CCNA Data Operations and Support Questions

The most likely cause is that the partition column data type in the table definition does not match the actual partition folder names. Athena uses the folder names to determine which partitions to scan (partition pruning). If the data type mismatch causes Athena to be unable to correctly interpret the folder names, partition pruning fails, and Athena scans all partitions, leading to excessive data scan.

Option A is incorrect because JSON format does not prevent partition pruning; it may affect compression but not pruning. Option B is incorrect because the WHERE clause filters on a non-partition column, but it also filters on the partition column 'day', so partition pruning should work if the column data type matches. Option D is incorrect because sorting within partitions does not affect scan size; it affects query performance but not the amount of data scanned.

The Lambda function must have the `iam:PassRole` permission to pass the IAM role used by the AWS Glue job. Without this permission, the Glue job cannot assume the role required for execution, leading to intermittent failures when the job is invoked. Option A is incorrect because the resource `*` already allows starting the job, and the issue is not about resource restriction.

Option B is incorrect because while S3 permissions may be necessary for the job's data access, they are not the cause of the job not starting. Option C is incorrect because batching job start requests does not address the underlying permission issue.

Increasing the number of workers (MaxCapacity) directly increases the parallelism of the Glue ETL job. Since the DPU usage is low, the job is not fully utilizing available resources; adding workers allows more concurrent processing, thus improving performance. Option A: Disabling job bookmarks avoids reading metadata but does not address low DPU usage and may cause reprocessing.

Option B: Push-down predicates filter data at the source, reducing data volume, but the primary bottleneck here is parallelism, not data volume. Option D: Changing output to CSV would increase overhead and worsen performance because Parquet is columnar and compressed, while CSV is not.

The correct first step is to increase the number of DPUs allocated to the job. The 'Insufficient Memory' error indicates that the current memory per worker is insufficient for processing the 10 GB dataset. Increasing DPUs provides more memory and compute resources, directly addressing the issue.

Option A is incorrect because Python shell jobs are single-threaded and cannot handle large datasets efficiently; Glue Spark jobs are designed for distributed processing. Option B (repartitioning) can improve performance but does not add memory; it may help after resource allocation is adequate. Option C is invalid because AWS Glue does not support Java jobs; it supports Python and Scala.

If both queries scan the same amount of data despite being on a partitioned table, it indicates that the partition column 'ds' is not being used as a filter in the queries, so partition pruning is not applied and all partitions are scanned. Option A is correct because the partition column is not used as a filter. Option B is incorrect because the table does have partitions defined (it is partitioned by 'ds').

Option C is incorrect because the table is partitioned. Option D is incorrect because partition pruning is not working correctly; if it were, the amount of data scanned would differ based on the filter.

The issue is DynamoDB throttling due to high write traffic from Lambda. The DynamoDB table is on-demand, which can throttle if bursts exceed sustained limits. Reducing the Lambda batch size from 100 to 25 decreases the number of records processed per invocation, lowering the instantaneous write rate.

Implementing exponential backoff with jitter in the Lambda code allows retries on throttled requests, making the system more resilient. Option A is not required because on-demand mode automatically scales, and switching to provisioned can be costly. Option C is incorrect because increasing shards would increase parallelism and worsen throttling.

Option D is incorrect because increasing concurrency would also increase write pressure on DynamoDB.

The error 'HIVE_CANNOT_OPEN_SPLIT' occurs when Athena attempts to read a partition but the metadata in the Glue Data Catalog does not match the actual data in S3. This is commonly caused by running an AWS Glue Crawler that updates the schema of a table (e.g., adding, removing, or changing column types) without updating the partition metadata accordingly. The crawler may update the table schema but leave existing partition metadata unchanged, leading to a mismatch.

Option A correctly identifies this cause. Option B is incorrect because a permissions issue would result in an access denied error, not a split error. Option C is incorrect because while Athena has a limit on the number of partitions per table (currently 20,000), this limit would cause a different error or query failure, not 'HIVE_CANNOT_OPEN_SPLIT'.

Option D is incorrect because concurrent queries can cause throttling or other issues but not this specific split error.

Options C and D are correct. Increasing the maximum Java heap size (mapreduce.map.java.opts) gives each task more memory to avoid heap space errors. Increasing YARN memory overhead (yarn.nodemanager.resource.memory-mb) allows containers to request more memory from YARN.

Option A is incorrect because EMRFS consistent view addresses S3 consistency, not memory. Option B is incorrect because increasing the number of containers per node without increasing total memory reduces memory per container, worsening heap issues. Option E is incorrect because decreasing YARN container size reduces available memory, counterproductive for out-of-memory errors.

