DEA-C01 Data Operations and Support Practice Test 19 — 15 Questions

Question 1

A data team runs a daily AWS Glue ETL job that processes data from an Amazon Redshift cluster and writes results to Amazon S3. The job completes successfully but takes 2 hours longer than expected. The job uses the JDBC connection to Redshift. The Redshift cluster is 4 dc2.large nodes. The Glue job has 10 workers of type G.1X. Which change would MOST likely reduce the job duration?

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

Accepted Answer

Use the S3 staging option in the Glue connection to unload data from Redshift to S3 first. The JDBC connection in AWS Glue reads data row-by-row from Redshift, which is slow for large datasets. By enabling the S3 staging option in the Glue connection, the job uses Redshift's UNLOAD command to export data to S3 in parallel, then Glue reads from S3. This bypasses the JDBC bottleneck and leverages Redshift's massively parallel processing (MPP) to export data much faster.

Answer

Use Redshift Spectrum to query data directly from S3

Answer

Increase the Redshift cluster size to 8 nodes

Answer

Increase the number of Glue workers to 20

Question 2

A company uses Amazon S3 to store raw data and runs AWS Glue ETL jobs to transform it into Parquet. The data is then queried using Amazon Athena. Queries are slow and expensive due to high scan volumes. Which THREE design changes can improve query performance and reduce costs? (Select THREE.)

Accepted Answer

Convert the data to a columnar format like Parquet or ORC if not already. Option B is correct because columnar formats such as Parquet or ORC let Athena read only the columns referenced in a query, dramatically reducing the bytes scanned compared to row-based formats like CSV or JSON. Option C is correct because splittable compression such as Snappy (or Zstandard) reduces storage and scan volume while still allowing Athena to split large files across parallel readers, unlike non-splittable gzip on a single object. Option E is correct because partitioning by frequently filtered columns such as date or region enables partition pruning, so Athena skips reading irrelevant S3 prefixes and scans far less data. Option A is not appropriate because shrinking files to 1 MB creates a huge number of small files, increasing S3 LIST and request overhead and hurting, not helping, Athena performance. Option D is not appropriate here because bucketing on high-cardinality columns does not reduce the data scanned by Athena and is generally used for join optimization in Hive/Spark, not for lowering Athena scan costs.

Answer

Increase the number of files by reducing file size to 1 MB

Answer

Use bucketing on high-cardinality columns

Question 3

A data engineer runs a Spark job on Amazon EMR that reads data from Amazon S3 and writes results back to S3. The job fails with an 'S3AccessDenied' error. The engineer verifies that the IAM role attached to the EMR cluster has s3:GetObject and s3:PutObject permissions on the relevant buckets. What is the MOST likely cause of the error?

Accepted Answer

The IAM role does not have s3:ListBucket permission on the bucket.. The IAM role attached to the EMR cluster must have the s3:ListBucket permission on the bucket to allow the Spark job to enumerate objects when reading from S3. Without this permission, even with s3:GetObject and s3:PutObject, the job fails with an 'S3AccessDenied' error because the S3 list operation is required for directory listing and file discovery.

Answer

S3 Transfer Acceleration is not enabled on the bucket.

Answer

EMRFS consistent view is not configured.

Answer

The S3 bucket is in a different AWS Region than the EMR cluster.

Question 4

A company uses AWS DMS to migrate data from an on-premises Oracle database to Amazon Redshift. The migration is successful, but after a few days, data in Redshift becomes inconsistent with the source due to ongoing changes. The company needs to keep Redshift synchronized with minimal latency. Which approach should the data engineer use?

Accepted Answer

Configure DMS with ongoing replication using change data capture (CDC).. AWS DMS supports ongoing replication using change data capture (CDC), which captures incremental changes from the Oracle source (via Oracle LogMiner or binary logs) and applies them to Amazon Redshift in near real-time. This approach ensures that Redshift remains synchronized with the source database with minimal latency, meeting the requirement for ongoing consistency after the initial full load.

Answer

Use Amazon Redshift COPY with S3 staging and AWS Lambda triggers.

Answer

Schedule a full DMS load every night.

Answer

Set up Amazon Redshift Spectrum to query the Oracle database directly.

Question 5

A data engineer notices that an Amazon Kinesis Data Firehose delivery stream is failing to deliver data to an Amazon S3 bucket. The CloudWatch metrics show 'DeliveryToS3.Success' is 0 and 'S3.BucketExists' is 1. What is the MOST likely cause?

Accepted Answer

The IAM role for Firehose lacks s3:PutObject permission.. The metric 'S3.BucketExists' is 1, confirming the S3 bucket exists, so the issue is not bucket existence. With 'DeliveryToS3.Success' at 0, the failure is in the write operation. The IAM role assumed by Firehose must have the s3:PutObject permission to deliver data; lacking it would cause all delivery attempts to fail silently, matching the observed metrics.

Answer

The S3 bucket has an ACL that denies access to Firehose.

Answer

The Firehose delivery stream Lambda transformation function is failing.

Answer

The S3 bucket does not exist.

