DEA-C01 Data Operations and Support Practice Test 30 — 15 Questions

Question 1

A company is using AWS Glue to catalog data stored in Amazon S3. The data is partitioned by year, month, and day. A data analyst reports that new partitions are not automatically discovered by the Glue crawler. The crawler runs on a schedule every hour. What is the MOST likely reason for the missing partitions?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

The IAM role used by the crawler does not have permission to list the S3 bucket.. For a Glue crawler to discover new partitions in S3, its IAM role must have s3:ListBucket (and GetObject) permissions on the bucket and prefix. If the role lacks ListBucket, the crawler cannot enumerate the partition folders and will silently miss new partitions even though it runs on schedule. This is the most common cause of 'crawler runs but doesn't find new data' issues.

Answer

The Glue Data Catalog is not configured to use a Hive metastore.

Answer

The number of partitions exceeds the Glue catalog limit of 100,000.

Answer

The crawler schedule is set to run too frequently.

Question 2

A company uses Amazon S3 to store log files from multiple applications. The logs are written in JSON format. A data engineer wants to use Amazon Athena to query these logs. The logs are stored in a bucket with the following structure: 's3://logs/app1/date=2021-01-01/'. The engineer creates an Athena table with partitions. However, when querying, Athena returns zero results for partitions that exist. The engineer has run MSCK REPAIR TABLE to add partitions. What is the most likely cause of the issue?

Accepted Answer

The partition key name in the table definition does not match the S3 folder naming convention.. The most likely cause is that the partition key name in the Athena table definition does not match the S3 folder naming convention. When using MSCK REPAIR TABLE, Athena relies on the partition folder structure (e.g., 'date=2021-01-01') to automatically add partitions. If the table's partition key is named differently (e.g., 'dt' instead of 'date'), MSCK REPAIR will not recognize the folders and will not register the partitions, resulting in zero results. Option A is incorrect because MSCK REPAIR does not fail silently; it either adds partitions or reports none if the structure doesn't match. Option C is incorrect because Athena fully supports JSON format. Option D is incorrect because the bucket location does not affect partition registration; data can be queried in any bucket as long as the table points to it.

Answer

The MSCK REPAIR TABLE command failed silently.

Answer

The log files are in JSON format and Athena does not support JSON.

Answer

The log files need to be copied to a different bucket in the same region.

Question 3

A company uses Amazon Kinesis Data Firehose to deliver streaming data to an Amazon S3 bucket. The data is then processed by a scheduled AWS Glue ETL job that loads it into an Amazon Redshift table. Recently, the Glue job has been failing with the error: 'S3ServiceException: Access Denied'. The Firehose delivery stream is configured with a prefix and error logging to the same S3 bucket. The Glue job uses the same IAM role that has s3:GetObject and s3:ListBucket permissions on the bucket. What is the most likely cause?

Accepted Answer

The S3 bucket has default encryption enabled with AWS KMS (SSE-KMS), and the Glue job's IAM role lacks kms:Decrypt permission.. The Glue job's IAM role has s3:GetObject and s3:ListBucket, which are sufficient for reading objects from S3 in the absence of encryption. However, when the bucket uses SSE-KMS default encryption, reading an object requires the kms:Decrypt permission on the KMS key in addition to s3:GetObject. The 'Access Denied' error is the classic symptom of a missing kms:Decrypt grant, making option D the most likely cause.

Answer

The Glue job expects a different data format than what Firehose writes.

Answer

The Glue job's IAM role does not have s3:GetObjectVersion permission.

Answer

The Glue job is using the wrong IAM role that does not have permissions to the S3 bucket.

Question 4

A data engineer is setting up a data pipeline using Amazon Kinesis Data Firehose to deliver data to Amazon S3. The data must be transformed using an AWS Lambda function before delivery. Which THREE steps are required to configure this?

Accepted Answer

Create an AWS Lambda function that transforms the data.. Option B is correct because Firehose data transformation requires an AWS Lambda function (a standard regional Lambda function, not Lambda@Edge) that receives batches of records and returns transformed records in the expected Firehose format. Option C is correct because the Firehose delivery stream assumes an IAM role, and that role must include lambda:InvokeFunction permission on the transformation function so Firehose can call it. Option E is correct because you must enable and select the Lambda function in the Firehose delivery stream's processing configuration (the Lambda transformation processor) so records are transformed before being written to Amazon S3. Option A is wrong because Lambda@Edge runs at CloudFront edge locations for viewer/origin request and response events, not for Firehose record transformation. Option D is wrong because S3 event notifications trigger actions after objects land in S3; Firehose invokes the transformation Lambda itself, so no S3 event notification is needed.

