CCNA Data Operations Support Questions

Option D is correct because increasing the number of parallel tasks improves throughput. Option A is wrong because turning off validation reduces reliability but may help slightly. Option B is wrong because it does not affect replication lag.

Option C is wrong because batch-optimized apply is not for Aurora.

Option B is correct because an OutOfMemoryError in Glue often indicates that the DPU (worker) allocation is insufficient for the data size. Option A is wrong because the error is not about file format. Option C is wrong because the job is not about joining; the error is heap space.

Option D is wrong because the error is not about partitioning.

Option D is correct because VACUUM reclaims space from deleted rows. Option A is wrong because ANALYZE updates statistics. Option B is wrong because COPY loads data.

Option C is wrong because UNLOAD exports data.

Options B and D are correct. Multi-node clusters with automatic replication provide high availability. Option A is wrong because single-node clusters are not HA.

Option C is wrong because concurrency scaling improves performance, not HA. Option E is wrong because cross-region snapshot is disaster recovery, not HA.

Option D is correct because S3 bucket policies may deny access even if the role allows it. Option A is wrong because the role is already attached. Option B is wrong because encryption would cause different errors.

Option C is wrong because if the role exists, it should work; the issue is likely external.

Option C is correct because concurrent delete operations can cause a split to be read after deletion. Option A is incorrect because the error mentions the file path, not table metadata. Option B is incorrect because ACLs would cause permission errors.

Option D is incorrect because S3 is strongly consistent for new objects but deletions can race.

Option A is correct because using DISTSTYLE KEY on join columns can reduce data redistribution. Option D is correct because increasing the number of slices distributes data across more compute nodes. Option B is incorrect because SORTKEY helps with range restriction, not joins.

Option C is incorrect because DISTSTYLE ALL on both tables would broadcast both, which is inefficient. Option E is incorrect because dropping and recreating tables is disruptive and may not help.

Option C is correct because DynamoDB auto scaling adjusts capacity based on load, and adding a dead-letter queue (DLQ) for failed records prevents data loss and allows reprocessing. Option A is wrong because increasing Lambda timeout does not address DynamoDB throttling. Option B is wrong because Lambda reserved concurrency may limit processing, not help.

Option D is wrong because Kinesis shard count does not affect DynamoDB throughput.

Option B is correct because S3 Object Lock with retention mode prevents overwrites and deletes. Option A is wrong because it only prevents accidental deletion. Option C is wrong because it only protects against delete, not overwrite.

Option D is wrong because it manages lifecycle, not immutability.

Option D is correct because S3 bucket policies can override IAM permissions; if the bucket policy denies access from the EMR cluster, the write will fail even with IAM allow. Option A is wrong because S3 Lifecycle rules do not affect write permissions. Option B is wrong because KMS permissions are needed only if the bucket uses SSE-KMS.

Option C is wrong because EMR cluster termination would cause a different error.

Option C is correct because the last modified date shows when the object was last modified, and lifecycle policies are based on object age. The engineer needs the current date to calculate the age. Option A is wrong because ContentLength is not relevant.

Option B is wrong because ETag is for integrity. Option D is wrong because metadata is shown but not needed for age calculation.

Option B is correct. On-demand DynamoDB can scale, but it has a per-partition throughput limit. Reducing the Lambda batch size reduces the number of concurrent writes per shard, decreasing the chance of hitting partition limits.

Option A is wrong because increasing shards would increase concurrency, worsening throttling. Option C is wrong because decreasing reserved concurrency could cause Lambda throttling but not DynamoDB throttling. Option D is wrong because using a DynamoDB stream adds complexity and does not directly reduce write throttling.

Option B is correct because DynamoDB Accelerator (DAX) provides in-memory caching, reducing read latency. Option D is correct because Global Tables provide local read replicas, reducing latency for global users. Option A is wrong because GSI adds secondary indexes but does not reduce read latency for primary key lookups.

Option C is wrong because increasing write capacity does not affect read performance. Option E is wrong because DynamoDB Streams is for change capture, not reducing latency.

Option B is correct because Lake Formation permissions are separate from IAM; the analyst needs SELECT permissions granted via Lake Formation. Option A is wrong because S3 bucket policy may allow access but Lake Formation can override. Option C is wrong because the table appears, so catalog permissions are likely fine.

Option D is wrong because Athena workgroup permissions are not related.

Option D is correct because implementing exponential backoff in the Glue job will handle throttling gracefully. Option A is wrong because increasing provisioned capacity increases cost and may not be necessary. Option B is wrong because using DynamoDB Accelerator does not affect throughput limits.

Option C is wrong because changing the schedule may not solve the issue.

