CCNA Data Operations and Support Questions

Increasing the number of parallel tasks in a DMS task improves throughput, allowing data to be loaded faster to the target and reducing replication lag. Option A is incorrect because the S3 bucket size does not affect DMS replication performance. Option C is incorrect: while Batch Optimized Apply can reduce apply overhead on certain targets like PostgreSQL, increasing parallel tasks is a more direct and effective way to address lag.

Option D is incorrect because disabling validation reduces data integrity checks and may provide only minor lag reduction, but it is not the recommended primary action.

For high availability in Amazon Redshift, you need to protect against data loss and downtime. Option B (configure cross-region snapshot copy) ensures that snapshots are replicated to another region, providing disaster recovery and high availability in case of a regional outage. Option E (deploy a multi-node cluster with at least two compute nodes) provides node-level redundancy; if one node fails, the workload can be redistributed.

Option C (enable automatic replication across Availability Zones) is not a feature of Amazon Redshift; Redshift does not support automatic cross-AZ replication for compute nodes. Option A (concurrency scaling) improves query concurrency, not availability. Option D (single-node cluster) offers no redundancy.

Creating a global secondary index (GSI) on the attributes used in the reporting queries allows the reporting application to use Query operations instead of expensive Scan operations. This reduces the read capacity consumption and avoids partition-level throttling, which is the cause of the 'ProvisionedThroughputExceededException' errors. On-demand tables have per-partition throughput limits, and frequent Scans can exceed those limits.

Option A is incorrect because increasing the maximum read capacity via AWS Support is not applicable to on-demand tables; on-demand scaling is automatic but per-partition limits still apply. Option B is incorrect because switching to provisioned capacity would require careful capacity planning and would likely increase costs compared to using a GSI. Option C is incorrect because DAX can cache data and reduce read load, but it does not eliminate the inefficiency of Scan operations; the root cause is the Scan itself, which can be avoided by using a GSI to enable efficient queries.

The error 'Path does not exist' occurs when Athena has already listed the files in the table's location and then tries to read a specific file that was deleted after the listing. This is a race condition caused by concurrent operations. Option A is incorrect because an incomplete multipart upload would not cause a path-not-found error; the file would simply not be visible or be incomplete.

Option B is incorrect because outdated Glue metadata would cause schema mismatches or table not found errors, not a missing file path. Option C is incorrect because a bucket policy denying access would result in an Access Denied error, not 'Path does not exist'.

Using DISTSTYLE KEY on the join column for large tables colocates data with the same key on the same slice, reducing the need for data redistribution operations like DS_DIST_ALL_INNER and DS_BCAST_INNER. Option C is correct because increasing the number of slices by resizing the cluster distributes data across more compute nodes, allowing more parallelism and reducing the relative impact of data redistribution. Option A is incorrect because setting DISTSTYLE to ALL on both tables would broadcast each table to all nodes, increasing data movement and likely worsening performance.

Option D is incorrect because SORTKEYs optimize range-restricted scans and sorting, not join data movement. Option E is incorrect because dropping and recreating tables with the same DDL does not change distribution or sort strategies, so it does not address the root cause of excessive redistribution.

Enabling DynamoDB auto scaling allows the table to adjust its provisioned throughput based on actual traffic patterns, which helps prevent 'ProvisionedThroughputExceededException' when the Lambda function writes to DynamoDB. Additionally, configuring a dead-letter queue (DLQ) for the Lambda function ensures that records that fail due to throttling are captured and can be reprocessed later, preventing data loss. Option B is incorrect because increasing the Lambda function timeout does not address DynamoDB throughput limits.

Option C is incorrect because increasing the number of Kinesis shards may increase the rate of Lambda invocations, potentially worsening the throttling issue. Option D is incorrect because increasing Lambda reserved concurrency could allow more concurrent invocations, which may exacerbate throughput exceedance, and disabling retries would cause data loss.

The CLI command output provides the last modified date of the object. To determine if the object is eligible for lifecycle transition to S3 Glacier based on its age, the engineer needs the current date to compute the object's age. The age is calculated as the difference between the current date and the last modified date.

Option A is correct because without the current date, the engineer cannot verify the age condition. Option B (ETag) is used for integrity checking and not for age calculations. Option C (metadata archive flag) is not relevant; the archive flag indicates storage class but not age.