Answer

Create a Lambda@Edge function in the same Region.

Answer

Configure an S3 event notification to trigger the Lambda function when new data arrives.

Question 5

A data engineer is troubleshooting an AWS Glue ETL job that fails with a 'java.lang.OutOfMemoryError: Java heap space' error. The job processes a 50 GB Parquet file from an S3 bucket. The job uses a G.1X DPU (16 GB memory) and default parameters. Which action should the engineer take to resolve the issue?

Accepted Answer

Change the worker type to G.2X (32 GB memory).. Changing the worker type to G.2X (32 GB memory) doubles the memory per worker, directly addressing the Java heap space error. Option B is incorrect because increasing the number of workers does not increase memory per worker; each G.1X DPU still has only 16 GB. Option C is incorrect because increasing the batch size would increase the amount of data loaded into memory per worker, potentially worsening the memory issue. Option D is incorrect because converting from Parquet to JSON typically increases file size and memory usage due to lack of compression and columnar storage.

Answer

Increase the number of workers from 2 to 4.

Answer

Increase the 'batch size' parameter in the DynamicFrame reader.

Answer

Convert the input data from Parquet to JSON format.

Question 6

A data engineer is running an Amazon EMR cluster with Spark to process log files. The cluster uses instance fleets with m5.xlarge core nodes. The engineer observes that the Spark job is running slower than expected. CloudWatch metrics show that the cluster's CPU utilization is below 20% but memory utilization is near 90%. Which configuration change would most likely improve performance?

Accepted Answer

Use memory-optimized instances (r5.xlarge) for core nodes.. The CloudWatch metrics show CPU below 20% while memory is near 90%, which is the classic signature of a memory-bound Spark workload. Spark executors on m5.xlarge (16 GiB RAM) are spilling to disk or GC-thrashing because the working set exceeds available heap. Switching core nodes to r5.xlarge (memory-optimized, 32 GiB RAM) doubles the memory per node, reducing spills and GC pressure, which directly addresses the bottleneck.

Answer

Increase the number of core nodes from 5 to 10.

Answer

Increase the number of Spark shuffle partitions.

Answer

Decrease the number of core nodes to reduce overhead.

Question 7

A data engineer is troubleshooting an AWS Glue job that writes data to an Amazon S3 bucket in Parquet format. The job runs successfully but the output files are smaller than the configured 'groupFiles' size. The engineer has set 'groupFiles' to 'inPartition' and 'groupSize' to 1 GB. The input data is 10 GB in a single partition. What is the most likely reason for the small files?

Accepted Answer

The 'groupFiles' parameter only affects the input read phase, not the output write phase.. 'groupFiles' only works when the input data is already small and needs to be coalesced. However, if the input is large and the job writes output, the output file size is determined by the number of Spark partitions, not grouping. The grouping feature only applies to reading input files. Option A is wrong because the setting is correct. Option C is wrong because grouping is a read-time feature, not write-time. Option D is wrong because grouping does not require repartitioning.

Answer

The 'groupFiles' parameter is deprecated in the current Glue version.

Answer

The 'groupFiles' parameter is misspelled or set incorrectly.

Answer

The engineer must also set 'repartition' to 1 to merge output files.

Question 8

A data engineer is monitoring an Amazon Kinesis Data Stream used to ingest clickstream data. The engineer notices that the stream's 'WriteProvisionedThroughputExceeded' metric is frequently above zero. Which TWO actions could help mitigate this issue? (Choose TWO.)

