DEA-C01 Data Operations and Support Practice Test 20 — 15 Questions

Question 1

A data engineer is designing a disaster recovery plan for an Amazon Redshift data warehouse. The cluster is in us-east-1 and must be recoverable in us-west-2 with minimal data loss. Which THREE actions should the engineer take? (Choose THREE)

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

Accepted Answer

Create manual snapshots and copy them to us-west-2. Options A, D, and E are correct. To recover an Amazon Redshift cluster in a different region (us-west-2) from us-east-1, you need to have snapshots available in the target region. Manual snapshots (option A) can be copied to us-west-2 and are retained even if the cluster is deleted. Automated snapshots with a retention period (option D) ensure regular backups, and by configuring automated snapshot copy to us-west-2 (option E), these snapshots are automatically replicated to the target region for cross-region recovery. Option B (multi-AZ) provides high availability within a single region but does not help with cross-region disaster recovery. Option C (concurrent scaling) improves query performance under load, not disaster recovery.

Answer

Deploy Redshift in a multi-AZ configuration

Answer

Enable Redshift concurrent scaling

Question 2

A data engineer needs to set up a disaster recovery solution for an Amazon RDS for MySQL database. The database must be available in another AWS Region with minimal data loss. What is the simplest approach?

Accepted Answer

Create a cross-Region read replica of the database.. Cross-Region read replica. Amazon RDS for MySQL supports cross-Region read replicas, which provide asynchronous replication to another Region. In a disaster, you can promote the read replica to a standalone primary database, minimizing data loss (typically seconds). Option A is wrong because Multi-AZ is within a single Region and does not provide cross-Region protection. Option B is wrong because AWS DMS is a separate service that requires ongoing management and cost; it is not the simplest approach. Option C is wrong because manual snapshots copied daily have a Recovery Point Objective (RPO) of up to 24 hours, resulting in significant data loss.

Answer

Enable Multi-AZ deployment in the same Region.

Answer

Set up AWS Database Migration Service (DMS) for continuous replication.

Answer

Take a manual snapshot and copy it to the other Region daily.

Question 3

A company uses Amazon Kinesis Data Firehose to deliver streaming data to Amazon S3. The data must be transformed in real-time using a custom Lambda function. Which TWO steps are required to enable this? (Choose TWO)

Accepted Answer

Configure Kinesis Data Firehose to use a Lambda function for data transformation. 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.

Answer

Create a Kinesis Data Analytics application to transform the data

Answer

Write the transformation logic directly in the Firehose delivery stream configuration

Answer

Use Kinesis Data Streams as the source for Firehose

Question 4

Refer to the exhibit. An IAM policy is attached to a user who needs to read objects from the 'example-bucket' S3 bucket. The user reports being unable to read any object under the 'confidential/' prefix. What is the reason for this access issue?

Accepted Answer

The explicit deny statement overrides the allow statement. An explicit deny statement overrides any allow statement, regardless of the order in which they appear. In this policy, there is an allow for GetObject on all objects in example-bucket, but there is an explicit deny for GetObject on the 'confidential/' prefix. Since explicit deny takes precedence, the user cannot read objects under that prefix. Option A is incorrect because the order of evaluation does not matter; explicit deny always wins. Option B is incorrect because the deny statement does not need an explicit allow; the deny itself is effective. Option D is incorrect because the resource ARN in the deny statement is correctly specified as 'arn:aws:s3:::example-bucket/confidential/*'.

Answer

The allow statement is evaluated before the deny statement

Answer

The deny statement is missing an explicit allow for the confidential prefix

Answer

The resource ARN in the deny statement is incorrect

Question 5

A data engineer is troubleshooting an AWS Glue ETL job that suddenly started failing with 'An error occurred while calling o103.pyWriteDynamicFrame. Unknown error'. The job writes data to an Amazon Redshift table. Which step should the engineer take FIRST?

Accepted Answer

Review the job's CloudWatch Logs for detailed error messages.. The error message 'An error occurred while calling o103.pyWriteDynamicFrame. Unknown error' is generic and does not specify the root cause. The first troubleshooting step should be to review the job's CloudWatch Logs, which provide detailed error messages, stack traces, and other diagnostic information. Option A is incorrect because recreating the table with a different distribution style is a premature action without understanding the cause. Option B is incorrect because testing with a small dataset may not reproduce the issue and is not the first step; logs can help determine if the issue is data-related. Option C is incorrect because updating the Redshift JDBC driver version is not the first step; log analysis should precede any changes.

Answer

Recreate the Redshift table with a different distribution style.

Answer

Test the job with a small sample dataset to isolate the issue.

Answer

Update the Redshift JDBC driver version in the Glue job.