Option D (ContentLength) is the object size and does not affect lifecycle age eligibility.

DynamoDB on-demand mode can throttle requests when traffic exceeds the table's previous peak by more than double. Implementing exponential backoff with jitter in the application code allows retries to spread out and succeed without overwhelming the table. Option A is incorrect because switching to provisioned capacity requires accurate capacity planning and may still throttle during unexpected spikes.

Option B is incorrect because DAX caches frequently read items, reducing read load, but it does not directly address throttling caused by exceeding the table's throughput limits; it also adds cost and complexity. Option C is incorrect because increasing read capacity units applies only to provisioned capacity mode, not on-demand.

(Enable DynamoDB Global Tables) is correct because Global Tables provide local read replicas in multiple AWS Regions, reducing read latency for users accessing from different geographic locations. Option C (Enable DynamoDB Accelerator (DAX)) is correct because DAX is an in-memory cache that significantly reduces read latency for frequently accessed items. Option A is incorrect because adding a Global Secondary Index (GSI) improves query flexibility but does not directly reduce read latency for primary key lookups.

Option D is incorrect because DynamoDB Streams capture changes to the table for event-driven processing, not for reducing read latency. Option E is incorrect because increasing write capacity units only affects write throughput, not read performance.

Lake Formation permissions are separate from IAM; even if the analyst has IAM permissions to run Athena, they also need specific Lake Formation permissions (e.g., SELECT) on the table to query it. The table appears in the catalog because the analyst has DESCRIBE permission on the database or table, but querying requires additional data access permissions. Option A is incorrect because the table appears in the catalog, so it is registered.

Option B is incorrect because the S3 bucket policy might allow access, but Lake Formation can override it. Option D is incorrect because the workgroup output location affects where query results are stored, not the ability to query a specific table.

Implementing exponential backoff and retries in the Glue job is a best practice to handle transient throttling errors from DynamoDB such as ProvisionedThroughputExceededException. This approach allows the job to automatically retry failed operations with increasing delays, reducing the likelihood of sustained failures without requiring changes to the DynamoDB table's provisioned capacity. Option A is incorrect because DynamoDB Accelerator (DAX) is an in-memory cache that can reduce read traffic for cached items, but it does not guarantee elimination of ProvisionedThroughputExceeded exceptions, especially for read operations that are not cached or when the underlying issue is read throughput limits.

Option B is incorrect because running the job less frequently does not address the intermittent throttling; the job may still encounter the same error when it runs. Option D is incorrect because, although increasing read capacity units could directly address read throttling, it is not the most cost-effective or best practice for dealing with occasional throttling. The recommended approach is to first implement exponential backoff and retries, only increasing provisioned capacity if throttling persists.

Moreover, the ProvisionedThroughputExceededException in this scenario is due to read throughput limits, and increasing write capacity would not help.

The correct answers are C and D. Option C enables S3 default encryption using SSE-KMS with a customer managed key, ensuring all objects are encrypted at rest with keys managed by the company. Option D creates a customer managed key in AWS KMS with automatic yearly rotation, which satisfies the requirement for key rotation; the company can also perform manual rotations every 90 days if needed.

Option A is incorrect because SSE-C requires the customer to manage the keys themselves, including storing them in Secrets Manager, and does not provide automatic rotation. Option B is incorrect because a bucket policy can enforce encryption but does not manage keys. Option E is incorrect because SSE-S3 uses Amazon-managed keys, not customer-managed keys.

The CLI command output shows the log group exists but `storedBytes: 0`, meaning no logs have been written. The most common cause is that the Lambda execution role lacks the required permissions (`logs:CreateLogStream`, `logs:PutLogEvents`). Option B is incorrect because even if the function logged to a different group, logs for this group would still be written if permissions allowed.

Option C is incorrect because the function could be invoked but unable to write logs. Option D is incorrect because a retention policy does not prevent logs from being written; it only determines how long they are kept.

AWS Glue job bookmarks track processed data and enable incremental processing by automatically storing state information about previously processed data, so only new and updated records are processed in subsequent runs. This approach is cost-effective because it avoids full table scans and reduces data processing. Option B is not optimal because AWS DMS with CDC adds extra cost and operational overhead for a simple hourly incremental load, and it is not directly integrated with Glue.

