MLS-C01 Data Engineering Practice Test 19 — 15 Questions

Question 1

A company uses Amazon Redshift for its data warehouse. The data engineering team notices that queries are slow and wants to improve performance without changing the schema. Which action is most likely to improve query performance?

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

Accepted Answer

Increase the number of nodes in the cluster.. Increasing the number of nodes in an Amazon Redshift cluster distributes data and query processing across more compute resources, which directly improves parallel execution and reduces query execution time. This is the most effective way to boost performance without altering the schema, as it scales the cluster's CPU, memory, and I/O capacity.

Answer

Decrease the number of nodes to reduce network overhead.

Answer

Disable compression on all tables to reduce CPU overhead.

Answer

Change the distribution style from AUTO to EVEN.

Question 2

A data pipeline uses AWS Glue to transform data from Amazon RDS to Amazon S3. The team wants to ensure that only new or updated records are processed in each run, minimizing cost and time. Which AWS Glue feature should be used?

Accepted Answer

Enable Glue Job Bookmarks.. Glue Job Bookmarks track processed data and persist state between job runs, enabling incremental processing of new or updated records from a source like Amazon RDS. This minimizes cost and time by avoiding full table scans and reprocessing unchanged data.

Answer

Use Glue triggers to run the job on a schedule.

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Use Glue partition pruning to filter data.

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Use Glue crawlers to detect new data.

Question 3

A company is using Amazon SageMaker to train machine learning models. The training data is stored in Amazon S3, but the data includes personally identifiable information (PII) that must be anonymized before training. What is the most efficient way to anonymize the data?

Accepted Answer

Use an AWS Glue ETL job to read from S3, apply anonymization, and write to another S3 bucket.. AWS Glue ETL jobs are purpose-built for serverless data transformation at scale, making them the most efficient choice for anonymizing PII in S3 before training. Glue can read directly from S3, apply built-in or custom anonymization transforms (e.g., masking, hashing) using PySpark or Scala, and write the cleaned data to a separate S3 bucket without provisioning any infrastructure. This approach decouples the data preparation from SageMaker, avoids unnecessary compute costs during training, and scales automatically with data volume.

Answer

Use Amazon Athena to query the data and apply anonymization functions.

Answer

Use Amazon Redshift Spectrum to query and anonymize data in S3.

Answer

Use a SageMaker Processing job to read from S3 and apply anonymization.

Question 4

A data engineer needs to move 50 TB of historical data from an on-premises Hadoop cluster to Amazon S3. The network bandwidth is limited to 100 Mbps. Which AWS service should be used to transfer the data most efficiently?

Accepted Answer

AWS Snowball Edge device to physically ship the data.. Given 50 TB of data and a 100 Mbps network link, the theoretical minimum transfer time over the network is over 46 days (50 TB * 8 / 100 Mbps ≈ 4,000,000 seconds ≈ 46.3 days), not accounting for protocol overhead, retransmissions, or contention. AWS Snowball Edge is a physical appliance that bypasses the network bottleneck entirely, allowing you to copy data locally and ship it to AWS, making it the most efficient option for this volume over a constrained link.

Answer

Amazon S3 Transfer Acceleration to speed up the transfer.

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AWS Direct Connect to establish a dedicated network connection.

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AWS Site-to-Site VPN to connect and copy data.

Question 5

A team is building a data lake on Amazon S3 and using AWS Glue to catalog data. They notice that Glue crawlers are taking too long to update the catalog for a large dataset with millions of small files. Which approach will MOST improve crawler performance?

Accepted Answer

Consolidate the small files into larger files (e.g., 100 MB each).. AWS Glue crawlers incur significant overhead when processing millions of small files because each file requires a separate read, schema inference, and metadata write operation. Consolidating small files into larger files (e.g., 100 MB each) reduces the total number of objects that the crawler must scan, dramatically decreasing the time spent on file-level operations and improving overall throughput.

Answer

Increase the frequency of the crawler runs.

Answer

Partition the data by date in S3.

Answer

Use a custom classifier to parse the data.

Question 6

A data engineer needs to run a one-time ETL job to transform 500 GB of data from Amazon RDS to Amazon S3. The job should be cost-effective and require minimal infrastructure management. Which AWS service should be used?

Accepted Answer

AWS Glue. AWS Glue is the correct choice because it is a fully managed, serverless ETL service designed for one-time or scheduled data transformation jobs. It automatically provisions and scales the underlying Spark environment, requires no infrastructure management, and charges only for the resources consumed during job execution, making it highly cost-effective for a 500 GB ETL workload from RDS to S3.

Answer

Amazon EMR

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Amazon Athena

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AWS Data Pipeline

Question 7

A company wants to build a data lake on Amazon S3. The data lake should support both batch and real-time data ingestion. Which AWS services should be used for data ingestion? (Choose TWO.)

Accepted Answer

AWS Glue. AWS Glue is correct because it provides a managed ETL service that can handle batch data ingestion into a data lake on Amazon S3. It can be scheduled for periodic batch loads or triggered by events, making it suitable for batch ingestion workflows. Amazon Kinesis Data Firehose is correct because it is a fully managed service for loading streaming data into S3 in near real-time, supporting real-time ingestion with automatic buffering and compression.

Answer

Amazon Redshift

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Amazon Athena

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Amazon SQS

Question 8

A data engineer wants to stream clickstream data from a web application to Amazon S3 for near-real-time analytics. Which AWS service should be used to ingest and buffer the data before landing in S3?

