MLS-C01 Mock Exam 82 — 25 Questions

Question 1

A data scientist wants to explore a large dataset stored in Amazon S3 using SQL queries without moving the data. The dataset is in CSV format and is updated daily with new partitions. Which AWS service should be used to directly query the data in S3?

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

Accepted Answer

Amazon Athena. Amazon Athena is a serverless interactive query service that allows you to analyze data directly in Amazon S3 using standard SQL, without needing to load or transform the data. It supports CSV format and can automatically discover new partitions when used with Hive-style partitioning and the MSCK REPAIR TABLE command or by enabling partition projection. This makes it the ideal choice for directly querying a large, daily-updated CSV dataset stored in S3.

Answer

Amazon Redshift Spectrum

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Amazon EMR

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AWS Glue

Question 2

A data engineer needs to process streaming data from an IoT fleet and store the results in Amazon S3 for analysis. The solution must be serverless and handle data that arrives at irregular intervals. Which AWS service should be used to ingest the data?

Accepted Answer

AWS IoT Core. AWS IoT Core is the correct choice because it is a fully managed, serverless service designed specifically to ingest data from IoT devices at scale, handling irregular and high-frequency message arrivals via MQTT, HTTP, or LoRaWAN protocols. It can directly route data to Amazon S3 using IoT Rules, making it ideal for this streaming IoT fleet scenario without requiring any server management.

Answer

Amazon S3

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Amazon Simple Queue Service (SQS)

Answer

Amazon Kinesis Data Streams

Question 3

A company is designing a data pipeline to analyze customer behavior. The pipeline must handle real-time streaming data and batch data. The data must be stored in a data lake on Amazon S3 and also made available for interactive queries. Which THREE services should be combined to build this pipeline? (Choose THREE.)

Accepted Answer

Amazon Kinesis Data Streams. Amazon Kinesis Data Streams is correct because it is the primary AWS service for ingesting and processing real-time streaming data at scale. It can capture and store streaming data from sources like clickstreams or IoT devices, making it available for downstream consumers such as AWS Glue or Amazon Athena for analysis.

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Amazon Redshift

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Amazon DynamoDB Streams

Question 4

A company is building a data lake on Amazon S3. Raw data is ingested from multiple sources in different formats (CSV, JSON, Parquet). The data must be cataloged and made queryable using Amazon Athena. The data schema may evolve over time. Which approach minimizes manual effort and supports schema evolution?

Accepted Answer

Use AWS Glue Crawlers to automatically create and update the Glue Data Catalog. AWS Glue Crawlers automatically infer schema from data in S3, create and update the Glue Data Catalog tables, and handle schema evolution by detecting changes such as new columns or partitions. This minimizes manual effort because the crawler runs on a schedule or trigger, and the catalog is natively integrated with Athena for querying without any additional setup.

Answer

Use Athena only, without a catalog, by directly querying files

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Use Amazon EMR to process data and write to a Hive metastore

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Manually create tables in Athena using DDL statements

Question 5

A data engineer is investigating a slow Athena query on a partitioned table. The table is partitioned by year, month, and day, and the data is stored in S3 with the prefix pattern 'raw/YYYY/MM/DD/'. The engineer runs the above CLI command and sees that there are many small files. Which action would most improve query performance?

Accepted Answer

Use S3DistCp to coalesce files into fewer, larger files.. The core issue is that the Athena query is slow due to many small files, which increases the overhead of S3 LIST operations and task scheduling in the Presto/Trino engine underlying Athena. Coalescing these small files into fewer, larger files with S3DistCp reduces the number of S3 GET requests and minimizes the scheduling overhead, directly improving query throughput. This is a classic small-files problem, not a data format or partitioning issue.

Answer

Convert the data to columnar format like Parquet or ORC.

Answer

Increase the number of partitions in the Athena DDL.

Answer

Add more partitions to reduce the amount of data scanned per query.

Question 6

A data scientist wants to query a dataset stored in Amazon S3 using standard SQL without provisioning any servers. The dataset is in CSV format and is updated daily. Which AWS service should be used?

Accepted Answer

Amazon Athena. Amazon Athena is a serverless interactive query service that allows you to query data directly from Amazon S3 using standard SQL without provisioning any servers. It is ideal for querying CSV datasets that are updated daily because it supports schema-on-read, meaning you can define the table schema at query time without loading or transforming the data beforehand.

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Amazon Redshift

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Amazon RDS

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Amazon DynamoDB

Question 7

A company uses Kinesis Data Streams to ingest real-time sensor data. The data is consumed by a Lambda function that writes to DynamoDB. During peak hours, the Lambda function throws ProvisionedThroughputExceededException. The team wants to decouple the write operation and improve resilience. What should they do?

Accepted Answer

Use Kinesis Firehose as a consumer of the stream, with a Lambda transformation to write to DynamoDB, and enable error handling.. Kinesis Firehose can consume data from a Kinesis Data Stream and invoke a Lambda function for transformation before delivering to destinations like DynamoDB. By using Firehose with error handling, the team decouples the write operation from the Lambda consumer, allowing Firehose to buffer data and retry failed writes, which improves resilience against ProvisionedThroughputExceededException without losing data.

