MLS-C01 Practice Test 49 — 15 Questions

Question 1

A data scientist needs to process a large volume of streaming data from IoT devices and store the results in Amazon S3 for further analysis. Which AWS service is most suitable for ingesting and processing this data in near real-time?

Accepted Answer

Amazon Kinesis Data Analytics. Amazon Kinesis Data Analytics is designed for real-time processing of streaming data. AWS Glue is for batch ETL, Amazon EMR for big data processing, and Amazon Redshift for data warehousing.

Answer

Amazon Redshift

Answer

AWS Glue

Answer

Amazon EMR

Question 2

Which TWO data formats are columnar and optimized for analytics queries in Amazon S3?

Accepted Answer

ORC. Parquet and ORC are columnar storage formats. JSON and CSV are row-oriented. Avro is row-oriented.

Answer

CSV

Answer

JSON

Answer

Avro

Question 3

A data engineer needs to schedule an AWS Glue ETL job to run every hour. Which service should be used to trigger the job?

Accepted Answer

Amazon CloudWatch Events (EventBridge). Option B is correct because Amazon CloudWatch Events (EventBridge) can trigger Glue jobs on a schedule. Option A is wrong because Lambda can trigger Glue but is not a scheduler; Option C is wrong because Data Pipeline is for complex workflows; Option D is wrong because Step Functions is for state machines, not simple scheduling.

Answer

AWS Lambda

Answer

AWS Step Functions

Answer

AWS Data Pipeline

Question 4

A data engineer is designing a data pipeline that transforms raw JSON files (each 50-200 KB) in Amazon S3 into Parquet format using AWS Glue. The pipeline must minimize data processing costs and handle a high volume of small files (millions per day). The engineer configures a Glue ETL job with Spark, but the job is slow and expensive due to overhead of reading many small files. Which optimization should the engineer implement to reduce cost and improve performance?

Accepted Answer

Use S3 object grouping or batch operations to combine small files before Glue processing.. Option C is correct. Using S3 object grouping (e.g., S3 batch operations or partitioning) to create larger files reduces the number of tasks and overhead in Spark. Option A is wrong because increasing DPUs increases cost without addressing the small file problem. Option B is wrong because converting to CSV is not more efficient than Parquet; Parquet is columnar and efficient. Option D is wrong because increasing worker type also increases cost without solving small file issue.

Answer

Increase the worker type to G.2X for more memory per worker.

Answer

Increase the number of DPUs allocated to the Glue job.

Answer

Change the output format from Parquet to CSV to reduce compression overhead.

Question 5

A data engineer is building a data pipeline using AWS Glue. The pipeline reads data from Amazon S3, transforms it, and writes it back to S3 in a different format. The engineer needs to handle schema evolution (new columns added over time). Which TWO features of AWS Glue can help manage schema evolution?

Accepted Answer

AWS Glue Data Catalog. Options B and D are correct. The Glue Data Catalog can store schema and update it as new columns are added. DynamicFrame in Glue ETL can handle schema changes automatically by allowing optional fields. Option A is wrong because AWS Lake Formation is for data lake security, not schema evolution. Option C is wrong because Amazon Athena is a query engine, not a schema evolution tool. Option E is wrong because S3 object tags are not for schema management.

Answer

AWS Lake Formation

Answer

Amazon Athena

Answer

Amazon S3 object tags

Question 6

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 7

An e-commerce company uses Amazon Redshift for analytics. The data engineering team needs to load daily sales data from an S3 bucket that receives new files every hour. The data must be loaded into Redshift with minimal impact on query performance during the day, and they need to handle late-arriving data (files that appear after the daily load). Which approach should they use?

Accepted Answer

Use a staging table to load data incrementally with a MERGE operation, and schedule a late-arriving data job to merge files that arrive after the daily load.. Option A is correct because staging tables allow incremental loads with upsert logic using a staging table, and a late-arriving data process can merge the additional records later without blocking queries. Option B (COPY with auto staging) Redshift Spectrum queries data in S3 without loading, which may be slower for frequent queries. Option C (Kinesis Firehose) is real-time streaming, suitable for near-real-time but not for batched daily loads with late data handling. Option D (Glue ETL with overwrite) overwrites data, losing late-arriving data.

Answer

Use AWS Glue ETL to copy the data from S3 to Redshift, overwriting the existing data each day.

Answer

Stream the data from S3 using Amazon Kinesis Firehose to load into Redshift continuously.