Question 6

Refer to the exhibit. A company has an S3 bucket 'my-data-lake' with the lifecycle policy shown. Objects under the 'logs/' prefix are being moved to GLACIER after 30 days and expire after 365 days. A data engineer notices that objects older than 365 days are still present in the bucket and are not being deleted. What is the most likely cause?

Accepted Answer

The expiration days count from the transition date, not the object creation date. When a lifecycle rule includes both transition to GLACIER and expiration, the expiration days count from the transition date, not from the object creation date. In this scenario, objects transition to GLACIER at 30 days, so the expiration at 365 days actually triggers after 395 days from creation (30 + 365). Therefore, objects older than 365 days from creation but younger than 395 days are not yet expired, explaining why they remain.

Answer

Lifecycle expiration does not apply to objects in GLACIER storage class

Answer

The rule status is disabled

Answer

The prefix filter does not match the objects

Question 7

A data engineer needs to monitor the number of records processed by an AWS Glue ETL job and send an alert if the count drops below a threshold. Which AWS service should be used to create this custom metric?

Accepted Answer

Amazon CloudWatch. Amazon CloudWatch is the correct service for creating custom metrics because it allows you to publish your own data points, such as the number of records processed by an AWS Glue ETL job. You can use the CloudWatch PutMetricData API or the AWS Glue job script to emit a custom metric, then set an alarm on that metric to trigger an alert when the count drops below a threshold.

Answer

Amazon S3

Answer

AWS Config

Answer

AWS CloudTrail

Question 8

Refer to the exhibit. A data engineer runs two queries on an Athena table partitioned by 'ds'. Both queries scan the same amount of data. What does this indicate?

Accepted Answer

The partition column is not being used as a filter. 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.

Answer

The table does not have any partitions defined

Answer

The table is not partitioned

Answer

Partition pruning is working correctly

Question 9

A company's Amazon Redshift cluster is running slowly. The data engineer suspects that table design is the cause. Which TWO design practices can improve query performance? (Choose TWO.)

Accepted Answer

Define appropriate sort keys on frequently filtered columns.. Options A and C are correct. Defining appropriate sort keys on frequently filtered columns allows Redshift to use zone maps to skip irrelevant blocks, reducing the amount of data scanned. Defining appropriate distribution keys to collocate data on the same node slices minimizes data shuffling during joins, improving performance. Option B is incorrect because GROUP BY and DISTINCT are query optimization techniques, not table design practices. Option D is incorrect because resizing the cluster (adding nodes or slices) is an infrastructure change, not a table design practice. Option E is incorrect because using VARCHAR instead of CHAR for fixed-length strings does not directly impact query performance from a table design perspective; it is a data type choice that affects storage but not query performance via sort or distribution design.

Answer

Use GROUP BY instead of DISTINCT in queries.

Answer

Increase the number of slices per node by resizing the cluster.

Answer

Use VARCHAR instead of CHAR for fixed-length strings.

Question 10

A data engineer is troubleshooting an AWS Glue ETL job that fails intermittently with the error 'Rate exceeded.' The job reads from an Amazon RDS for MySQL source and writes to Amazon S3. What is the MOST likely cause of this error?

Accepted Answer

The number of Glue job workers or parallel queries is exceeding the maximum connections or IOPS of the RDS instance.. The 'Rate exceeded' error when reading from RDS typically indicates that the number of connections or queries per second exceeds the RDS instance's maximum limits. Option A is incorrect because the job reads from RDS, not Kinesis. Option C is incorrect because S3 writes return a 503 SlowDown error, not 'Rate exceeded'. Option D is incorrect because insufficient IAM permissions cause an access denied error.

Answer

The Glue job is using Amazon Kinesis Data Streams as a source, which has a shard throughput limit.

Answer

The Amazon S3 bucket has a bucket policy that limits the number of objects written per second.

Answer

The IAM role attached to the Glue job does not have sufficient permissions to read from RDS.

Question 11

A data engineer is designing an ETL pipeline that uses AWS Glue to process data from an Amazon DynamoDB table and write results to an S3 bucket in Parquet format. The pipeline must handle schema changes in the source DynamoDB table. Which THREE steps should the engineer take to ensure the pipeline handles schema evolution? (Choose THREE.)

Accepted Answer

Use Glue's 'recast' transformation to handle type changes.. Options A, B, and E are correct. Option A: Glue's 'recast' transformation can handle type changes by converting data types as needed. Option B: Setting the Glue crawler to update the table's schema in the Data Catalog ensures that new columns or changes in the source DynamoDB table are reflected. Option E: Using Spark's 'mergeSchema' option when writing to S3 allows Parquet files to have differing schemas, enabling schema evolution. Option C is incorrect because converting Parquet to CSV does not help with schema evolution and may introduce data loss or inefficiency. Option D is incorrect because partitioning by date and deleting old partitions is a data retention strategy, not a schema evolution technique.

Answer

Convert the Parquet output to CSV to avoid schema constraints.