Option C can work but is less reliable if timestamps are not updated on changes or if there are late-arriving records, and it may require additional indexing. Option D is inefficient because full table scans each hour are costly and slow, especially for large tables.

Enabling 'groupFiles' in the S3 sink coalesces small files into larger ones, reducing the number of write operations and improving write performance. Option C is correct because increasing MaxCapacity (DPU) allocates more processing units, increasing parallelism and processing speed. Option E is correct because increasing 'dynamodb.throughput.read.percentage' allocates a higher percentage of the table's provisioned read capacity to the Glue job, allowing more parallel reads from DynamoDB.

Option B is incorrect because decreasing 'dynamodb.splits' reduces the number of parallel readers, which can lower parallelism and slow down the job. Option D is incorrect because disabling job bookmarks results in reprocessing all data each run, which increases processing time and does not improve performance.

Increasing the number of shards in a Kinesis data stream increases the level of parallelism for both ingestion and consumption, allowing the consumer to process more data concurrently and catch up. Option A is incorrect because Kinesis Data Firehose is a delivery stream, not a replacement for real-time consumption, and cannot solve a consumer lag issue. Option B is incorrect because decreasing batch size reduces the amount of data processed per poll, which can actually slow down throughput and increase iterator age.

Option C is incorrect because enhanced fan-out is useful when multiple consumers need dedicated throughput, but it does not increase the total throughput of the stream; it simply provides each consumer with its own 2 MB/sec per shard read throughput, which may not help a single consumer that is already falling behind.

The 'ProvisionedThroughputExceededException' occurs when the Lambda function exceeds the DynamoDB table's write capacity. To resolve this without data loss, the data engineer should both increase the DynamoDB write capacity to accommodate the throughput and configure a dead-letter queue (DLQ) on the Lambda function. The DLQ captures records that fail after all retries, preventing data loss.

Option A (reducing shards) would lower the ingestion rate but may cause data loss and does not address the root cause. Option B (increasing read capacity) is irrelevant because the issue is with writes. Option D (disabling retries) would cause data loss because failed records would not be retried.

Concatenating small files into larger files reduces the overhead of file listing and task scheduling, improving query performance. Option D is correct because partitioning the data by a frequently filtered column allows Athena to use partition pruning, scanning only relevant partitions and reducing the amount of data read. Option A is incorrect because increasing the number of small files increases overhead and worsens performance.

Option B is incorrect because disabling S3 server-side encryption does not affect query performance. Option E is incorrect because converting from CSV to JSON does not improve query performance; columnar formats like Parquet or ORC would be beneficial.

Options A and E are considered correct for ensuring high availability in Amazon Redshift. Option A: Automated snapshots with cross-region copy enable recovery from a node failure by restoring data from a snapshot in another region, which reduces downtime but does not guarantee zero downtime. Option E: A multi-node cluster with RA3 node types separates compute and storage, allowing faster node replacement and minimizing downtime, though it does not eliminate it entirely.

The exam expects these as the best actions among the given choices to improve availability and withstand a single node failure.

Configuring the crawler's 'Schema updates' option to 'Update the table schema' allows Glue crawlers to automatically detect and update the schema when new columns are added to the data. Option A is wrong because a partition index is used to improve query performance on partitioned data, not to update the schema. Option C is wrong because setting the crawler's database output to a new database does not affect schema updates; it simply directs the crawler to write to a different database.

Option D is wrong because partition indexing (enable partition indexing) is about indexing partitions for faster querying, not about schema updates.

Many small files (under 1 MB) cause high overhead because each file requires a separate read operation and metadata call. Consolidating them into fewer larger files (e.g., 128 MB each) reduces the number of read operations and improves I/O efficiency, directly addressing the root cause of slowdowns and timeouts. Option A (converting to Parquet) improves storage efficiency and query performance but does not reduce the file count; it is a beneficial addition but not the most effective standalone fix for the small file problem.

Option C (adding more partitions) would increase overhead by creating even more directories/files to scan. Option D (S3 Select) applies within individual files and does not mitigate overhead from file quantity.

