MLS-C01 Practice Test 25 — 15 Questions

Question 1

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?

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

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

Answer

AWS Lambda

Question 2

A data scientist needs to run a one-time SQL query on a large dataset in Amazon S3. The dataset is stored in Parquet format and is about 500 GB. The query requires complex aggregations and joins. Which AWS service should be used to minimize cost and setup time?

Accepted Answer

Amazon Athena. Amazon Athena is the correct choice because it is a serverless query service that allows you to run SQL directly on data stored in S3 without provisioning any infrastructure. For a one-time query on 500 GB of Parquet data, Athena minimizes cost (pay-per-query, no idle cluster costs) and setup time (no cluster creation or data loading). Its ability to handle complex aggregations and joins on columnar formats like Parquet makes it ideal for this ad-hoc use case.

Answer

Amazon Redshift

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Amazon RDS for MySQL

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Amazon EMR with Spark SQL

Question 3

A company is using Amazon SageMaker to deploy a model for real-time inference. The model is updated frequently. Which deployment strategy allows for zero-downtime updates and easy rollback?

Accepted Answer

Blue/green deployment using endpoint updates. SageMaker's blue/green deployment (using endpoint updates with production variants) allows traffic shifting and rollback. A/B testing is for testing variants, not zero-downtime updates by itself. Canary deployment is a type of blue/green but not a separate AWS feature. Multi-model endpoints are for hosting multiple models.

Answer

Canary deployment

Answer

A/B testing with production variants

Answer

Multi-model endpoint

Question 4

A data scientist is training a deep learning model on Amazon SageMaker and notices that training is taking much longer than expected. The training job uses a single GPU instance. The model is a large transformer with millions of parameters. Which change would most likely reduce training time?

Accepted Answer

Use SageMaker's distributed data parallelism with multiple GPU instances. Using data parallelism with multiple GPU instances can significantly reduce training time for large models by distributing the workload across multiple GPUs. Model parallelism is also possible but data parallelism is more common and easier to implement.

Answer

Reduce the batch size to fit in memory

Answer

Use a smaller instance type

Answer

Switch to a CPU instance

Question 5

A machine learning team is building a multi-class image classifier using a pre-trained ResNet-50 model in Amazon SageMaker. The dataset has 10 classes but is highly imbalanced, with one class representing 80% of the samples. The team wants to improve model performance on the minority classes. Which TWO of the following approaches are most likely to help? (Select TWO.)

Accepted Answer

Oversample the minority classes in the training data.. Oversampling the minority classes (Option A) directly addresses class imbalance by replicating samples from underrepresented classes, giving the model more exposure to them during training. This is a standard data-level technique that helps the ResNet-50 model learn discriminative features for minority classes without altering the loss function or model architecture.

Answer

Reduce the batch size to increase the frequency of weight updates.

Answer

Increase the number of layers in the model.

Answer

Switch to a focal loss function.

Question 6

A data scientist is using a decision tree algorithm for a classification task. The tree is very deep and achieves 100% accuracy on the training set but performs poorly on the test set. Which technique should the data scientist use to improve generalization?

Accepted Answer

Prune the decision tree.. A deep decision tree that achieves 100% training accuracy but poor test accuracy is overfitting the training data. Pruning the tree removes branches that have little statistical power, reducing complexity and improving generalization to unseen data.

Answer

Add more features to the dataset.

Answer

Reduce the number of training samples.

Answer

Increase the maximum depth of the tree.

Question 7

A data engineer is designing a data pipeline that ingests 500 GB of data daily from an on-premises Oracle database to Amazon S3. The pipeline must minimize data loss and support change data capture (CDC). Which combination of services should they use?

Accepted Answer

AWS Database Migration Service (DMS) with ongoing replication. AWS DMS with ongoing replication is the correct choice because it provides continuous change data capture (CDC) from an Oracle source database using Oracle LogMiner or binary reader technology, replicating transactions in near real-time to Amazon S3. This minimizes data loss by capturing incremental changes without requiring batch snapshots, and it supports the 500 GB daily volume efficiently with parallel tasks and task tuning.

Answer

AWS Data Pipeline with SQL query

Answer

Amazon Kinesis Data Streams with a custom Oracle CDC connector

Answer

AWS Glue ETL jobs running on a schedule

Question 8

A team is using Amazon SageMaker to train a model. The training job repeatedly fails with a 'ResourceLimitExceeded' error. Which action should the team take to resolve this issue?

