MLS-C01 Data Engineering Practice Test 16 — 15 Questions

Question 1

A data engineer uses the IAM policy above for an AWS Lambda function that processes data in S3 and triggers an AWS Glue job. The Lambda function is unable to start the Glue job. What is the most likely cause?

Accepted Answer

The policy does not include iam:PassRole permission.. Option B is correct. The policy lacks 'glue:StartJobRun' action for the resource? Actually, the policy includes 'glue:StartJobRun' but the resource is specific to the job. However, the Lambda function's execution role may not have permission to pass the IAM role to Glue (iam:PassRole). Option B is correct: missing 'iam:PassRole' permission. Option A is wrong because the policy has GetObject on the bucket. Option C is wrong because Glue job name is correct. Option D is wrong because S3 PutObject is allowed.

Answer

The Glue job name in the resource ARN is misspelled.

Answer

The policy does not allow s3:PutObject on the bucket.

Answer

The policy does not include s3:GetObject on the bucket.

Question 2

A data engineer runs the AWS CLI command above to inspect an object in S3. The engineer wants to query this metadata (kafka-offset) using Amazon Athena to track processing progress. How can the engineer make this metadata available for Athena queries without modifying the existing data pipeline?

Accepted Answer

Use AWS Glue to create a table that includes the metadata as a column by running an ETL job.. Option B is correct. S3 object metadata is not automatically available in Athena. The engineer can use AWS Glue to crawl the S3 bucket and extract metadata into the Data Catalog; however, custom metadata is not crawled by default. A better approach is to store the metadata in a separate table or use S3 object tagging. But among options, Option B is correct: configure a Glue crawler to extract metadata? Actually, Glue crawlers do not extract custom metadata. Option D is correct: use S3 object tags, which can be queried via Athena using the $metadata column? Not exactly. Let's rethink. The best practice is to store metadata in a separate manifest file. Option B is correct because you can create a Glue table with a custom classifier to extract metadata? Actually, the correct answer is to use S3 Object Lambda to add metadata to the object content? Not listed. Given the options, Option B is correct: Use AWS Glue to create a table that includes the metadata? But Glue crawlers don't capture custom metadata. Option A is wrong because you cannot query metadata directly. Option C is wrong because Lambda cannot add metadata to existing objects without rewriting. Option D is correct: Use S3 object tags, which can be queried via Athena? Actually, Athena does not query tags. The best answer is to store metadata in a separate manifest file in S3 and query that. But the most practical is to use a Glue ETL job to read the objects and extract metadata into a table. Option B is the closest: 'Use AWS Glue to create a table that includes the metadata as a column' - you can use a Glue ETL job to extract metadata and store in Parquet. So Option B is correct.

Answer

Use S3 object tags instead of metadata and query the tags using Athena.

Answer

Use an AWS Lambda function to copy the metadata into the object's content as a new line.

Answer

Use Amazon Athena to query the object metadata directly by referencing the metadata field.

Question 3

A data engineer configures an S3 event notification to trigger an AWS Lambda function when a new object is created in 'my-input-bucket'. The Lambda function processes the CSV file and writes results to 'my-output-bucket'. The engineer notices that the Lambda function is not triggered for some objects. Which step should the engineer take to diagnose the issue?

Accepted Answer

Check the Lambda function's resource-based policy to ensure S3 has permission to invoke the function.. Option D is correct. The most likely issue is missing permissions. The S3 bucket must have permission to invoke the Lambda function. The engineer should check the Lambda resource-based policy to ensure it allows invocation from S3. Option A is wrong because the event notification configuration is separate. Option B is wrong because the Lambda function's execution role needs permission to write to output bucket, but that would not prevent triggering. Option C is wrong because CloudWatch Logs would show invocations but not if not triggered.

Answer

Check the Lambda function's execution role for permissions to write to the output bucket.

Answer

Review the CloudWatch Logs for the Lambda function to see if there are errors.

Answer

Verify that the S3 event notification is configured with the correct prefix and suffix filters.

Question 4

A company is using Amazon Kinesis Data Streams to ingest real-time clickstream data. The data is consumed by a Kinesis Data Analytics application that runs SQL queries. The application has been failing intermittently with 'ProvisionedThroughputExceededException' errors. Which action should be taken to resolve this issue?

Accepted Answer

Increase the number of shards in the Kinesis data stream.. The error indicates that the shard's read throughput limit (5 transactions/second per shard) is being exceeded. Increasing the number of shards increases the total throughput. Option A (increase shard count) is the correct solution. Option B (increase record size) could worsen the problem. Option C (use Kinesis Firehose) changes the architecture but does not address the shard throughput. Option D (disable error logging) does not solve the underlying issue.

Answer

Disable error logging in the Kinesis Data Analytics application.

Answer

Increase the record size in the Kinesis data stream.