Options A, C, and D are correct. Increasing the number of shards in the Kinesis stream (A) increases the ingestion capacity, which can reduce bottlenecks and lower latency. Increasing Flink parallelism (C and D) allows more parallel processing of data, improving throughput and reducing latency.

Option B is incorrect because decreasing the buffer time in Flink can lead to increased network overhead and may not improve overall performance; it can actually increase latency due to more frequent data transfers. Option E is incorrect because decreasing the checkpoint interval increases the frequency of state snapshots, adding overhead and potentially increasing latency rather than reducing it.

Implementing retry logic with exponential backoff allows the Lambda function to handle transient 'ProvisionedThroughputExceededException' errors by retrying write operations with increasing delays, which reduces the load on DynamoDB and gives the table capacity to recover. Since the function is idempotent, retries are safe. Option A is incorrect: decreasing the batch size reduces the number of records per invocation but does not directly address DynamoDB throttling; it may even increase the number of concurrent invocations, potentially worsening the issue.

Option B is incorrect: increasing reserved concurrency allows more Lambda functions to run concurrently, which would increase the write rate to DynamoDB, exacerbating throttling. Option D is incorrect: increasing Kinesis shards increases the stream's ingestion capacity but does not affect DynamoDB's throughput limits; the bottleneck is at the DynamoDB table, not the stream.

The 'Failed to add supplemental logging' error occurs because AWS DMS requires supplemental logging on the source Oracle database for CDC to capture changes. DMS typically attempts to add supplemental logging automatically, but if it fails (e.g., due to insufficient privileges), the engineer must enable it manually. Option A is correct: enabling supplemental logging manually resolves the issue.

Option B is incorrect because recreating the endpoint does not address the missing supplemental logging. Option C is incorrect because the target MySQL engine version is unrelated to the source Oracle logging requirement. Option D is incorrect because increasing the task log interval only affects log output frequency, not the underlying logging configuration.

The correct answers are D (AWS Step Functions) and E (Amazon Managed Workflows for Apache Airflow). AWS Step Functions is a serverless orchestration service that lets you coordinate multiple AWS services into flexible, multi-step workflows. It is ideal for ETL pipelines that require sequential steps, branching, and error handling.

Amazon MWAA is a managed service for Apache Airflow, an open-source platform used to programmatically author, schedule, and monitor workflows. MWAA simplifies setting up and operating Airflow, making it suitable for complex ETL orchestration with dependencies. In contrast, AWS Batch (A) is designed for batch computing jobs, not workflow orchestration.

AWS Lambda (B) is for individual serverless functions, not multi-step orchestration. AWS Data Pipeline (C) is an older ETL orchestration service that is less flexible and less commonly used compared to Step Functions and MWAA.

AWS DMS can directly write data to Amazon S3 in Parquet format when configuring the DMS task. To encrypt data at rest with a customer-managed KMS key, you specify the KMS key ID in the S3 endpoint settings. This allows DMS to encrypt objects with the specified KMS key as they are written to S3.

Option B is incorrect because it introduces an unnecessary EC2 instance; DMS can write Parquet directly without an intermediate conversion step. Option C is incorrect because DMS can write Parquet directly; converting JSON to Parquet with Glue adds complexity and is not required. Option D is incorrect because a bucket policy requiring KMS encryption does not automatically encrypt data written by DMS; the encryption must be configured in the DMS task or endpoint settings.

Increasing DPUs (Data Processing Units) allocates more compute resources to the Glue job, allowing it to process data faster, thus reducing runtime. Option E is correct: Partitioning the input data in S3 allows Glue to use partition pruning, reading only the necessary partitions instead of scanning the entire dataset, which reduces I/O and processing time. Option A is incorrect: Disabling job bookmarking does not reduce runtime; it only affects how Glue tracks processed data and might cause reprocessing.

Option C is incorrect: Reducing the number of workers would decrease parallelism, likely increasing runtime. Option D is incorrect: Changing from Spark to Python Shell would likely increase runtime because Python Shell is single-threaded and not designed for large-scale data processing compared to Spark.

The most likely cause is that the S3 bucket is in a different AWS Region than the Firehose stream. AWS Kinesis Data Firehose requires the destination S3 bucket to be in the same region as the delivery stream. If the bucket is in a different region, Firehose will fail with a 'S3 bucket does not exist' error even though the bucket exists.

Option A is incorrect because a misspelled bucket name would cause a similar error, but the error message specifically says the bucket does not exist, which can also happen if the bucket is in a different region. Option B is incorrect because default encryption does not affect the bucket's existence; Firehose can write to encrypted buckets with the proper permissions. Option D is incorrect because s3:ListBucket permission is not required for Firehose to deliver data; s3:PutObject is sufficient.