Accepted Answer

Increase the number of shards in the stream.. Option A is correct because WriteProvisionedThroughputExceeded indicates that producers are exceeding the stream's ingest capacity, and each shard supports a fixed write throughput of 1 MB/s or 1,000 records/s, so adding shards (resizing/shard splitting) increases total write capacity. Option D is correct because a hot shard caused by an uneven partition key (for example, a constant or low-cardinality key) can throttle writes even when aggregate capacity is sufficient; adding a random prefix to the partition key spreads records across more shards, balancing the load. Option B is incorrect because the retention period only controls how long data is stored and has no effect on write throughput capacity. Option C is incorrect because decreasing shards reduces total write capacity and would worsen throttling. Option E is incorrect because enhanced fan-out increases read throughput for consumers (dedicated 2 MB/s per consumer per shard) and does not address write-side throttling.

Answer

Reduce the data retention period to free up capacity.

Answer

Decrease the number of shards to reduce overhead.

Answer

Enable enhanced fan-out on the stream.

Question 9

A data engineer is troubleshooting an AWS Glue ETL job that fails with an 'Access Denied' error when trying to write to an S3 bucket. The IAM role used by the job has the policy shown in the exhibit. The bucket 'my-bucket' uses S3 default encryption with AWS KMS. What is the most likely missing permission?

Accepted Answer

kms:GenerateDataKey and kms:Decrypt. When an S3 bucket uses AWS KMS for default encryption, any write operation requires the IAM role to have kms:GenerateDataKey and kms:Decrypt permissions on the KMS key. The policy in the exhibit grants s3:PutObject but does not include any KMS actions, resulting in an 'Access Denied' error. Option A (s3:GetObjectVersion) is not needed for writing. Option B (glue:GetObject) is not a valid AWS action. Option C (s3:ListBucketMultipartUploads) is not required for a write operation. Option D (s3:PutObjectAcl) is unnecessary unless the job explicitly sets ACLs.

Answer

s3:GetObjectVersion

Answer

glue:GetObject

Answer

s3:ListBucketMultipartUploads

Answer

s3:PutObjectAcl

Question 10

A company uses AWS Glue to run ETL jobs that process data from an Amazon RDS for MySQL database and load it into an Amazon S3 data lake. The Glue job runs daily and processes incremental data. Recently, the job has been taking longer than expected. The engineer checks the CloudWatch logs and sees that the job is spending most of its time on the 'Reading from JDBC' phase. The MySQL table has 10 million rows and is indexed on the primary key. The Glue job uses a 'job bookmark' to track processed data. The engineer wants to improve the performance of the read phase. Which action is most likely to help?

Accepted Answer

Modify the job to use a 'query' parameter that selects only the new or modified rows based on a timestamp column.. Using a 'query' parameter with a timestamp filter allows the Glue job to push down the filtering to the JDBC source, so only new or modified rows are read from MySQL. This drastically reduces the volume of data transferred and the time spent in the 'Reading from JDBC' phase, especially when combined with an index on the timestamp column.

Answer

Increase the JDBC 'fetchSize' parameter to 10000.

Answer

Disable job bookmark and perform a full refresh each time.

Answer

Increase the number of DPUs for the Glue job.

Question 11

A data engineering team is troubleshooting a failing AWS Glue ETL job that processes data from an S3 bucket. The job writes output to another S3 bucket. The job fails with an AccessDenied error when writing to the output bucket. The IAM role used by the job has the following policy attached: {"Version":"2012-10-17","Statement":[{"Effect":"Allow","Action":["s3:GetObject","s3:ListBucket"],"Resource":["arn:aws:s3:::input-bucket/*","arn:aws:s3:::input-bucket"]}]}. What is the most likely cause of the failure?

Accepted Answer

The IAM role does not have s3:PutObject permission on the output bucket.. The IAM policy attached to the Glue job role only grants s3:GetObject and s3:ListBucket on the input bucket. There is no s3:PutObject (or any write) permission on the output bucket, so when Glue attempts to write results, S3 returns AccessDenied. The most likely cause is therefore the missing write permission on the output bucket.

Answer

The ETL job is processing more than 10 TB of data.

Answer

The output bucket has a bucket policy that denies access to the IAM role.

Answer

The IAM role used by the job does not exist.

Question 12

A company uses Amazon S3 as a data lake. A data engineer needs to ensure that all objects uploaded to the 'incoming' prefix are automatically encrypted at rest using AWS KMS with a specific customer managed key. What is the simplest way to enforce this?