Answer

Increase the Lambda function's reserved concurrency and provision more DynamoDB write capacity.

Answer

Place the Lambda function's output into an Amazon SQS queue, and have a second Lambda function write to DynamoDB.

Answer

Use Kinesis Data Analytics to process the stream and write results directly to DynamoDB.

Question 8

Which TWO options are best practices for managing access to data stored in Amazon S3 for a data lake?

Accepted Answer

Use IAM policies to control user and role permissions. C is correct because IAM policies are the primary mechanism for controlling access to AWS services, including S3, for users and roles within an AWS account. They allow you to define fine-grained permissions based on identity, which is a best practice for managing access to a data lake. This aligns with the principle of least privilege and centralized access control.

Answer

Use S3 access control lists (ACLs) for granular permissions

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Enable default encryption with SSE-S3

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Generate pre-signed URLs for all data access

Question 9

A data scientist is building a model to predict customer churn. The dataset has 20 features, including categorical variables with high cardinality (e.g., ZIP code). The data scientist wants to use a linear model. Which feature engineering technique is MOST appropriate for the high-cardinality categorical features?

Accepted Answer

Target encoding. Target encoding is the most appropriate technique for high-cardinality categorical features when using a linear model because it replaces each category with the mean of the target variable for that category, creating a numeric feature that captures the relationship between the category and the target without exploding the feature space. One-hot encoding would create an unmanageable number of binary columns (e.g., thousands of ZIP codes), leading to the curse of dimensionality and making the linear model unstable or computationally infeasible.

Answer

One-hot encoding

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TF-IDF

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Standard scaling

Question 10

Which TWO services can be used to perform hyperparameter tuning in Amazon SageMaker? (Choose two.)

Accepted Answer

Amazon SageMaker Automatic Model Tuning. Amazon SageMaker Automatic Model Tuning (option A) is the native hyperparameter tuning service in SageMaker, which automatically searches for the best hyperparameter values by launching training jobs with different combinations and evaluating them against a specified objective metric. Amazon SageMaker Experiments (option B) is used to organize, track, and compare machine learning experiments, including hyperparameter tuning runs, by capturing parameters, metrics, and artifacts for reproducibility and analysis.

Answer

AWS Glue

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Amazon SageMaker Ground Truth

Answer

Amazon EMR

Question 11

A machine learning engineer needs to process a large dataset that does not fit on a single Amazon SageMaker notebook instance's EBS volume. The data is stored in S3. What is the MOST efficient way to access the data from the notebook?

Accepted Answer

Read the data directly from S3 using the boto3 library.. Reading data directly from S3 using the boto3 library is the most efficient approach for a dataset that exceeds the notebook instance's EBS volume capacity. Boto3 allows you to stream data in chunks or use S3 Select for server-side filtering, avoiding the need to download the entire dataset to local storage. This method leverages S3's high-throughput API and eliminates the bottleneck of writing to a local EBS volume, which is limited in size and I/O performance.

Answer

Increase the EBS volume size to 5 TB.

Answer

Mount the S3 bucket as a file system using s3fs.

Answer

Use SageMaker File input mode in the notebook.

Question 12

A data scientist is using Amazon SageMaker Ground Truth to create a labeled dataset for object detection. The team has limited budget and wants to minimize labeling costs while ensuring high-quality labels. Which approach is MOST cost-effective?

Accepted Answer

Use active learning to automatically label high-confidence data and send only uncertain data to a private workforce.. Active learning uses the model to automatically label high-confidence data points, while only sending low-confidence or uncertain data to a private workforce for human labeling. This significantly reduces the number of data points requiring manual labeling, thereby minimizing costs while still ensuring high-quality labels through expert review of challenging cases. Option A is incorrect because using only a private workforce for all data is expensive due to the high cost of domain experts. Option B is incorrect because using a public workforce with three workers per data point increases labeling costs without necessarily guaranteeing higher quality than a focused approach. Option D is incorrect because relying solely on automated labeling without human review can introduce errors and reduce label quality, especially for uncertain cases.

Answer

Use only a private workforce of domain experts to label all data.

Answer

Use a public workforce and have each data point labeled by three workers.

Answer

Use the built-in automated labeling feature without human review.

Question 13

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.. AWS Glue with Spark and dynamic frames is the most cost-effective approach because it is serverless, automatically scales workers based on file size, and is optimized for ETL on large CSV files (10-50 GB) in S3. Dynamic frames provide built-in transformations and schema inference, reducing development effort, while the ability to adjust the number of workers allows handling peak loads without over-provisioning. This minimizes idle compute costs compared to always-on clusters like EMR.

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.

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

Question 14

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 the most suitable service because it can process streaming data from IoT devices in near real-time using SQL or Apache Flink, and directly output the results to Amazon S3. It is designed for continuous, low-latency ingestion and analysis of data streams, making it ideal for this use case.