Answer

Use Amazon Redshift Spectrum to query data directly from S3 and create external tables.

Question 8

Refer to the exhibit. An IAM policy is attached to a data engineering team's role. The team needs to upload data to the 'confidential' prefix in the 'my-data-lake' bucket. However, they are receiving 'AccessDenied' errors. What is the likely cause?

Accepted Answer

The Deny statement with the condition explicitly denies access to the 'confidential' prefix for accounts other than 123456789012.. Option A is correct. The Deny statement denies all s3 actions on the confidential prefix for any principal account that is not 123456789012. If the team's role is from a different account (e.g., 111111111111), the Deny applies. Option B is incorrect because the Deny has a condition, but it still denies for other accounts. Option C is incorrect because the Allow statement does not include the confidential prefix explicitly; however, the Deny overrides the Allow. Option D is incorrect because the condition does not require the team to use a specific source IP.

Answer

The condition in the Deny statement requires the team to use a specific source IP address.

Answer

The Allow statement only grants GetObject and PutObject, but the team needs ListBucket.

Answer

The Allow statement's resource does not include the 'confidential' prefix.

Question 9

A company is using AWS Glue to run ETL jobs that transform data from Amazon S3 to Amazon Redshift. The jobs are failing intermittently with timeouts. What is the most likely cause?

Accepted Answer

The AWS Glue job does not have enough DPUs (Data Processing Units) allocated.. Insufficient DPU allocation can cause timeouts in Glue jobs. The other options are less likely: S3 bucket policies would cause permission errors, not timeouts; Redshift maintenance would affect all jobs; compression would improve performance.

Answer

The S3 bucket policy is too restrictive.

Answer

The Amazon Redshift cluster is in maintenance mode.

Answer

The source data is not compressed.

Question 10

Which THREE considerations are important when designing a data lake on Amazon S3?

Accepted Answer

Setting up S3 Lifecycle policies to transition data to colder storage. Organizing data with partitioning, encrypting data at rest, and setting up lifecycle policies are key. Single Availability Zone is not an S3 consideration (S3 is regional). Using Provisioned IOPS is for block storage.

Answer

Using Provisioned IOPS for S3

Answer

Using a single Availability Zone for data storage

Question 11

A company is using Amazon Athena to query a data lake in S3. Queries are slow and expensive. The data is stored as JSON. Which action will improve query performance and reduce cost?

Accepted Answer

Convert the data to Parquet format. Option C is correct because converting to columnar formats like Parquet reduces scan volume and improves performance. Option A is wrong because increasing workers is not applicable to Athena (serverless); Option B is wrong because compressing with gzip reduces size but still requires full scan; Option D is wrong because partitioning helps but columnar format is more impactful.

Answer

Compress the JSON files using gzip

Answer

Partition the data by date

Answer

Increase the number of Athena workers

Question 12

A company uses Amazon Kinesis Data Firehose to deliver streaming data to Amazon S3. The data contains personally identifiable information (PII) that must be redacted before storage. Which AWS service can be integrated with Kinesis Data Firehose to transform the data in real time?

Accepted Answer

AWS Lambda. Option A is correct. AWS Lambda can be used as a transformation function within a Kinesis Data Firehose delivery stream to redact PII. Option B is wrong because Amazon EMR is for big data processing, not inline transformation. Option C is wrong because Amazon Kinesis Data Analytics is for analyzing streams, not for transformation. Option D is wrong because Amazon Athena is a query service, not a transformation service.

Answer

Amazon Athena

Answer

Amazon Kinesis Data Analytics

Answer

Amazon EMR

Question 13

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 14

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 15

A data scientist needs to train a machine learning model using a large dataset (500 GB) stored in an S3 bucket. The training will be performed on a SageMaker notebook instance. The data scientist wants to minimize data transfer costs and reduce training time. Which data ingestion approach should the data engineer recommend?

Accepted Answer

Use the SageMaker SDK to directly read the data from S3 during training without copying it to the notebook.. Option C is correct because using S3 as a data source for SageMaker directly avoids copying data into the notebook instance, reducing transfer costs and time. Option A (copying to notebook EBS) incurs transfer costs and uses local storage. Option B (EFS) adds cost and complexity. Option D (RDS) is for structured data and incurs additional costs.

Answer

Copy the dataset to the notebook instance's attached EBS volume before training.

Answer

Load the dataset into an Amazon RDS database and query it from the notebook.

Answer

Mount the S3 bucket to the notebook instance using Amazon Elastic File System (EFS).