Enabling Instance Fleets allows EMR to use multiple instance types for Spot Instances, reducing the risk of interruption by diversifying across instance pools. Option E is correct because using a task instance group with Spot Instances for non-critical tasks ensures that if Spot Instances are reclaimed, only those non-critical tasks are affected, while core and master nodes running on On-Demand continue processing. Option B is incorrect because using only On-Demand increases costs.

Option C is incorrect because using Spot Instances for core nodes risks data loss or job failure if they are reclaimed, as HDFS data is stored on core nodes. Option D is incorrect because termination protection prevents accidental termination, but does not address Spot Instance interruptions.

The OutOfMemoryError in executors indicates insufficient memory per executor to handle the increased data volume. Increasing 'spark.executor.memory' and 'spark.executor.memoryOverhead' directly addresses this by providing more heap and off-heap memory without any code changes. Option A is wrong because the RDS instance size does not affect executor memory; the bottleneck is in Spark processing.

Option B is wrong because switching to AWS Glue would require code changes and may still need memory tuning, making it less efficient. Option D is wrong because adding core nodes increases parallelism but does not increase memory per executor, so the OOM could still occur.

Creating a read replica offloads read queries from the primary instance, reducing read latency with minimal application changes. The application simply needs to direct read-only queries to the replica endpoint. Option A is incorrect because deleting unused indexes may help write performance but does not directly address high read latency during peak hours.

Option B is incorrect because enabling Multi-AZ is for high availability and failover, not for improving read performance. Option C is incorrect because increasing the DB instance class can improve performance but is more disruptive and costly compared to adding a read replica, and may require downtime for resizing.

Options A and B are correct. Amazon Kinesis Data Firehose can invoke an AWS Lambda function for data transformation. You configure Firehose to call the Lambda function (A), and the Lambda function must return the transformed records in the required format, including fields like recordId, result, and data (B).

Option C is incorrect because Kinesis Data Analytics is used for real-time analytics, not for simple record transformations within Firehose. Option D is incorrect because transformation logic cannot be written directly in the Firehose delivery stream configuration; it must be implemented in a Lambda function. Option E is incorrect because using Kinesis Data Streams as a source is optional and not required for enabling Lambda transformation.

The new data uses a flat date folder (YYYY-MM-DD) instead of the existing Hive-style partition layout (year=YYYY/month=MM/day=DD). AWS Glue crawlers expect Hive-style partitions to automatically infer partitions. To include the new data without breaking the existing pipeline, the simplest solution is to store the new data in the same Hive-style format as the existing data.

Option A is incorrect because partition indexes help with query performance, not with partition format mismatch. Option B is incorrect because custom classifiers affect schema inference, not partition structure. Option D is incorrect because converting to Parquet does not change the partition folder structure.

Changing the fact table’s distribution style to KEY on the join column co-locates rows from both tables on the same nodes, eliminating the need for broadcasting and reducing network traffic. This fully optimizes the join for the large fact table. Option A is incorrect: removing the sort key can degrade range queries, and a compound sort key on the join column does not address data distribution.

Option B: while setting the dimension table to DISTSTYLE ALL would avoid broadcasting by replicating the table to all nodes, it does not improve the fact table’s own data distribution, which remains EVEN and can cause skew or suboptimal joins in other queries. Option C: adding slices increases parallelism but still requires broadcasting, so it does not address the root cause of the performance issue.

The error indicates an out-of-memory error in the Spark executor. This occurs when the executor memory is insufficient for the data being processed. Option D, changing the worker type from G.1X to G.2X, doubles the memory per worker (from 4 GB to 8 GB for Spark executors), addressing the memory issue directly.

Option A is incorrect because decreasing the number of workers reduces the total memory and parallelism, worsening the problem. Option B is incorrect because the error is not related to IAM permissions; an IAM policy for S3 write access would resolve a different error (e.g., AccessDenied). Option C is incorrect because increasing the number of workers from 2 to 4 adds more executors but does not increase memory per executor, and the error is likely due to insufficient per-executor memory.

Configuring a Dead Letter Queue (DLQ) for the Kinesis Data Firehose delivery stream allows failed records to be captured and stored for later analysis and reprocessing. While Firehose automatically retries delivery, persistent failures can lead to data loss. A DLQ (or error backup destination) ensures that records that cannot be delivered are preserved.