Answer

Amazon Redshift

Answer

AWS Glue

Answer

Amazon EMR

Question 15

A company runs a real-time fraud detection model on a SageMaker endpoint. The model is a TensorFlow neural network trained on transactional data. The endpoint uses a single ml.p3.2xlarge instance. Recently, the application’s latency has increased from 50ms to 500ms on average. The CloudWatch metrics show that CPU utilization is at 90%, GPU utilization is at 30%, and memory utilization is at 40%. The number of requests per second has remained stable. The ML team suspects the model is not fully utilizing the GPU. What action should the team take to reduce latency without changing the instance type?

Accepted Answer

Use SageMaker Neo to compile the model for the target instance. SageMaker Neo compiles the model to optimize inference for the target hardware, improving GPU utilization and reducing latency. Option A is incorrect because SageMaker Batch Transform is for offline inference, not real-time requests. Option B is incorrect because switching to a CPU-based instance (ml.c5.large) would not leverage the GPU and could increase latency. Option D is incorrect because adding more instances improves throughput, not per-request latency, and does not address GPU underutilization.

Answer

Switch to SageMaker Batch Transform to process requests in batches

Answer

Change the endpoint to a compute-optimized instance like ml.c5.large

Answer

Increase the number of instances behind the endpoint and use a load balancer

Question 16

A company uses Amazon Athena to query a data lake in Amazon S3. The data is partitioned by year, month, day, and hour. The team notices that queries are slow and expensive. The team wants to improve performance and reduce costs. Which THREE actions should the team take?

Accepted Answer

Ensure queries filter on partition columns (year, month, day, hour).. Athena charges based on the amount of data scanned per query. By filtering on partition columns (year, month, day, hour), Athena uses partition pruning to skip reading irrelevant S3 prefixes, drastically reducing the data scanned and thus lowering both cost and query latency.

Answer

Increase the number of partitions by adding a partition for minute.

Answer

Use CSV format with GZIP compression.

Question 17

A company is designing a data pipeline to process log files from multiple sources. The logs are written to Amazon S3 every hour. The data is then transformed using AWS Glue ETL jobs and loaded into Amazon Redshift for analysis. The company needs to ensure that the data is available for analysis within 30 minutes of being written to S3. Currently, the Glue job is triggered hourly, but the company wants to reduce the latency. Which solution should the company implement?

Accepted Answer

Use Amazon S3 event notifications to invoke an AWS Lambda function that starts the Glue job automatically. Configuring an S3 event notification to invoke AWS Lambda, which starts the Glue job, allows near-real-time processing within minutes. Option A is wrong because hourly triggers do not reduce latency. Option B is wrong because increasing the crawler frequency does not trigger ETL jobs. Option D is wrong because Redshift Spectrum does not transform data.

Answer

Increase the frequency of the Glue crawler to run every 5 minutes

Answer

Use Amazon Redshift Spectrum to query the data directly from S3 without transformation

Answer

Reduce the Glue job trigger frequency to every 15 minutes

Question 18

A company is migrating its on-premises Hadoop cluster to AWS. They have a large amount of historical data stored in HDFS. Which approach is the most efficient for transferring this data to Amazon S3?

Accepted Answer

Use AWS Snowball Edge devices.. AWS Snowball Edge is ideal for large data transfers when network bandwidth is limited. AWS DataSync is for network transfers, but slower for huge datasets. S3 Transfer Acceleration improves speed but still network. Direct Connect is network-based.

Answer

Use AWS Direct Connect.

Answer

Use AWS DataSync over the internet.

Answer

Use S3 Transfer Acceleration.

Question 19

An IAM policy attached to an AWS Glue job allows reading and writing to an S3 bucket and accessing Glue Data Catalog. The job fails with an access denied error when trying to create a table in the Data Catalog. What is the likely issue?

Accepted Answer

The policy does not include the glue:CreateTable action.. The policy allows GetTable and GetDatabase actions, but not CreateTable. The job needs glue:CreateTable permission. The S3 actions are sufficient. The error is specifically about creating a table.

Answer

The Glue Data Catalog is not enabled for the account.

Answer

The job does not have permission to write to the S3 bucket.

Answer

The S3 bucket is encrypted with a KMS key that the job cannot access.

Question 20

A data engineer needs to extract data from an Amazon RDS for MySQL database into Amazon S3 for further processing. The data volume is 2 TB and the job must run daily within a 1-hour window. Which AWS service is most suitable for this task?

Accepted Answer

AWS Glue. AWS Glue is the most suitable service because it provides a fully managed ETL (Extract, Transform, Load) capability that can efficiently extract 2 TB of data from Amazon RDS for MySQL and write it to Amazon S3. Glue can leverage JDBC connections to the RDS instance, scale horizontally with its dynamic worker allocation, and complete the job within a 1-hour window by using appropriate worker types (e.g., G.2X or G.8X) and partitioning strategies. Additionally, Glue integrates natively with the AWS Glue Data Catalog and can handle incremental or full-load extraction with minimal overhead.

Answer

Amazon Kinesis Data Firehose

Answer

AWS Database Migration Service (DMS)

Answer

Amazon Athena