DEA-C01 Data Operations and Support Practice Test 4 — 15 Questions

Question 1

A data engineer applies the above S3 bucket policy to an S3 bucket used by a Glue ETL job. The Glue job writes objects to the bucket. Which of the following is true about the behavior of the policy?

Accepted Answer

The policy denies all PutObject requests because the Allow and Deny statements are contradictory.. In AWS IAM policy evaluation, an explicit Deny always overrides any Allow. The policy has an Allow statement granting s3:PutObject for all principals, but a separate Deny statement explicitly denies s3:PutObject when the encryption condition is not aws:kms. Since the Deny applies to all PutObject requests (including those with no encryption or AES256), and the Allow does not include a condition to match only aws:kms, the Deny takes precedence and blocks all PutObject requests, making the policy effectively deny all PutObject operations.

Answer

The policy allows PutObject with aws:kms encryption because the Allow statement is broader.

Answer

The policy allows PutObject with no encryption because the Deny only applies to PutObject.

Answer

The policy allows PutObject with AES256 encryption and denies PutObject with aws:kms encryption.

Question 2

A company runs a nightly batch processing pipeline using AWS Glue ETL jobs. The pipeline reads data from an Amazon S3 bucket, transforms it, and writes results to an Amazon Redshift cluster. Recently, the data volume has increased significantly, and some Glue jobs are failing with the error 'java.lang.OutOfMemoryError: Java heap space'. The data engineer needs to modify the job configuration to prevent these failures without changing the code. The job currently uses 10 DPUs and processes data in a single Spark DataFrame. Which of the following is the MOST effective solution?

Accepted Answer

Increase the number of DPUs to 20 and enable job bookmarking for incremental processing.. Increasing DPUs from 10 to 20 provides more memory and compute resources, directly addressing the 'java.lang.OutOfMemoryError: Java heap space' caused by insufficient memory for the single DataFrame. Enabling job bookmarking allows incremental processing, which reduces the volume of data processed per run, further mitigating memory pressure without code changes.

Answer

Reduce the number of DPUs to 5 and increase the Spark executor memory by setting 'spark.executor.memory' in job parameters.

Answer

Change the script to use DynamicFrame instead of DataFrame and disable the 'spark.sql.shuffle.partitions' configuration.

Answer

Add a 'coalesce(1)' operation before writing to Redshift to reduce the number of output files.

Question 3

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 4

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 5

Match each AWS data compression format to its typical use case.

Accepted Answer

Snappy: Designed for high speed and efficient compression, ideal for real-time data processing.. In AWS data engineering, compression format choice depends on trade-offs between compression ratio and speed. Snappy and LZO are optimized for speed, Gzip and Bzip2 for higher compression (with Bzip2 highest), and Zstandard offers a middle ground. Common misassociations include confusing Gzip with Bzip2 for highest compression and missing Snappy's speed focus.

Answer

Gzip: Provides the highest compression ratio, best for archival storage.

Answer

Bzip2: Commonly used for log files due to good compression ratio.

Question 6

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 7

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 8

A company runs a data pipeline using AWS Lambda to process records from an Amazon Kinesis Data Stream. Recently, the Lambda function has been experiencing high invocation errors and the stream is throttling. The function performs simple transformations and writes to Amazon S3. What is the most effective way to reduce throttling and errors?

Accepted Answer

Increase the batch size in the Lambda event source mapping.. Increasing the batch size in the Lambda event source mapping allows each invocation to process more records from the Kinesis stream, reducing the number of total invocations. This lowers the rate at which Lambda polls the stream, which decreases the likelihood of hitting the Kinesis read throughput limits (5 transactions per second per shard) and reduces throttling errors. The simple transformations and S3 writes are likely I/O-bound, so larger batches improve throughput without increasing invocation concurrency.

Answer

Increase the Lambda function timeout.

Answer

Enable provisioned concurrency on the Lambda function.

Answer

Increase the number of shards in the Kinesis stream.

Question 9

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 10

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 11

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 12

A company runs a critical data pipeline using Amazon Kinesis Data Firehose to deliver data to Amazon S3. The data is compressed with GZIP and partitioned by year/month/day/hour. Recently, the delivery to S3 has been failing with 'Rate exceeded' errors. The Firehose delivery stream has a buffer size of 128 MB and buffer interval of 60 seconds. What is the most effective way to resolve this issue?

Accepted Answer

Increase the buffer size to 256 MB and buffer interval to 120 seconds.. The 'Rate exceeded' error indicates that Kinesis Data Firehose is sending requests to S3 at a rate that exceeds the S3 bucket's request rate limits for PUT operations. Increasing the buffer size to 256 MB and the buffer interval to 120 seconds allows Firehose to accumulate more data before each S3 PUT request, reducing the number of requests per second and staying within S3's 3,500 PUT requests per second limit per prefix. This directly addresses the throttling issue without changing the data volume.

Answer

Transition objects to S3 Glacier after 30 days.

Answer

Decrease the buffer size to 64 MB and buffer interval to 30 seconds.

Answer

Enable server-side encryption on the S3 bucket.

Question 13

A data engineer is monitoring an Amazon EMR cluster running a Spark job. The job is processing a large dataset and the engineer notices that the cluster is using a high percentage of disk space on the core nodes. The job fails with 'No space left on device' error. What is the most effective way to resolve this issue without modifying the job logic?

Accepted Answer

Increase the number of core nodes in the cluster.. Increasing the number of core nodes distributes the intermediate shuffle data and temporary files across more nodes, reducing the per-node disk usage. This directly addresses the 'No space left on device' error without altering the Spark job logic, as core nodes in EMR store both HDFS data and local shuffle spills.

Answer

Attach additional EBS volumes to the core nodes.

Answer

Increase the EBS volume size attached to the core nodes.

Answer

Change the core node instance type to one with more memory.

Question 14

A company uses Amazon CloudWatch Logs to collect application logs from EC2 instances. The logs are exported to Amazon S3 for long-term storage. Recently, the export task failed with the error 'Access Denied'. What is the most likely cause of this failure?

Accepted Answer

The IAM role does not have s3:PutObject permission on the destination bucket.. The export task from CloudWatch Logs to S3 uses an IAM role to write data to the destination bucket. If the role lacks the s3:PutObject permission, the S3 service will reject the request with an 'Access Denied' error. This is the most common cause because the export operation requires write access to the bucket.

Answer

The S3 bucket policy denies access from the CloudWatch Logs service.

Answer

The IAM role does not have s3:ListBucket permission.

Answer

The EC2 instances are in a VPC without a VPC endpoint for CloudWatch Logs.

Question 15

A data engineer is troubleshooting an AWS Glue crawler that is not correctly inferring the schema of CSV files stored in Amazon S3. The files have headers, but the crawler is treating the header row as data. The crawler is configured with a custom classifier that has a CSV classifier with 'Column header' set to 'Use first row as header'. What is the most likely reason the crawler is not recognizing the header?

Accepted Answer

The CSV classifier's 'Quote symbol' setting does not match the files.. The CSV classifier's 'Quote symbol' setting must match the files. If the files do not use quotes, but the classifier expects a quote symbol (e.g., double quotes), the classifier may fail to match, causing the crawler to fall back to default behavior and treat the header row as data. Option B is wrong because a varying number of columns would cause schema issues, not header misrecognition. Option C is wrong because the delimiter is unrelated to header detection; a custom delimiter can be set separately. Option D is wrong because case of header text does not affect the crawler's ability to recognize headers.

Answer

The CSV files have a varying number of columns across rows.

Answer

The CSV files have a different delimiter than the default comma.

Answer

The header row contains uppercase letters.