Option B is correct because the log group shows 'storedBytes': 0, indicating no logs have been written. The most common cause is that the Lambda execution role lacks permission to create log streams and put logs. Option A is wrong because the log group exists.

Option C is wrong because retention does not prevent logs from being written. Option D is wrong because the CLI command shows the log group exists.

Increasing the number of DPUs allocated to the Glue job provides more memory and processing capacity, which directly addresses the OutOfMemoryError. Option A is wrong because it increases parallelism without addressing memory. Option B is wrong because the error is not related to schema.

Option D is wrong because S3 performance is not the issue.

Option B is correct because Glue job bookmarks track processed data and enable incremental processing without reprocessing full tables. Option A is wrong because CDC from DMS adds complexity and cost. Option C is wrong because full scans are inefficient and costly.

Option D is wrong because querying by timestamp may miss updates if timestamps are not indexed or updated.

Options A and C are correct. Glue needs s3:GetObject to read from S3 and s3:PutObject to write output. Option B is incorrect because glue:StartJobRun is not needed for the job itself.

Option D is incorrect because kms:Decrypt is needed only if S3 uses KMS encryption. Option E is incorrect because dynamodb:GetItem is not relevant unless accessing DynamoDB.

Option A is correct because DMS uses multiple tasks in parallel to maximize throughput, which can cause high CPU on the target. Option B is wrong because DMS does not use read replicas during full load. Option C is wrong because LOB settings affect column size handling, not CPU load.

Option D is wrong because validation occurs after migration, not during full load.

Option D is correct because Kinesis Firehose can buffer the small files and write larger batches to S3, then use a scheduled COPY or Redshift Spectrum. Option A still processes each file individually. Option B is for streaming, not batch.

Option C adds complexity and cost.

Option A is correct because increasing the number of DynamoDB reads per segment increases parallelism when reading from DynamoDB. Option C is correct because increasing the number of DPUs (data processing units) allocated to the job increases parallelism. Option E is correct because using the 'groupFiles' option can reduce the number of small files written to S3, reducing write overhead.

Option B is wrong because decreasing the batch size reduces throughput. Option D is wrong because disabling job bookmark may cause reprocessing but does not improve performance; it may actually degrade it.

Option A (enable Multi-AZ) and Option D (automated snapshots with cluster recreation) are correct. Multi-AZ provides synchronous replication across AZs for high availability. Automated snapshots allow point-in-time recovery and cluster replacement in case of failure.

Option B is wrong because concurrency scaling improves query performance, not availability. Option C is wrong because manual snapshots require manual intervention. Option E is wrong because workload management (WLM) does not affect availability.

Option A is correct because the Status 'Enabled' means versioning is turned on for the bucket. Option B is wrong because MFA Delete is not required for versioning. Option C is wrong because versioning does not automatically expire old versions.

Option D is wrong because versioning does not encrypt objects.

Option C is correct because increasing shards increases parallelism. Option A is wrong because it reduces batch size. Option B is wrong because it doesn't help throughput.

Option D is wrong because it adds latency.

Option B is correct because adding a dead-letter queue (DLQ) on the Lambda function captures failed records for later reprocessing, preventing data loss. Option A (disable retries) would lose data. Option C (increase DynamoDB write capacity) addresses the throughput but does not handle failures that may still occur.

Option D (reduce shards) would reduce the ingestion rate but may not solve the issue and could cause data loss.

Option A is correct because enabling DynamoDB auto scaling dynamically adjusts throughput to match demand. Option B is wrong because increasing Lambda batch size would increase write requests, worsening the problem. Option C is wrong because Lambda retries are already built-in but would still fail.

Option D is wrong because disabling retries would lose data.

Option C is correct because the error indicates that the task is trying to insert rows that already exist. Enabling 'BatchApplyEnabled' and setting 'TaskRecoveryTableEnabled' to false prevents duplicate handling errors during replication. Option A is wrong because truncating would lose data.

Option B is wrong because disabling constraints may cause data integrity issues. Option D is wrong because dropping indexes does not prevent duplicate key violations.

Option A is correct because compacting small files into larger ones reduces overhead. Option C is correct because partitioning limits data scanned. Option B is wrong because converting to Parquet helps but is not one of the two selected here (though it is also good).

Option D is wrong because more files increase overhead. Option E is wrong because disabling encryption does not affect performance.

Options B and D are correct. Enabling multi-node cluster with RA3 node types provides managed storage and resilience. Enabling cross-region snapshot copy ensures data can be restored in another region.

Option A is incorrect because concurrency scaling does not provide high availability. Option C is incorrect because single-node clusters are not highly available. Option E is incorrect because VPC routing does not affect node failure.