Changing to EVEN distribution distributes rows evenly across all nodes, reducing data movement during queries that do not benefit from the current KEY distribution on customer_id. This is especially useful for large, frequently updated tables where data skew or redistribution can cause performance issues. Option A (ALL) is wrong because it duplicates the entire table on every node, which is inefficient for large tables.

Option B (AUTO) is wrong because it may not choose the optimal distribution style for this workload. Option D (adding a sort key) improves sort performance but does not directly reduce data movement.

WS Step Functions (Option B). Step Functions is a serverless workflow service that can orchestrate multiple AWS services, including EMR steps, into a state machine. It can be triggered on a schedule using Amazon EventBridge or CloudWatch Events, and it can integrate with Amazon SNS to send email notifications on failure.

Option A (Amazon EventBridge) is an event bus that can trigger services but does not provide built-in orchestration for complex workflows. Option C (SQS) is a message queue service and not an orchestrator. Option D (Amazon CloudWatch Events) is similar to EventBridge and can trigger Lambda functions but lacks native workflow orchestration capabilities.

DISTSTYLE ALL replicates the entire table to every node, but within each node, the data is stored on a single slice. For dc2.large nodes, which have 2 slices per node, this means that a full table scan will only use one slice per node, not all available slices. This leads to underutilization and slow query performance.

Option A is incorrect because the leader node does not execute data queries; it only coordinates them. Queries are executed on compute nodes. Option C is incorrect because WLM queue slots control concurrency, not the number of slices used by a single query.

Option D is incorrect because sort keys affect data ordering and compression, not slice distribution.

The error 'cannot resolve column_name' indicates that the Spark SQL query is trying to reference a column that does not exist in the schema provided by the AWS Glue Data Catalog. This typically happens when the source data schema has changed (e.g., column renamed or dropped) but the Data Catalog schema is not updated accordingly. Option B is incorrect because a corrupted file would cause a read or parse error, not a schema resolution error.

Option C is incorrect because an IAM permissions issue would result in an AccessDenied error. Option D is incorrect because a data type mismatch would cause a type casting error, not a 'cannot resolve' error which is about column names.

Correct answers are A, D, and E. DISTSTYLE KEY on frequently joined tables collocates data on the same node, reducing data movement during joins. Applying compression encodings reduces storage and I/O, improving performance.

SORT KEYs on columns used in WHERE clauses enable efficient range-restricted scans. Option B is incorrect because the default distribution style (AUTO) may not be optimal; explicit distribution is better for performance. Option C is incorrect because DISTSTYLE ALL stores a copy of the table on every node, which increases storage and load time; it is suitable only for small dimension tables, not large fact tables.

Increasing the number of DPUs from 10 to 20 allows the job to process data in parallel, reducing execution time. AWS Glue standard mode scales linearly with DPUs for ETL jobs that are not I/O bound. In this case, with 500 GB of data and 10 DPUs, the job is likely CPU-bound and can benefit from additional parallelism.

Option B is incorrect because using a smaller worker type (G.1X) reduces available memory and CPU, worsening performance. Option C is incorrect because changing the output from Parquet (columnar, compressed) to CSV (row-based, uncompressed) increases data size and I/O, slowing the job. Option D is incorrect because reducing partitions can cause data skew and reduce parallelism, increasing runtime.

The IAM role has permissions to read from the source S3 bucket (s3:GetObject, s3:ListBucket) and to decrypt using the KMS key (kms:Decrypt). However, DataBrew needs to write intermediate results to a separate output S3 bucket. The error indicates that the job cannot access data, which in the context of DataBrew often means it cannot write to the output bucket.

Therefore, the role is missing s3:PutObject permission on the output bucket. Option B is incorrect because the role already has s3:ListBucket on the source bucket. Option C is incorrect because the S3 bucket being in a different region would not cause an access denied error if the role has proper cross-region permissions; DataBrew can access buckets in any region.

Option D is incorrect because kms:Decrypt is sufficient for reading encrypted objects; kms:GenerateDataKey is needed for writing.

Implementing data validation checks at the ingestion stage ensures that only valid data enters the pipeline, catching issues early. Option B is correct because AWS Glue DataBrew provides data profiling and schema enforcement capabilities that help maintain data quality by identifying anomalies and enforcing schemas. Option E is correct because setting up Amazon CloudWatch alarms allows proactive monitoring of pipeline failures and data anomalies, enabling timely responses.