Accepted Answer

Use a bucket policy that denies PutObject requests without the required encryption header.. A bucket policy that denies s3:PutObject unless the request includes the required encryption header (e.g., x-amz-server-side-encryption: aws:kms and x-amz-server-side-encryption-aws-kms-key-id) enforces encryption at upload time with the specified customer managed key. This is the simplest enforcement mechanism because it operates at the bucket level and rejects non-compliant uploads before the object is written. It also works regardless of which client or SDK performs the upload.

Answer

Enable S3 Transfer Acceleration to force encryption in transit.

Answer

Configure S3 Inventory to report on encryption status and alert on non-compliance.

Answer

Enable default encryption on the bucket with SSE-S3.

Question 13

A data engineer needs to monitor the performance of an Amazon Redshift cluster. Which Amazon CloudWatch metric should the engineer monitor to detect disk space issues?

Accepted Answer

PercentageDiskSpace. PercentageDiskSpace is the correct metric because it directly reports the percentage of disk space used in the Redshift cluster. Monitoring this metric allows the engineer to detect when disk space is running low, which can lead to performance degradation or query failures. Unlike I/O or CPU metrics, it specifically measures storage utilization, making it the most relevant for disk space issues.

Answer

ReadIOPS

Answer

WriteIOPS

Answer

NetworkThroughput

Answer

CPUUtilization

Question 14

A company uses Amazon Kinesis Data Firehose to deliver streaming data to Amazon S3. The data is in JSON format, and each record is approximately 5 KB. The company has set the buffer interval to 60 seconds and the buffer size to 5 MB. However, the data engineer observes that the delivery to S3 is delayed by up to 5 minutes during peak traffic. The engineer wants to reduce the delivery latency to under 1 minute. Which TWO actions should the engineer take? (Choose TWO.)

Accepted Answer

Reduce the buffer size to 1 MB.. Option B is correct because Kinesis Data Firehose flushes data to S3 when either the buffer size OR the buffer interval is reached; lowering the buffer size from 5 MB to 1 MB makes the size threshold trigger much sooner, so smaller batches are delivered more frequently and latency drops. Option E is correct because reducing the buffer interval from 60 seconds to 10 seconds forces a flush at least every 10 seconds even if the buffer size is not reached, directly capping delivery latency well under 1 minute. Together, B and E ensure the delivery stream flushes on the shorter of the two thresholds, which is exactly what is needed to cut the observed 5-minute delays. Option A does not help because GZIP compression only reduces payload size and can actually add CPU overhead, not reduce flush latency. Option C is wrong because increasing the buffer size to 50 MB would delay flushes further, worsening latency. Option D is wrong because converting to Parquet changes the storage format and query efficiency, not the Firehose buffering/flush timing that governs delivery latency.

Answer

Enable GZIP compression for the delivery stream.

Answer

Increase the buffer size to 50 MB.

Answer

Convert the data format to Apache Parquet before delivery.

Question 15

A company is migrating its on-premises data warehouse to Amazon Redshift. The data includes tables with up to 100 columns and 500 million rows. The migration involves a full load followed by incremental updates. The company needs to minimize downtime during the final cutover. Which THREE strategies should the data engineer use to facilitate the migration? (Choose THREE.)

Accepted Answer

Use the COPY command to load data from Amazon S3.. Option B is correct because the COPY command is the most efficient, parallelized way to bulk-load large datasets from Amazon S3 into Redshift, and it supports loading from multiple files in parallel to speed up the full load and minimize cutover downtime. Option C is correct because storing the source files in a columnar format such as Parquet lets COPY read only needed columns, compresses data heavily, and reduces I/O and load time compared with row-based CSV or JSON. Option D is correct because after a large load, running VACUUM re-sorts rows and reclaims space (restoring sort-key performance) and ANALYZE refreshes table statistics so the query planner produces efficient plans for the subsequent incremental workload. Option A is not appropriate because adding WLM queues does not by itself accelerate a single large load and can even reduce per-query resources; queue design is about workload isolation, not migration throughput. Option E is not appropriate because disabling distribution keys removes the ability to co-locate joins and causes data redistribution at query time, hurting performance; distribution keys should be chosen deliberately, not disabled.

Answer

Increase the number of WLM queues to allow more concurrent loads.

Answer

Disable distribution keys on the target tables to simplify loading.