Option D is correct because Glue crawlers can be configured to update the table schema when new columns are detected. Option A is wrong because partition indexing does not update schema. Option B is wrong because the crawler's output is a table, not a database.

Option C is wrong because adding partition indexes does not update schema.

Option D is correct because many small files cause high overhead for listing and reading files in Athena. Compacting small files into fewer larger files (e.g., 128 MB each) reduces the I/O operations and improves performance. Option A is wrong because file format conversion (e.g., to Parquet) helps but does not address the small file problem.

Option B is wrong because increasing partitions would increase overhead. Option C is wrong because S3 Select is for retrieving subset of data from a single file, not for optimizing many files.

Option C is correct because LF-TBAC allows column-level permissions based on tags. Option A (IAM policy) does not support column-level restrictions. Option B (S3 bucket policy) is too coarse.

Option D (database-level grant) grants access to all columns.

Option D is correct because EVEN distribution distributes rows evenly across nodes, reducing data movement for queries that do not benefit from key distribution. Option A is wrong because ALL distribution duplicates the entire table on each node, which is inefficient for large tables. Option B is wrong because AUTO lets Redshift decide, but it may not choose the best.

Option C is wrong because sorting by other columns does not reduce data movement.

Option A is correct because AWS Step Functions can orchestrate EMR steps and integrate with Amazon SNS for notifications. Option B is wrong because Amazon CloudWatch Events can trigger Lambda but not directly orchestrate EMR steps. Option C is wrong because Amazon SQS is a message queue.

Option D is wrong because Amazon EventBridge can trigger but not orchestrate complex workflows.

Option A is correct because increasing the volume size of EBS attached to core nodes provides more storage. Option B is wrong because changing instance type affects memory/CPU, not disk. Option C is wrong because Spot Instances are cheaper but do not add disk.

Option D is wrong because reducing data retention is not always feasible and may lose data.

Option C is correct because even with EMRFS consistent view enabled, there can be a propagation delay between the crawler updating the catalog and the actual S3 objects being visible. The Spark job may be reading stale metadata. Option A is wrong because the crawler updates the catalog, but the job may read from S3 directly.

Option B is wrong because the file exists. Option D is wrong because the catalog is updated.

Option B is correct because the error indicates a missing column. This typically happens when the source data schema changes (e.g., column renamed or dropped) but the Data Catalog schema is not updated. Option A is incorrect because an incompatible IAM role would cause a different error (e.g., AccessDenied).

Option C is incorrect because a corrupted file would cause a read error, not a schema resolution error. Option D is incorrect because the 'cannot resolve' error is about column names, not data types.

Option D is correct because grouping small files into fewer, larger splits reduces the number of Spark partitions and memory overhead. Option A is wrong because increasing the number of workers increases parallelism but may not fix heap space per worker. Option B is wrong because using a different instance type with more memory per worker could help, but grouping files is more effective.

Option C is wrong because changing worker type to G.1X increases memory but may not solve the root cause of too many small files.

Option A is correct because the MySQL JDBC driver must be included in the Glue job's dependent JARs or as a Python module. Option B is incorrect because the driver class name is correct; the driver JAR is missing. Option C is incorrect because the error is about driver, not connection.

Option D is incorrect because subnet routing does not affect driver loading.

Option B is correct because increasing DPUs adds parallelism and memory, speeding up processing. Option A is incorrect because Parquet is already efficient; CSV would be slower. Option C is incorrect because reducing partitions may cause OOM.

Option D is incorrect because consolidating files into fewer, larger files can reduce parallelism.

Option C is correct because Step Functions has a default limit on concurrent executions (e.g., 1 million per account per region). Option A is wrong because Lambda concurrency limits would produce a different error. Option B is wrong because API Gateway is not involved.

Option D is wrong because state machine definition does not affect execution limits.

Option C is correct because Firehose uses a trust policy with a service principal but the delivery role must have S3 permissions. Option A is wrong because SSE-KMS requires KMS permissions, not S3 access. Option B is wrong because bucket name is part of the configuration.

Option D is wrong because VPC endpoints affect connectivity, not access denied after connection.

Option A is correct because the query filters for 'ERROR' and if logs don't contain that string, no results are returned. Option B is wrong because the query has a limit of 20. Option C is wrong because the query is syntactically correct.

Option D is wrong because the query does not use a time filter, so it scans all available logs, but if no error logs exist, results are empty.

Options B, C, and D are correct. Distribution style KEY on join keys collocates data, sort keys on WHERE columns improve scan efficiency, and compression reduces I/O. Option A is wrong because ALL distribution stores copy on every node, increasing storage and load time.

