. Option C is wrong because G.4X provides 64 GB per worker, which is excessive over-provisioning for this scenario and results in a higher cost than necessary. Option D is wrong because setting 'coalesce' reduces the number of output files but does not increase the memory available to executors during the shuffle phase; the failure is due to memory exhaustion during processing, not output file count.

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Option A is wrong because S3 Transfer Acceleration only speeds up uploads over long distances via edge locations; it does not provide scheduling, incremental data capture, or database connectivity. Option C is wrong because AWS Glue crawlers are designed for schema discovery and metadata cataloging, not for scheduled incremental data transfer from a database to S3; Glue ETL jobs can do this but require custom code, whereas DMS is the managed service for database migration. Option D is wrong because Kinesis Data Firehose ingests streaming data from producers like Kinesis streams or direct PUT, not from a relational database via JDBC; it lacks built-in change data capture for incremental database loads.

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Option A is wrong because AWS DataSync is a network-based transfer service that still relies on the internet connection; at 100 Mbps, it would take over 48 days to transfer 50 TB, exceeding the 2-week requirement. Option C is wrong because Amazon Kinesis Data Firehose is designed for real-time streaming data ingestion, not for batch transfer of 50 TB of historical data from a Hadoop cluster. Option D is wrong because AWS Glue ETL with a JDBC connection would require pulling data over the slow network, which is impractical for 50 TB, and Glue is optimized for schema discovery and transformation, not bulk data migration.

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Option B is wrong because iterator age exceeding the retention period would cause data to expire and become unavailable, not cause duplicate processing. Option C is wrong because DynamoDB throttling on checkpoint writes would cause checkpoint failures and potential re-processing, but the question states checkpointing is occurring and the issue is duplicate processing, not checkpoint failures. Option D is wrong because the 1 MB API limit applies to the total payload per PutRecords request, not per record, and exceeding it would cause write failures or retries, not duplicate processing of already-successful records.

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Option A is wrong because reducing the volume of incoming data is not a scalable solution and may not be feasible; the job should be able to handle the required data volume. Option B is wrong because AWS Glue does not allow direct configuration of memory settings per executor; memory is tied to DPU allocation, and increasing DPUs is the correct way to increase total memory. Option C is wrong because using a smaller file size for input does not address the root cause of memory exhaustion; Glue can process many small files efficiently, but the issue is the total data volume or skewed partitions, not file size.

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Option A is wrong because reducing the number of partitions would increase the data size per partition, worsening memory pressure and likely causing the same or a more severe memory error. Option C is wrong because switching to a smaller instance type would reduce available memory per executor, directly contradicting the need to resolve a memory error. Option D is wrong because S3 Select can reduce the amount of data read from S3, but it does not increase the memory available to the Glue job's Spark executors; the memory error occurs during processing, not during data ingestion.

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Option A is wrong because a t3.nano EC2 instance requires ongoing management (patching, scaling, monitoring) and incurs fixed hourly costs even at low data volumes, making it less cost-effective and operationally efficient than a serverless solution. Option C is wrong because SQS is a message queue, not a streaming ingestion endpoint; it would require additional components (e.g., an HTTP endpoint to receive POSTs and put to SQS) and the Lambda function adds latency and complexity, whereas Firehose directly accepts HTTP POSTs. Option D is wrong because API Gateway + Lambda introduces per-request costs and cold start latency, and it is over-engineered for simple streaming ingestion; Firehose provides built-in buffering, compression, and direct S3 delivery without custom code.

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Option A is wrong because kinesis:PutRecord is used to write data to a Kinesis stream, but the application reads from the input stream as a source, not writes to it; the error is not about writing. Option C is wrong because kinesis:CreateStream is an administrative action to create a new stream, which is irrelevant to an existing stream used as input. Option D is wrong because kinesis:PutRecords is for batch writing to a stream, not for reading or for accessing reference data from S3.

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Option A is wrong because 'Inherit schema from table' is not a valid Glue crawler configuration; crawlers do not inherit schemas from existing tables automatically. Option C is wrong because without enabling 'Merge tables', the crawler will create multiple tables for each distinct schema, not a single unified table. Option D is wrong because 'Each file as a separate table' would create a separate table per CSV file, which defeats the goal of having a single table with all columns merged.

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Option A is wrong because the Kinesis Client Library (KCL) is used for consuming data from Kinesis Data Streams with checkpointing in DynamoDB, but it does not integrate with Lambda as a trigger; the question specifies using Lambda for transformation, and KCL is a separate consumer model that would require managing EC2 instances or containers. Option B is wrong because increasing timeout and memory only reduces the likelihood of timeouts or out-of-memory errors but does not handle transient errors like network glitches or service throttling; it does not provide a mechanism to retry or capture failed records. Option D is wrong because a dead-letter queue (DLQ) on Lambda captures records that fail after all retries, but it does not automatically write to S3; the engineer would need additional logic to process the DLQ and write to S3, and the question specifically requires writing to S3 with no data loss, which Firehose handles directly.

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Option B is wrong because using an AWS Lambda function to transform records to Parquet before sending to Firehose introduces unnecessary complexity, additional cost, and operational overhead (e.g., managing Lambda concurrency, packaging Parquet libraries, handling record size limits), whereas Firehose's built-in conversion handles this natively. Option C is wrong because Amazon Kinesis Data Analytics is designed for real-time analytics and stream processing using SQL or Flink, not for simple format conversion; it adds latency, complexity, and cost without benefit for a straightforward schema conversion. Option D is wrong because provisioning an Amazon EMR cluster to convert data in micro-batches is a heavy, over-engineered solution that requires cluster management, scaling, and job orchestration, far exceeding the operational overhead needed for a simple format conversion that Firehose can perform automatically.

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Option B is wrong because a dead letter queue (DLQ) is used to capture failed events for asynchronous invocations, not to prevent logs from being written; it does not affect CloudWatch Logs permissions. Option C is wrong because if the Lambda function had not been invoked, the log group would not exist at all; the presence of the log group with `storedBytes` of 0 indicates the function was invoked but failed to write logs. Option D is wrong because if the log group were encrypted with a KMS key and the Lambda function lacked decrypt permission, the function would fail with an access denied error when trying to write logs, but the log group would still show `storedBytes` as 0; however, the question states the log group exists and `storedBytes` is 0, which is consistent with missing write permissions, not KMS decryption issues (KMS errors would typically produce a different error message in CloudWatch).

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