Answer

Switch from Kinesis Data Analytics to Kinesis Data Firehose.

Question 5

A data engineering team is designing a data pipeline to process large CSV files (10-50 GB each) stored in Amazon S3. The pipeline must transform the data using AWS Glue and load it into Amazon Redshift for analytics. The team wants to minimize costs while ensuring the pipeline can handle peak loads. Which approach is the most cost-effective?

Accepted Answer

Use AWS Glue with Spark and dynamic frames, scaling the number of workers based on file size.. Option D (use AWS Glue with Spark and dynamic frame) is correct because Glue's Spark-based ETL can handle large files efficiently, and using dynamic frames allows schema inference without manual parsing. Option A (use a single r5.xlarge) may not handle peak loads. Option B (Lambda) has time and memory limits unsuitable for large files. Option C (EMR with Hive) is more complex and typically more expensive than Glue for this use case.

Answer

Use AWS Lambda to process each file and load into Redshift.

Answer

Use Amazon EMR with Hive to transform the data and load into Redshift.

Answer

Use an AWS Glue Python shell job with a single r5.xlarge worker.

Question 6

A company is using Amazon DynamoDB to store sensor data. The data is exported to Amazon S3 using DynamoDB Streams and AWS Lambda for long-term archival. Recently, the Lambda function has been failing due to 'ProvisionedThroughputExceededException' on the DynamoDB stream. What is the most likely cause?

Accepted Answer

The Lambda function is processing records too slowly, causing the stream to throttle.. The error indicates that the DynamoDB stream's read throughput is being exceeded. The Lambda function's event source mapping reads from the stream, and if it is processing too slowly or there are too many shards, it can throttle. Option B (increase the batch size) reduces the number of reads per second. Option A (increase Lambda memory) may not help. Option C (disable stream) would stop the export. Option D (increase DynamoDB write capacity) does not affect stream reads.

Answer

The DynamoDB stream is disabled.

Answer

The DynamoDB table's write capacity is too low.

Answer

The Lambda function does not have enough memory allocated.

Question 7

A data scientist is building a training dataset from data stored in Amazon S3. The data consists of JSON files each containing a 'timestamp' field. The scientist wants to use AWS Glue to catalog the data and enable querying via Amazon Athena. However, Athena queries are returning zero results for time-range filters. What is the most likely cause?

Accepted Answer

The 'timestamp' field is not defined as a partition column in the Glue table.. Athena uses the partition columns derived from the Glue catalog. If the timestamp column is not used as a partition key, queries that filter on it will scan all data. Option B (timestamp is not a partition column) is correct because Glue can automatically partition by date, but the user must set it. Option A (wrong file format) is unlikely if JSON is supported. Option C (Athena cannot query nested JSON) is false; Athena supports JSON. Option D (insufficient permissions) would cause a different error.

Answer

The AWS Glue crawler does not have permissions to read the S3 bucket.

Answer

Athena cannot query nested JSON objects.

Answer

The JSON files are not in the correct format for Athena.

Question 8

A company is streaming data from IoT devices to Amazon Kinesis Data Firehose, which writes to an Amazon S3 bucket. The data is then processed by an AWS Glue ETL job and loaded into Amazon Redshift. The team notices that some records are missing in Redshift. They suspect data loss during the Firehose delivery. Which configuration parameter should be checked first?

Accepted Answer

The buffer interval (e.g., 60 seconds) and buffer size.. Firehose can buffer data before writing to S3. If the buffer interval is too long and the stream ends, data may be lost if the buffer is not flushed. Option C (buffer interval) is the most likely cause. Option A (compression) does not cause loss. Option B (KMS key) is for encryption. Option D (error logging) only logs errors, does not prevent loss.

Answer

The AWS KMS key used for encryption.

Answer

The CloudWatch error logging configuration.

Answer

The compression format (GZIP, Snappy, etc.).

Question 9

A data engineer needs to set up a data pipeline that ingests data from an Amazon RDS MySQL database into Amazon S3. The pipeline should run daily and capture incremental changes (inserts, updates, deletes) from the source database. Which AWS service should be used as the data ingestion tool?

Accepted Answer

AWS Database Migration Service (DMS) with continuous change data capture (CDC).. AWS Database Migration Service (DMS) supports continuous replication and can capture changes using CDC. Option A (DMS with CDC) is correct. Option B (Glue) can do batch but not native CDC. Option C (Data Pipeline) is older and less suited for CDC. Option D (Kinesis) is for streaming, not database replication.

Answer

Amazon Kinesis Data Streams with a Lambda function.

Answer

AWS Data Pipeline with a SQL activity.

Answer

AWS Glue with a scheduled crawler.