Option E is wrong because default distribution is AUTO, which may not be optimal.

Option D is correct because increasing DPUs allows more parallelism if the job is distributed. Option A is wrong because reducing the number of partitions may increase data skew. Option B is wrong because using a smaller instance type reduces resources.

Option C is wrong because converting to CSV increases file size and processing time.

Option C is correct because DataBrew uses a separate S3 bucket for storing intermediate outputs and recipe results. The IAM role needs s3:PutObject permission on that bucket. The error typically manifests as access denied when DataBrew tries to write.

Option A is wrong because the role has kms:Decrypt permission. Option B is wrong because DataBrew does not require VPC endpoints by default. Option D is wrong because the role includes s3:ListBucket.

Option A is correct because data validation at ingestion catches issues early. Option C is correct because schema enforcement prevents data type mismatches. Option E is correct because monitoring with CloudWatch allows proactive detection of failures.

Option B is wrong because manual sampling is not scalable. Option D is wrong because compressing data does not ensure quality.

Option D is correct because EMRFS consistent view is important for eventually consistent S3; lack of consistency can cause retries. Option A is wrong because reducing node count reduces parallelism. Option B is wrong because fewer instances reduce compute capacity.

Option C is wrong because HDFS is slower for S3 access.

Option C is correct because elastic resize allows adding nodes without downtime, addressing CPU bottleneck. Option A is incorrect because concurrency scaling only helps with many concurrent queries, not CPU. Option B is incorrect because distributing data on a different key may not reduce CPU.

Option D is incorrect because spectrum offloads to S3 but does not reduce cluster CPU usage.

Option B is correct because VACUUM and ANALYZE reclaim space and update statistics, which can significantly improve query performance. Option A is wrong because distribution key changes require table recreation. Option C is wrong because WLM queues affect concurrency, not disk usage.

Option D is wrong because compression encoding is set at table creation.

Option A is correct because increasing the number of core nodes adds more capacity for processing. Option D is correct because enabling task nodes with spot instances can offload work from core nodes. Option B is incorrect because instance store is temporary and not suitable for HDFS.

Option C is incorrect because consolidating tasks may not help. Option E is incorrect because reducing core nodes would worsen utilization.

Option C is correct because if the S3 bucket policy denies the crawler's IAM role, the crawler cannot list objects and discover partitions. Option A is incorrect because the crawler can handle many partitions. Option B is incorrect because the schedule is hourly, which should be sufficient.

Option D is incorrect because the crawler can discover partitions in S3 without a Hive metastore connection.

Option A is correct because increasing the number of reducers distributes the memory load, and m5.xlarge instances are cost-effective. Option B is wrong because r5 instances are memory-optimized but more expensive. Option C is wrong because increasing instance count may not help if reducer memory is the issue.

Option D is wrong because reducing input size may affect completeness.

Option C is correct because the error states the bucket policy does not allow the firehose to put objects. The solution is to add an Allow statement in the bucket policy granting the firehose's IAM role permission to execute s3:PutObject. Option A is incorrect because the error is about bucket policy, not ACLs.

Option B is incorrect because the error is already about permissions. Option D is incorrect because the issue is at the S3 bucket policy level, not IAM role trust policy.

Option B is correct because S3 Event Notifications with a Lambda function can process only after a final PUT, and writing to a temporary prefix then renaming ensures atomicity. Option A is wrong because S3 Copy can't detect completeness. Option C is wrong because S3 Select still reads incomplete files.

Option D is wrong because S3 Lifecycle policies manage lifecycle, not completeness.

Option B is correct because adding a DynamoDB Accelerator (DAX) cluster provides an in-memory cache, reducing read latency significantly. Option A is wrong because on-demand capacity already scales automatically. Option C is wrong because increasing read capacity units is not applicable in on-demand mode.

Option D is wrong because Global Tables do not reduce read latency for local reads.

Option C is correct because the 'Username' field shows the ARN of the IAM user, which is the identity that made the API call. Option A (access key ID) is not shown in the output. Option B (IP address) is not in the output.

Option D (MFA) is not recorded in this field.

Options B and C are correct. Setting concurrency scaling offloads queries, and using sort keys reduces the amount of data scanned. Option A is wrong because vacuum does not reduce CPU significantly.

Option D is wrong because increasing node count increases CPU capacity but not reduce usage. Option E is wrong because enabling audit logging adds CPU overhead.

Option B is correct because the error indicates that the object is not a valid Parquet file. Since the job expects Parquet, it likely encountered a CSV file. Option A is wrong because the error specifically says invalid Parquet, not missing file.

Option C is wrong because insufficient memory would cause OOM errors. Option D is wrong because the error is about the object's format, not permissions.