Option C is incorrect because compressing data files reduces storage costs but does not directly address data quality. Option D is incorrect because manual sampling is not scalable and cannot consistently ensure data quality in large-scale pipelines.

VACUUM and ANALYZE are the first actions to take because they reclaim disk space from deleted rows and update table statistics, which can significantly improve query performance and reduce disk usage. Option B is incorrect because enabling compression on all columns is not a first step; compression is typically set during table creation and may not address current disk usage issues. Option C is incorrect: redistributing tables by changing the distribution key requires recreating the table, which is an invasive operation and not the first troubleshooting step.

Option D is incorrect: modifying the WLM queue affects concurrency management, not disk usage or query performance directly related to high disk usage.

The IAM role used by the crawler must have permissions to list the S3 bucket and read its objects. Without s3:ListBucket permission, the crawler cannot discover new partitions in the bucket, even if it runs on schedule. Option B is incorrect because the crawler does not need a Hive metastore connection to discover partitions in S3; it can update the Glue Data Catalog directly.

Option C is incorrect because the partition limit is 1,000,000 (not 100,000) per table, and the scenario does not indicate that limit is reached. Option D is incorrect because running the crawler every hour is a reasonable frequency; if permissions are correct, it should discover new partitions at that interval.

Options A and C are correct. Enabling concurrency scaling offloads read queries to additional clusters, reducing CPU load on the main cluster. Optimizing table design with sort keys and compression reduces the amount of data scanned per query, lowering CPU usage.

Option B (increasing node count) increases overall CPU capacity but does not reduce usage; it may even increase cost without addressing inefficiency. Option D (running VACUUM) primarily reclaims disk space and maintains data distribution, not CPU reduction. Option E (enabling audit logging) adds CPU overhead, making it counterproductive.

The error indicates that the job encountered an object that is not a valid Parquet file. Since the S3 location contains both .parquet and .csv files, the Glue job likely attempted to read a CSV file as if it were Parquet, causing the invalid Parquet error. Option C is correct because the CSV file was incorrectly placed in the directory with a .parquet extension (or the job's schema inference expects all files to be Parquet).

Option A is incorrect because the error is about format, not a missing file. Option B is incorrect because the error is about the object's format, not a permissions issue. Option D is incorrect because insufficient memory would typically cause out-of-memory or capacity errors, not an invalid Parquet error.

The 'Communications link failure' error during connection indicates a network connectivity issue between the AWS Glue job and the RDS instance. Even if the Glue job uses a VPC endpoint for S3, that endpoint does not provide connectivity to RDS. To connect to an RDS instance in a private subnet, the Glue job must be attached to the same VPC (e.g., via an elastic network interface) or have a network path through VPC peering or VPN.

Option A (max connections) would yield a 'too many connections' error, not a communications link failure. Option B (KMS permissions) would cause an access denied error, not a connection failure. Option C (JDBC driver incompatibility) would typically produce a 'No suitable driver' or driver class not found error.

Therefore, the most likely cause is that the Glue job lacks a network path to the RDS instance.

The ClassNotFoundException for Hive classes indicates that the required Hive JARs are not available in the Glue job's classpath. The solution is to include the Hive JAR files in the 'Python library path' or use a Glue version that supports Hive connectivity. Option A is incorrect because a network connectivity issue would cause a different error, such as a timeout or connection refused.

Option C is incorrect because simply adding an import statement in the Python script does not provide the underlying JAR files; the JARs must be included via library path. Option D is incorrect because IAM permissions do not affect class loading; the error is related to missing dependencies, not authorization.

The 'S3ServiceException: SlowDown' error indicates that the AWS Glue job is making requests to Amazon S3 at a rate that exceeds the bucket's request rate limits. Implementing exponential backoff and retry logic (option C) is the most effective solution because it reduces the effective request rate by introducing delays between retries, allowing S3 to recover from throttling. Option A is incorrect because increasing partitions would likely increase the number of concurrent requests, exacerbating throttling.

Option B is incorrect because switching to a larger worker type does not affect the rate of S3 requests. Option D is incorrect because S3 Transfer Acceleration improves network transfer speed but does not reduce request throttling.