Option A: Decreasing the buffer interval from 60 to 30 seconds would cause more frequent deliveries, increasing the likelihood of transient failures affecting smaller batches and not reducing overall data loss. Option B: Increasing the buffer size to 10 MB does not prevent data loss; it only changes the threshold for flushing data. Since the data rate is 2 MB/min, the buffer size is rarely the trigger (interval is more likely).

Option D: Enabling data transformation with AWS Lambda to compress data reduces the size of records but does not address delivery failures; it could even introduce additional failure points from the Lambda function. Thus, only C directly addresses data loss by capturing failed records.

ProvisionedThroughputExceededException occurs when a shard's write throughput exceeds 1 MB/s, often due to hot shards from uneven partition key distribution. While increasing shard count (Option A) can help spread the load, it does not automatically fix the root cause if partition keys remain skewed. The best action is to configure auto-scaling (Option D), which in Amazon Kinesis Data Streams can be achieved by switching to on-demand mode.

On-demand mode automatically scales capacity based on traffic patterns, eliminating throttling without manual intervention. Option B decreases write capacity, worsening the issue. Option C only improves read throughput, not write.

Increasing the number of shards increases the parallelism of the stream, allowing higher throughput. Using Kinesis Data Analytics for windowed aggregation reduces the volume of data sent to Lambda, preventing timeouts during spikes. Option A is wrong because reducing batch size decreases the number of records per invocation, which can increase the number of Lambda invocations and overhead, but does not address the root cause of timeouts due to volume.

Option B is wrong because increasing the Lambda timeout to 15 minutes allows the function to run longer, but it does not increase throughput or handle spikes efficiently; it only delays failures and is not scalable. Option C is wrong because increasing memory can improve performance, but setting reserved concurrency limits the maximum number of concurrent executions, which can throttle processing during spikes and reduce scalability.

Increasing the Lambda batch size to 500 reduces the number of Lambda invocations per second from approximately 80 to 16, and reducing the batching window to 30 seconds helps spread writes more evenly across time. This lowers the concurrency of write operations to DynamoDB, which can reduce the frequency of 'WriteProvisionedThroughputExceededException' errors by staying within the provisioned 5000 WCU more consistently, even though the total write capacity required (16000 WCU) exceeds provisioned. Options A and B do not reduce the write load—batching writes (A) still consumes the same WCU, and increasing concurrency (B) worsens throttling.

Option C is irrelevant as timeout does not affect write rate.

Options A and B are correct. Multi-AZ deployment (B) provides automatic failover to a standby in a different Availability Zone, meeting the RTO of less than 30 minutes. Automated backups with point-in-time recovery (A) allow recovery to any point within the retention period, enabling an RPO of less than 5 minutes.

Option C is wrong because automated backups have a minimum retention of 1 day; you cannot set a 5-minute retention. Option D is wrong because a cross-Region read replica requires manual promotion and does not provide automatic failover, resulting in higher RTO. Option E is wrong because a single-AZ instance without Multi-AZ does not provide automatic failover, leading to RTO exceeding 30 minutes.

The 'State Machine Execution Throttled' error indicates that the state machine has exceeded the default concurrent execution limit for Step Functions. The proper resolution is to request a service quota increase from AWS Support for the concurrently running executions. Option A is incorrect: reducing the number of steps does not affect the limit on concurrent executions.

Option B is incorrect: a CloudWatch alarm can detect throttling but cannot resolve it; the alarm would only notify, not increase quotas. Option C is incorrect: API rate limits are not the same as execution throttling; Step Functions has separate limits for API call rate and concurrent executions. Adjusting API rate limits would not address execution throttling.

Using an S3 VPC Endpoint with a bucket policy that enforces HTTPS ensures all traffic to S3 is encrypted in transit, without any application changes. Option E is correct because AWS Storage Gateway can mount S3 as a file system and be configured to use HTTPS, enforcing encryption in transit. Option A is incorrect because S3 Transfer Acceleration only improves speed over long distances, it does not enforce encryption—it optionally uses HTTPS but the application must still use HTTPS.

Option B is incorrect because default encryption (SSE-S3, SSE-KMS, DSSE-KMS) encrypts data at rest, not in transit. Option C is incorrect because server-side encryption with SSE-S3 also only encrypts data at rest.

Options B and E are correct. The Glue job's IAM role must have s3:PutObject permission on the staging S3 bucket (option B). Additionally, the S3 bucket policy must explicitly allow the Glue job's IAM role to perform s3:PutObject (option E).

Option C is incorrect because AWS Glue uses the IAM role attached to the job for S3 operations, not the Glue service principal; granting the service principal write access is unnecessary and not a standard practice. Option A is incorrect because the error is a permission issue, not a script path issue. Option D is incorrect because the error is about writing to the staging bucket, not reading from the source bucket.