Question 6

A data engineer is troubleshooting an AWS Glue ETL job that fails with the error: 'An error occurred while calling o123.pyWriteDynamicFrame. Access Denied when writing to S3 bucket: my-bucket'. The job uses a Glue service role named 'GlueServiceRole'. Which TWO actions should the engineer take to resolve the issue? (Choose TWO.)

Accepted Answer

Check if the S3 bucket policy denies access from the GlueServiceRole.. The error 'Access Denied when writing to S3 bucket' during pyWriteDynamicFrame indicates an S3 authorization failure for the Glue job's execution role, so option D is correct: the engineer must verify that the IAM policy attached to GlueServiceRole includes s3:PutObject (and typically s3:PutObjectAcl) on the target bucket/prefix, since Glue writes output objects to S3 using that role. Option C is also correct because an explicit Deny in the S3 bucket policy overrides any Allow in the IAM policy, so the engineer must check whether the bucket policy denies access from GlueServiceRole. Option A is wrong because disabling S3 Block Public Access is unrelated to a role-based write failure and would weaken security without fixing the permission issue. Option B is wrong because the failure is writing to S3, not to the Glue Data Catalog, so Data Catalog permissions would not resolve the S3 Access Denied error. Option E is wrong because S3 is accessed via AWS public service endpoints and does not require the Glue job to be in the same VPC as the bucket; VPC placement only matters for private endpoint configurations, not for this authorization error.

Answer

Disable S3 Block Public Access on the bucket.

Answer

Grant the GlueServiceRole permission to write to the AWS Glue Data Catalog.

Answer

Ensure the Glue job is in the same VPC as the S3 bucket.

Question 7

A data engineer is monitoring an Amazon Kinesis Data Analytics for Apache Flink application that processes streaming data. The application is falling behind (increasing 'MillisBehindLatest') and the CPU utilization of the Flink task managers is consistently above 80%. Which THREE actions should the engineer take to improve performance? (Choose THREE.)

Accepted Answer

Increase the number of shards in the Kinesis data stream.. Option A is correct because increasing the number of shards in the Kinesis data stream raises the stream's read throughput capacity (each shard supports up to 1 MB/s or 1,000 records/s for reads), which directly addresses the growing MillisBehindLatest by allowing the Flink source to consume data faster. Option C is correct because enabling auto-scaling for the Flink application (via Kinesis Data Analytics' automatic scaling of KPUs) dynamically adds parallel task slots and CPU resources when utilization is high, relieving the sustained >80% CPU pressure on task managers. Option E is correct because increasing the Flink application's parallelism distributes the workload across more parallel subtasks and task slots, lowering per-task CPU load and improving overall processing throughput. Option B is not appropriate because shortening the checkpoint interval increases checkpointing overhead and I/O rather than reducing state size, potentially worsening performance. Option D is wrong because decreasing the number of task managers reduces available CPU and parallelism, which would increase contention and further degrade the application.

Answer

Decrease the checkpoint interval to reduce state size.

Answer

Decrease the number of task managers to reduce CPU contention.

Question 8

Order the steps to set up a Kinesis Data Analytics application for real-time stream processing.

Accepted Answer

Step 1: Set up source stream; Step 2: Create analytics application; Step 3: Configure application with source and logic; Step 4: Start application; Step 5: Monitor performance. First, set up the source stream. Then create the analytics application, configure it with the source and logic, start it, and finally monitor performance.

Answer

Step 1: Create analytics application; Step 2: Configure application with source and logic; Step 3: Set up source stream; Step 4: Start application; Step 5: Monitor performance

Answer

Step 1: Set up source stream; Step 2: Create analytics application; Step 3: Start application; Step 4: Configure application with source and logic; Step 5: Monitor performance

Answer

Step 1: Set up source stream; Step 2: Create analytics application; Step 3: Configure application with source and logic; Step 4: Monitor performance; Step 5: Start application

Question 9

Arrange the steps to set up a streaming ETL pipeline using Amazon Kinesis Data Firehose to Amazon S3.

Accepted Answer

1. Create a Kinesis Data Firehose stream
2. Configure the data source
3. Set the S3 destination
4. Enable optional Lambda transformation
5. Test the pipeline. First, create the Firehose stream, configure source, set S3 destination, enable optional Lambda transformation, and test.

Answer

1. Configure the data source
2. Create a Kinesis Data Firehose stream
3. Enable optional Lambda transformation
4. Set the S3 destination
5. Test the pipeline

Answer

1. Create a Kinesis Data Firehose stream
2. Set the S3 destination
3. Configure the data source
4. Enable optional Lambda transformation
5. Test the pipeline

Answer

1. Configure the data source
2. Set the S3 destination
3. Create a Kinesis Data Firehose stream
4. Enable optional Lambda transformation
5. Test the pipeline

Question 10

Match each AWS data analytics service to its primary function.

Accepted Answer

Amazon Athena: Query data in Amazon S3 using standard SQL. The correct matches are: Amazon Athena for serverless SQL querying on S3, Amazon Redshift for data warehousing, Amazon EMR for big data processing, and Amazon Kinesis for real-time streaming. Common confusions include swapping the roles of Athena and Redshift.

