MLS-C01 Practice Test 23 — 15 Questions

Question 1

A machine learning team is using AWS Glue to prepare data for training. They notice that the ETL job takes a long time to process large datasets. Which change is most likely to improve performance?

Accepted Answer

Increase the number of DPUs for the Glue job.. Increasing the number of DPUs (Data Processing Units) for the AWS Glue job allocates more distributed computing resources, which allows the job to process data in parallel across more executors. This directly reduces the runtime for large datasets by improving the parallelism of Spark transformations and actions.

Answer

Decrease the number of workers in the Glue job.

Answer

Disable Spark shuffle operations.

Answer

Reduce the dataset size by sampling.

Question 2

A machine learning engineer needs to deploy a model that performs real-time fraud detection. The model must be highly available and scalable. Which AWS service should be used to host the model?

Accepted Answer

Amazon SageMaker real-time endpoint. Amazon SageMaker real-time endpoints are designed for low-latency, synchronous inference, making them ideal for real-time fraud detection. They automatically scale across multiple instances and Availability Zones, providing high availability and elasticity to handle variable traffic loads without manual intervention.

Answer

AWS Lambda

Answer

Amazon ECS with a custom container

Answer

Amazon SageMaker batch transform

Question 3

A data scientist needs to process a large dataset (100 TB) for training a machine learning model. The data is stored in Amazon S3. Which approach is most cost-effective and efficient for data processing?

Accepted Answer

Use Amazon EMR with Apache Spark.. Amazon EMR with Apache Spark is the most cost-effective and efficient approach for processing 100 TB of data stored in S3 because it provides a managed, scalable cluster that can process large datasets in parallel using in-memory computation. EMR integrates natively with S3 via the EMRFS connector, allowing data to be read directly from S3 without the need for intermediate storage, and it supports auto-scaling and spot instances to reduce costs. For petabyte-scale data, Spark's distributed processing engine outperforms single-node solutions and is more flexible than SQL-only or ETL-only services.

Answer

Use AWS Glue ETL jobs.

Answer

Use Amazon Athena to run SQL queries.

Answer

Use Amazon SageMaker Processing with a single large instance.

Question 4

A company is using SageMaker to train a large NLP model. The training job is taking too long due to high I/O wait time. The data is stored as CSV files in S3. Which optimization should the company implement to reduce I/O wait time?

Accepted Answer

Use SageMaker Pipe mode to stream data directly from S3. SageMaker Pipe mode streams data directly from S3 into the training algorithm without first writing it to the local disk, eliminating the I/O wait time caused by downloading and decompressing CSV files. This is the most effective optimization for high I/O wait during training because it bypasses the bottleneck of writing large datasets to the instance's local storage.

Answer

Convert CSV files to RecordIO format

Answer

Use SageMaker batch transform before training

Answer

Use SageMaker File mode with larger instance storage

Answer

Use SageMaker ShardedByS3Key data distribution

Question 5

A company is using Amazon SageMaker to train and deploy machine learning models. The data science team wants to track and compare model versions, hyperparameters, and metrics across multiple training jobs. Which TWO AWS services should they use together to achieve this? (Choose TWO.)

Accepted Answer

Amazon S3. Amazon S3 is correct because it serves as the central repository for storing model artifacts, training data, and output files from SageMaker training jobs. By default, SageMaker saves model artifacts and training results to S3, enabling version tracking and reproducibility across experiments.

Answer

Amazon RDS

Answer

Amazon CloudWatch Logs

Answer

AWS Glue

Question 6

A company is deploying a real-time inference endpoint using Amazon SageMaker. The model is a large deep learning model that requires GPU inference. The company wants to minimize latency and cost. Which instance type and deployment strategy should be used?

Accepted Answer

Use a real-time endpoint with a GPU instance and enable multi-model endpoints.. Using a real-time endpoint with a GPU instance and enabling multi-model endpoints allows the company to serve multiple models on a single GPU instance, reducing cost by sharing the GPU resource while maintaining low latency for real-time inference. Multi-model endpoints load and unload models on demand, minimizing idle GPU time and optimizing cost without sacrificing the low-latency requirement.

Answer

Use a serverless inference endpoint with a GPU instance.

Answer

Use a batch transform job with a GPU instance.

Answer

Use an asynchronous inference endpoint with a GPU instance.

