DEA-C01 Timed Practice Test 4 — 10 Questions

Question 1

A company is designing a data lake on Amazon S3. Which TWO strategies improve query performance for Amazon Athena?

Accepted Answer

Partition the data by frequently queried columns such as date or region.. Partitioning data by frequently queried columns (e.g., date or region) allows Athena to prune the data scanned by only reading the relevant partitions, reducing the amount of data scanned and improving query performance. This is a core optimization for Athena, which charges based on data scanned and performs better with less I/O.

Answer

Enable S3 Versioning on the bucket.

Answer

Use server-side encryption with AWS KMS (SSE-KMS).

Answer

Store data in CSV format with header rows.

Question 2

A data engineer notices that an AWS Glue job processing data from an Amazon S3 bucket frequently fails with 'OutOfMemoryError'. The job reads CSV files, applies transformations, and writes Parquet to another S3 bucket. The job has 10 workers of type G.1X. Which change is MOST likely to resolve the issue?

Accepted Answer

Change the worker type from G.1X to G.2X. The G.1X worker type provides 16 GB of memory per worker. An OutOfMemoryError indicates that the job's memory requirements exceed this limit. Upgrading to G.2X doubles the memory per worker to 32 GB, directly addressing the memory shortage without changing the parallelism or incurring the overhead of additional workers.

Answer

Increase the number of workers to 20

Answer

Change the worker type from G.1X to G.8X

Answer

Enable the Spark UI to monitor memory and tune the job

Question 3

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?

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 4

A data engineer runs the above AWS CLI command and receives the output. The object is part of an S3 Lifecycle policy that transitions objects to Glacier Instant Retrieval after 30 days. The object was created on January 1, 2023. Why is the object still in STANDARD_IA storage class?

Accepted Answer

The object has not reached the transition age of 30 days yet. Option C is correct because the S3 Lifecycle rule transitions objects to Glacier Instant Retrieval after 30 days, but the object was created on January 1, 2023, and the current date (implied by the command output) is before January 31, 2023. The transition age is calculated from the object's LastModified date, not from any other timestamp, and the object must be at least 30 days old before S3 applies the transition. Since the object is only 20 days old (as of January 21, 2023, based on the output showing STANDARD_IA), it has not yet met the 30-day threshold.

Answer

The Lifecycle policy has a filter that excludes this object's prefix

Answer

Versioning is enabled and the current version is not the oldest

Answer

The metadata timestamp is used for lifecycle transitions instead of LastModified

Question 5

A data engineer is designing a data lake on Amazon S3 for analytics. The data includes sensitive PII that must be encrypted at rest. The company requires that the encryption keys be managed by the company's own hardware security module (HSM) and rotated every 90 days. Which TWO options meet these requirements? (Choose TWO.)

Accepted Answer

Use S3 server-side encryption with customer-provided keys (SSE-C). Option B is correct because SSE-C allows you to provide your own encryption keys, which can be managed and rotated from your own HSM. The keys are used server-side by S3 to encrypt objects at rest, but S3 does not store the keys—you manage them entirely, meeting the requirement for key management on your own HSM with 90-day rotation.

Answer

Use S3 server-side encryption with AWS KMS and an AWS managed key

Answer

Use client-side encryption with keys stored in AWS Secrets Manager

Answer

Use S3 server-side encryption with S3 managed keys (SSE-S3)

Question 6

A data engineer is troubleshooting a Glue ETL job that reads from an S3 bucket and writes to a Redshift table. The job fails with a 'MemoryError' when processing a large dataset. Which TWO actions should the engineer take to resolve this issue? (Choose TWO.)

Accepted Answer

Increase the number of DPUs and set 'spark.sql.shuffle.partitions' to a higher value.. Option A is correct because increasing the number of DPUs (Data Processing Units) provides more memory and compute resources to the Glue job, directly addressing the MemoryError. Setting 'spark.sql.shuffle.partitions' to a higher value reduces the amount of data shuffled per partition, preventing out-of-memory errors during wide transformations like joins or aggregations.

Answer

Increase the number of DPUs and set 'coalesce(1)' in the script.

Answer

Decrease the number of DPUs and increase 'spark.shuffle.partitions'.

Answer

Set the 'RedshiftTempDir' parameter to a larger S3 bucket.

Question 7

A data engineer is troubleshooting an AWS Glue job that reads from an Amazon RDS for PostgreSQL database using a JDBC connection. The job fails with the error 'java.sql.SQLException: No suitable driver'. Which TWO actions should the engineer take to resolve this issue? (Select TWO.)

Accepted Answer

Verify that the connection string in the job's JDBC URL uses the correct format and includes the driver class. Option A is correct because the 'No suitable driver' error in JDBC indicates that the driver class specified in the JDBC URL is either missing or incorrect. For PostgreSQL, the JDBC URL must follow the format 'jdbc:postgresql://host:port/database' and the driver class must be 'org.postgresql.Driver'. If the URL is malformed or the driver class is not properly referenced, the Glue job cannot load the driver, leading to this specific SQLException.

Answer

Check that the Glue job's VPC and security groups allow outbound traffic to the RDS instance

Answer

Restart the Glue job with a higher timeout value

Answer

Update the IAM role associated with the Glue job to allow 'rds:*' permissions

Question 8

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 9

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 10

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.. Increasing the number of shards in the Kinesis data stream (Option A) directly increases the ingestion capacity and parallelism source for the Flink application. With more shards, the application can read data from more partitions concurrently, reducing the backlog indicated by 'MillisBehindLatest'. This is a fundamental scaling action for Kinesis-based Flink applications.

Answer

Decrease the checkpoint interval to reduce state size.

Answer

Decrease the number of task managers to reduce CPU contention.