Answer

Amazon Athena: Fully managed data warehouse

Answer

Amazon Redshift: Query data in S3 using SQL

Question 11

A data engineer notices that a nightly AWS Glue ETL job has been failing for the past three days with the error 'Unable to locate credentials'. The job uses an IAM role for execution. What is the most likely cause of this error?

Accepted Answer

The IAM role's trust policy does not include glue.amazonaws.com as a trusted entity.. The error 'Unable to locate credentials' indicates that the AWS Glue job cannot obtain AWS credentials to authenticate API calls. Since the job uses an IAM role for execution, the most likely cause is that the trust policy of that IAM role does not include 'glue.amazonaws.com' as a trusted entity. Without this trust relationship, AWS Glue cannot assume the role and thus has no credentials to sign requests.

Answer

The IAM role does not have an access key attached.

Answer

The S3 bucket name in the job parameters is misspelled.

Answer

The JDBC connection string contains an incorrect password.

Question 12

A data engineer is troubleshooting a slow Amazon Redshift query that joins a large fact table with several dimension tables. The EXPLAIN plan shows a hash join on the distribution key, but the query still runs slowly. The fact table is distributed by KEY(column_x) and the dimension tables are distributed ALL. The engineer notices that the fact table has a high number of rows with the same value in column_x. What is the most likely cause of the slow performance?

Accepted Answer

The fact table's distribution key column has data skew, causing uneven data distribution across nodes.. Data skew in the distribution key column_x causes some slices to hold a disproportionate number of rows, leading to uneven workload distribution during the hash join. The EXPLAIN plan shows a hash join on the distribution key, which should be efficient if data is evenly distributed, but skew forces the node with the most rows to become a bottleneck, slowing the entire query.

Answer

The dimension tables should be distributed by KEY instead of ALL.

Answer

The Redshift cluster does not have enough disk space.

Answer

The fact table does not have a sort key.

Question 13

A company uses Amazon S3 to store large CSV files and runs Amazon Athena queries on them. The queries are becoming slower as data grows. A data engineer suggests converting the files to Apache Parquet format and partitioning the data. What is the primary benefit of converting to Parquet?

Accepted Answer

Parquet stores data in a columnar format, reducing the amount of data scanned per query.. Parquet is a columnar storage format that stores data by columns rather than rows. When Athena queries only a subset of columns, it can read just those columns from disk, drastically reducing the amount of data scanned per query. This directly addresses the performance slowdown because Athena charges by data scanned, and less scanning means faster queries and lower costs.

Answer

Parquet allows schema evolution without rewriting files.

Answer

Parquet supports nested data structures that CSV cannot.

Answer

Parquet is compressed by default, reducing storage costs.

Question 14

A data engineer is running an AWS Glue ETL job that reads from an Amazon RDS MySQL database and writes to Amazon S3. The job fails with a 'Communications link failure' error. The security group for the RDS instance allows inbound traffic from the Glue job's security group. What is the most likely cause of the failure?

Accepted Answer

The Glue job's security group does not allow outbound traffic to the RDS security group on port 3306.. AWS Glue ETL jobs run in a VPC that requires outbound security group rules to initiate connections to RDS. Even if the RDS security group allows inbound traffic from the Glue security group, the Glue security group must also have an outbound rule allowing traffic to the RDS security group on port 3306 (MySQL default port). Without this outbound rule, the TCP handshake from Glue to RDS fails, causing a 'Communications link failure'.

Answer

The JDBC connection string in the Glue job does not include the database name.

Answer

The Glue job is using the wrong JDBC driver.

Answer

The IAM role used by the Glue job does not have rds:Connect permission.

Question 15

A company is using Amazon DynamoDB as a data store for a real-time application. The application reads a single item by primary key and occasionally updates it. The data engineer notices high read latency during peak hours. Which TWO actions would most effectively reduce read latency?

Accepted Answer

Increase the read capacity units for the table.. Option A is correct because the workload is read-heavy with single-item reads by primary key, and if the table is provisioned with insufficient read capacity units (RCUs), requests will throttle or queue during peak hours, so raising the provisioned RCUs directly increases available read throughput and reduces latency. Option E is correct because DynamoDB Accelerator (DAX) is an in-memory cache purpose-built for DynamoDB that serves eventually consistent reads of individual items in microseconds, which is ideal for this read-by-primary-key pattern and offloads repeated reads from the table. Option B is not appropriate because global tables provide multi-Region active-active replication for availability and locality, not lower latency for a single-Region read pattern, and they add replication overhead. Option C is not appropriate because a local secondary index only provides an alternative sort key on the same partition key and does not speed up reads that already use the primary key. Option D is wrong because disabling auto-scaling and fixing read capacity removes the ability to scale with peak demand and would likely worsen throttling and latency.

Answer

Enable DynamoDB global tables.

Answer

Add a local secondary index on the table.

Answer

Disable auto-scaling and set a fixed read capacity.