Answer

Partition the data by date and delete old partitions.

Question 12

A data engineer needs to transfer 50 TB of data from an on-premises Hadoop cluster to Amazon S3. The network bandwidth is limited to 500 Mbps. Which TWO methods are appropriate for this transfer? (Choose TWO.)

Accepted Answer

Order an AWS Snowball Edge device to physically ship the data.. Options B and E are correct. AWS Snowball Edge is a physical device suitable for transferring large amounts of data (like 50 TB) when network bandwidth is limited (500 Mbps). AWS DataSync can also transfer data over the network with built-in optimization, making it appropriate for this scenario. Options A and C are incorrect: AWS Direct Connect provides a dedicated connection but does not increase bandwidth beyond 500 Mbps, and S3 Transfer Acceleration speeds up transfers but still relies on the same limited bandwidth. Option D is incorrect: Amazon Kinesis Data Firehose is designed for real-time streaming, not bulk data transfer.

Answer

Set up an AWS Direct Connect connection for higher bandwidth.

Answer

Use S3 Transfer Acceleration to upload over the internet.

Answer

Use Amazon Kinesis Data Firehose to stream the data.

Question 13

A data engineer at a financial services company manages an AWS Glue ETL pipeline that processes transaction data from Amazon S3 to Amazon Redshift for reporting. The pipeline runs every hour and uses a Glue job that reads Parquet files, performs transformations in Spark, and writes to Redshift using the JDBC connector. Recently, the job has been failing intermittently with the error: 'java.sql.BatchUpdateException: ERROR: null value in column "transaction_id" violates not-null constraint'. The data engineer has verified that the source Parquet files do contain non-null values for transaction_id. The job uses a DynamicFrame and applies a mapping to rename columns. The engineer also noticed that the failure occurs only during peak hours when there is high concurrency on Redshift. Which course of action should the engineer take to resolve this issue?

Accepted Answer

Review the Glue job's mapping transformation to ensure transaction_id is correctly mapped and not dropped.. The error indicates that transaction_id is being nullified or dropped during the Glue job's mapping transformation. Even though source files have non-null values, the mapping could be incorrectly mapping or omitting the column, causing nulls to be written to Redshift. The failure during peak hours is coincidental; the root cause is the mapping logic. Option A is incorrect because filtering nulls would not fix the mapping error and could discard valid data. Option B is incorrect because increasing Redshift WLM concurrency scaling does not address the null constraint violation. Option D is incorrect because more Glue workers do not fix the transformation issue; the problem is data quality, not capacity.

Answer

Add a filter in Glue to remove rows with null transaction_id.

Answer

Increase the Redshift WLM concurrency scaling to handle more queries.

Answer

Increase the number of Glue workers to handle peak-hour load.

Question 14

A data engineer notices that an Amazon Kinesis Data Firehose delivery stream is failing to deliver data to an Amazon S3 bucket. The engineer verifies that the S3 bucket exists and that the IAM role attached to the delivery stream has the necessary permissions. What is the MOST likely cause of the failure?

Accepted Answer

No data is being written to the Kinesis Data Firehose delivery stream.. If no data is written to the stream, Firehose has nothing to deliver. Option A is wrong because CloudWatch Logs is for monitoring, not for storing delivery data. Option B is wrong because insufficient permissions would cause an access denied error. Option D is wrong because delivery streams typically use S3 as a destination, not Kinesis Data Streams.

Answer

The delivery stream is configured to deliver to Amazon CloudWatch Logs.

Answer

The IAM role does not have permissions to write to the S3 bucket.

Answer

The delivery stream is configured to deliver to Amazon Kinesis Data Streams instead of S3.

Question 15

A data engineer is responsible for a real-time data pipeline that ingests clickstream data from a website into Amazon Kinesis Data Streams, then processed by an AWS Lambda function that writes to an Amazon DynamoDB table for user session tracking. The Lambda function is idempotent and uses the DynamoDB PutItem API with a condition expression to avoid overwriting existing records. Over the past week, the engineer has observed an increase in DynamoDB write throttling (ProvisionedThroughputExceededException) during peak traffic hours. The DynamoDB table has on-demand capacity. The engineer checks the Lambda function's reserved concurrency and finds it set to 1000. The Kinesis stream has 10 shards. The Lambda function's batch size is set to 100. The engineer suspects that the retry behavior is causing duplicate writes and throttling. Which change should the engineer make to reduce throttling?

Accepted Answer

Decrease the Lambda batch size to 10 to reduce the number of records processed per invocation.. 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.

Answer

Increase the number of Kinesis shards to 20 to distribute the load.

Answer

Decrease the Lambda reserved concurrency to 500 to limit the number of concurrent invocations.

Answer

Use a DynamoDB Stream to trigger a second Lambda function that writes to the table.