Question 10

A company is building a data lake on Amazon S3 and wants to use AWS Glue to catalog the data. The data includes CSV, Parquet, and JSON files. The team wants to ensure that the Glue crawler can infer the schema correctly and update the Data Catalog when new partitions are added. Which crawler configuration should be used?

Accepted Answer

Create a single crawler that includes all file extensions and set the 'Update all new and existing partitions' option.. Option C (create a single crawler that includes all supported file types and set 'Update all new and existing partitions' to true) is correct because Glue can handle multiple formats, and updating partitions ensures new data is cataloged. Option A (separate crawlers per format) is unnecessary. Option B (only Parquet) ignores other formats. Option D (disable partition updates) would miss new partitions.

Answer

Create separate crawlers for each file format and schedule them at different times.

Answer

Use a crawler that only catalogs Parquet files because they are more efficient.

Answer

Use a crawler with 'Update all new and existing partitions' disabled to avoid schema conflicts.

Question 11

A company uses Amazon Kinesis Data Streams with a shard count of 5. The data producer sends 1000 records per second, each 1 KB in size. The consumer application reads from the stream using the Kinesis Client Library (KCL) and processes records. The consumer is experiencing high latency and falling behind. What is the most effective way to improve consumer throughput?

Accepted Answer

Increase the number of shards in the stream.. Increasing the number of shards increases the read throughput and allows more consumers to read in parallel. Option B (increase the number of shards) is correct. Option A (increase record size) is irrelevant. Option C (use Kinesis Data Analytics) adds another service. Option D (use enhanced fan-out) is for multiple consumers, not for a single consumer falling behind.

Answer

Switch to Kinesis Data Analytics for processing.

Answer

Use enhanced fan-out to dedicate read throughput to the consumer.

Answer

Increase the record size to 5 KB.

Question 12

A company wants to store semi-structured data from IoT sensors in a cost-effective manner for occasional querying. The data is not updated once written. Which Amazon S3 storage class is the most cost-effective for this use case?

Accepted Answer

S3 Glacier Deep Archive. S3 Glacier Deep Archive is the lowest-cost storage class for rarely accessed data with retrieval times of 12 hours. Option D is correct. Option A (S3 Standard) is for frequent access. Option B (S3 Intelligent-Tiering) incurs monitoring costs. Option C (S3 One Zone-IA) is for infrequent access but has a minimum storage charge.

Answer

S3 Standard

Answer

S3 One Zone-Infrequent Access

Answer

S3 Intelligent-Tiering

Question 13

Which TWO configurations are required to enable AWS Glue to access data stored in a VPC? (Choose two.)

Accepted Answer

A VPC endpoint for Amazon S3.. Glue jobs running inside a VPC require a VPC endpoint for S3 (or Internet/NAT) to access S3 data, and a Glue connection that specifies the VPC, subnet, and security group. Option A (VPC endpoint for S3) and Option D (Glue connection) are correct. Option B (Internet gateway) is not secure. Option C (NAT gateway) is an alternative but not required if using VPC endpoint. Option E (S3 bucket policy) is not specific to VPC access.

Answer

A NAT gateway in a public subnet.

Answer

An Internet gateway attached to the VPC.

Answer

An S3 bucket policy that allows access from the Glue service principal.

Question 14

Which THREE factors should be considered when choosing between Amazon Kinesis Data Streams and Amazon Kinesis Data Firehose for a real-time data ingestion pipeline? (Choose three.)

Accepted Answer

The need for custom real-time processing logic using consumer applications.. Three key differences: Kinesis Data Streams supports custom processing with consumers (option A), requires manual scaling (option D), and has lower latency (option B). Firehose is for simple delivery with minimal code, auto-scaling, and higher latency due to buffering. Option C (exactly-once delivery) is not guaranteed by either. Option E (built-in analysis) is not a feature of Firehose.

Answer

The need for built-in data transformation and analytics.

Answer

The requirement for exactly-once delivery semantics.

Question 15

Which TWO steps are required to set up cross-account access to an Amazon S3 data lake for AWS Glue jobs running in a different AWS account? (Choose two.)

Accepted Answer

Add a bucket policy to the S3 bucket that grants access to the Glue service role from the other account.. To allow cross-account access, the S3 bucket policy must grant access to the Glue service role from the other account, and the Glue job must assume a role that has permissions to access the bucket. Option A (bucket policy) and Option D (IAM role in the second account) are correct. Option B (VPC peering) is not required for S3 access. Option C (cross-account Glue crawler) is not needed. Option E (same account) defeats cross-account.

Answer

Create a cross-account Glue crawler in the source account.

Answer

Set up VPC peering between the two accounts' VPCs.

Answer

Ensure both accounts are in the same AWS organization.

MLS-C01 Data Engineering Practice Test 16 — 15 Questions

Exhibit

MLS-C01 Data Engineering Practice Test 16 — 15 Questions

Exhibit