Accepted Answer

Amazon Kinesis Data Firehose. Amazon Kinesis Data Firehose is the correct choice because it is a fully managed service designed to ingest streaming data, buffer it, and reliably deliver it to destinations like Amazon S3 with near-real-time latency (typically 60 seconds). It handles automatic scaling, data transformation, and compression, making it ideal for clickstream data landing directly into S3 for analytics.

Answer

Amazon AppFlow

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Amazon Kinesis Data Streams

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AWS Glue

Question 9

A machine learning team needs to process a large dataset stored in Amazon S3 using Apache Spark. They want to minimize cost and avoid managing infrastructure. Which AWS service should they use?

Accepted Answer

AWS Glue. AWS Glue is a fully managed, serverless Spark environment that eliminates infrastructure management. It directly meets the requirement to process large datasets in S3 with Apache Spark while minimizing cost, as you only pay for resources consumed during job execution. Glue also integrates natively with S3 and the broader AWS ecosystem, making it the optimal choice for this use case.

Answer

Amazon Athena

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Amazon EMR

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Amazon SageMaker

Question 10

A data scientist needs to query a dataset stored as Parquet files in Amazon S3 using standard SQL without managing any infrastructure. Which service should they use?

Accepted Answer

Amazon Athena. Amazon Athena is a serverless interactive query service that allows you to query data stored in Amazon S3 using standard SQL without any infrastructure to manage. It directly reads Parquet files from S3 and executes queries using Presto under the hood, making it the ideal choice for this use case.

Answer

Amazon QuickSight

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AWS Glue

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Amazon Redshift

Question 11

A team wants to build a data pipeline that processes incoming JSON files from an S3 bucket and loads them into a Redshift table. The pipeline must handle schema evolution and data validation. Which combination of services would be MOST appropriate?

Accepted Answer

Amazon S3 + AWS Glue + Amazon Redshift. AWS Glue provides built-in schema discovery and evolution capabilities via its crawlers and the Data Catalog, which automatically detect and adapt to changes in JSON schemas. Combined with Glue ETL jobs for data validation and transformation, it seamlessly loads processed data into Amazon Redshift, making it the most appropriate choice for handling schema evolution and validation in this pipeline.

Answer

Amazon S3 + Amazon SQS + Amazon Redshift

Answer

Amazon S3 + AWS Data Pipeline + Amazon Redshift

Answer

Amazon S3 + AWS Lambda + Amazon Redshift

Question 12

A company uses Amazon EMR with Spark to process data daily. The job reads from S3 and writes to S3. Recently, the job started failing with 'S3AccessDenied' errors. The IAM role used by EMR has not changed. What is the MOST likely cause?

Accepted Answer

The S3 bucket policy was updated to deny access to the EMR role. The IAM role used by EMR has not changed, but the S3 bucket policy can be updated independently to deny access to that specific role. An explicit deny in a bucket policy overrides any allow in the IAM policy, causing the 'S3AccessDenied' error even though the role itself remains unchanged. This is the most likely cause because the job was previously working and only the bucket policy could have been modified without touching the EMR configuration.

Answer

The EMR cluster's security group blocks outbound traffic

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The S3 bucket was deleted and recreated

Answer

The EMR service role was rotated

Question 13

A company is designing a data pipeline to ingest data from multiple sources into an Amazon S3 data lake. The data must be encrypted at rest and in transit. Which TWO actions should be taken to meet these requirements?

Accepted Answer

Enable Server-Side Encryption on the S3 bucket. Enabling Server-Side Encryption (SSE-S3 or SSE-KMS) on the S3 bucket automatically encrypts data at rest when written to disk, using AES-256 encryption. This meets the requirement for encryption at rest without any client-side changes.

Answer

Enable S3 Transfer Acceleration

Answer

Use client-side encryption before uploading

Answer

Use S3 VPC Endpoint

Question 14

A company wants to analyze streaming data from IoT devices in near-real-time. They need to store raw data in Amazon S3 and also run SQL queries on the streaming data. Which TWO services should they use?

Accepted Answer

Amazon Kinesis Data Analytics. Amazon Kinesis Data Analytics is correct because it enables running SQL queries on streaming data in near-real-time, allowing the company to analyze IoT data as it arrives without needing to store it first. It integrates directly with Kinesis Data Streams or Firehose to process data streams using standard SQL, making it ideal for real-time analytics on streaming data.

Answer

AWS Glue

Answer

Amazon Kinesis Data Streams

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AWS Lambda

Question 15

A data engineer runs the CLI command to download an object from S3. The bucket owner is 123456789012, and the engineer's IAM user has s3:GetObject permission on the bucket. The object was uploaded by a different AWS account. What is the MOST likely reason for the AccessDenied error?

Accepted Answer

The object is owned by a different AWS account, and the bucket owner has not been granted access. When an object is uploaded to S3 by a different AWS account, the object is owned by the uploading account, not the bucket owner. By default, the bucket owner does not have access to objects uploaded by other accounts, even if the bucket owner has a policy granting s3:GetObject to their IAM users. The engineer's IAM user has permission on the bucket, but the object itself is not owned by the bucket owner, so the bucket owner cannot delegate access to it unless the object owner explicitly grants read access via an object ACL or a bucket policy that the object owner accepts.

Answer

The --expected-bucket-owner parameter is incorrect

Answer

The bucket policy denies access to the engineer's IAM user

Answer

The IAM policy does not allow s3:GetObject for that specific key