Question 7

A company is using AWS Glue ETL jobs to process data stored in Amazon S3. The jobs currently run sequentially and take too long. The data engineer wants to reduce job duration without rewriting the code. Which action is most effective?

Accepted Answer

Increase the number of DPUs (Data Processing Units) for the job. Increasing the number of DPUs (Data Processing Units) for the AWS Glue ETL job directly allocates more distributed computing resources, enabling parallel execution of the job's stages. This reduces the overall runtime without requiring any code changes, as Glue automatically distributes the workload across the additional DPUs.

Answer

Change the underlying EC2 instance type to a compute-optimized instance

Answer

Convert the data from CSV to Parquet format

Answer

Enable job bookmarks to skip already processed data

Question 8

Which TWO AWS services can be used to transform data in transit before storing it in Amazon S3? (Choose TWO.)

Accepted Answer

AWS Glue. AWS Glue is correct because it provides a serverless data integration service that can transform data in transit using its built-in transformation jobs (e.g., PySpark scripts) before writing the results to Amazon S3. This allows you to clean, enrich, or reshape streaming or batch data as it moves through the pipeline.

Answer

Amazon Redshift Spectrum

Answer

AWS Data Pipeline

Answer

Amazon Athena

Question 9

A team wants to move data from an on-premises Oracle database to Amazon S3 for analytics. The pipeline must run daily and handle incremental updates. Which THREE services should they use together? (Choose three.)

Accepted Answer

Amazon S3. Amazon S3 is the correct destination for storing the data because it provides a scalable, durable, and cost-effective object storage solution ideal for analytics workloads. The pipeline requires daily incremental updates, and S3 integrates seamlessly with AWS DMS for continuous replication and AWS Glue for ETL processing, making it the central storage layer for the analytics pipeline.

Answer

Amazon SageMaker

Answer

Amazon Athena

Question 10

A company uses Amazon SageMaker to host a model for fraud detection. The model uses a custom XGBoost container. The endpoint receives about 100 requests per second, each with 50 features. The team notices that the model's predictions are occasionally incorrect for a subset of requests. Which approach should the team take to debug the issue?

Accepted Answer

Enable SageMaker Model Monitor to capture and analyze inference data.. SageMaker Model Monitor captures inference data (input features and predictions) and compares them against a baseline to detect data drift or quality issues. This allows the team to identify if incorrect predictions stem from distribution shifts or anomalous input patterns, which is the most direct debugging approach for sporadic prediction errors.

Answer

Use SageMaker Debugger to capture tensors during inference.

Answer

Scale the endpoint to more instances to reduce load.

Answer

Enable detailed CloudWatch Logs for the endpoint.

Question 11

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

Answer

Amazon Kinesis Data Streams

Answer

AWS Glue

Question 12

A data engineer needs to transfer 50 TB of historical data from an on-premises Hadoop cluster to Amazon S3. The on-premises network has a 1 Gbps connection to AWS. The transfer must be completed within 10 days. What is the MOST efficient approach?

Accepted Answer

Use AWS Snowball Edge to physically ship the data.. Transferring 50 TB over a 1 Gbps connection would take approximately 5.6 days under ideal conditions (50 TB × 8 bits/byte / 1 Gbps / 86400 seconds/day ≈ 4.63 days), but real-world factors like network congestion, TCP overhead, and protocol inefficiencies typically reduce throughput to 50-70% of line rate, pushing the transfer beyond the 10-day window. AWS Snowball Edge provides a physical shipping alternative that bypasses network limitations entirely, making it the most efficient and reliable method for this volume and deadline.

Answer

Use Amazon S3 Transfer Acceleration to speed up the upload.

Answer

Use AWS DataSync over the existing network connection.

Answer

Set up a VPN connection and use multi-part upload directly to S3.

Question 13

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 14

A data engineer needs to analyze large CSV files stored in Amazon S3 using SQL queries. The data is not frequently accessed, and cost is a primary concern. Which AWS service should be used to query the data directly in S3 without moving it?

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 is ideal for this use case because it charges only for the data scanned per query, making it cost-effective for infrequently accessed large datasets. Athena integrates with AWS Glue Data Catalog for schema management and supports common formats like CSV, JSON, Parquet, and ORC.

Answer

Amazon EMR

Answer

Amazon Redshift Spectrum

Answer

AWS Glue

Question 15

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.