Accepted Answer

Request a service limit increase for SageMaker resources.. The 'ResourceLimitExceeded' error in Amazon SageMaker indicates that the AWS account has reached a service quota for SageMaker resources, such as the number of concurrent training jobs, total instance count, or specific instance types. The correct action is to request a service limit increase via the AWS Service Quotas console or by contacting AWS Support, as this directly addresses the quota cap causing the failure.

Answer

Reduce the size of the training dataset.

Answer

Switch to using Spot Instances.

Answer

Use a different instance type with less memory.

Question 9

A machine learning engineer is deploying a model using AWS Lambda for real-time inference. The model is a scikit-learn RandomForestClassifier with 100 trees, serialized as a pickle file of 150 MB. The Lambda function has 3 GB memory allocated. However, the inference requests are timing out after 30 seconds. What is the most likely cause?

Accepted Answer

The model is loaded from S3 on every invocation, causing high latency.. The default behavior of loading a model from S3 on every Lambda invocation introduces significant latency. Each invocation must download the 150 MB pickle file from S3 over the network, deserialize it, and then run inference, which easily exceeds the 30-second timeout. The model should be loaded once outside the handler (in global scope) and reused across invocations to avoid this overhead.

Answer

scikit-learn is not compatible with AWS Lambda.

Answer

The Lambda function does not have enough memory to load the model.

Answer

The Lambda function timeout is set too low; increase it to 5 minutes.

Question 10

A machine learning engineer is using Amazon SageMaker to train a model. The training job fails with an out-of-memory error. The training data size is 10 GB and the instance is ml.m5.xlarge (16 GB memory). Which change is MOST likely to resolve the issue without increasing cost?

Accepted Answer

Reduce the batch size in the training script.. Many algorithms allow you to set a batch size, and reducing it lowers memory usage. Option B is wrong because changing to GPU may not help and could increase cost. Option C is wrong because increasing instance type increases cost. Option D is wrong because decreasing the dataset size may lose information.

Answer

Switch to a GPU instance like p3.2xlarge.

Answer

Use a larger instance type like ml.m5.4xlarge.

Answer

Decrease the training dataset size.

Question 11

A machine learning engineer is deploying a model on SageMaker and needs to ensure that the endpoint can handle a sudden spike in traffic. The engineer expects traffic to increase by 10x during a promotional event. Which scaling strategy should be used?

Accepted Answer

Use scheduled scaling to add instances before the event, combined with dynamic scaling for the remaining duration.. The correct answer. Scheduled scaling allows you to add instances before the expected traffic spike, ensuring capacity is ready when needed. Combined with dynamic scaling (e.g., based on CPU utilization or request count), you can handle unexpected additional load during the event. Option A is wrong because a single large instance is a single point of failure and may not provide sufficient throughput for a 10x spike. Option B is wrong because manual scaling requires human intervention and may not react fast enough. Option C is wrong because relying solely on dynamic scaling may not scale up quickly enough for a sudden 10x increase, as there is a lag in metrics and scaling actions.

Answer

Use a single large instance type instead of multiple smaller instances.

Answer

Manually increase the instance count before the event.

Answer

Use only dynamic scaling based on the average latency metric.

Question 12

Which TWO AWS services can be used to transform data in a streaming fashion without using a persistent cluster? (Choose 2.)

Accepted Answer

AWS Lambda. (Lambda) and Option D (Kinesis Data Analytics) are serverless streaming transformation services. Option A (Glue) is serverless but not low-latency streaming. Option B (EMR) requires a persistent cluster. Option E (Data Pipeline) is for batch.

Answer

AWS Glue

Answer

Amazon EMR

Answer

AWS Data Pipeline

Question 13

Which TWO of the following are valid ways to reduce query costs in Amazon Athena? (Choose 2)

Accepted Answer

Partition the data in S3. (partitioning data) and Option D (using columnar formats like Parquet) are correct because both reduce the amount of data scanned by Athena queries, directly lowering costs. Option A (UNLOAD) exports results but does not reduce query costs. Option C (increasing query timeout) does not affect data scanned. Option E (encryption at rest) does not reduce costs.

Answer

Use UNLOAD to export query results to S3

Answer

Increase the query timeout limit

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Enable encryption at rest on S3

Question 14

A company wants to analyze historical data stored in Amazon S3 using Amazon Athena. The data is in CSV format and is partitioned by date. Which action will provide the best query performance and cost optimization?

Accepted Answer

Convert the data to Parquet format and use the existing partition structure. Converting data to Parquet and partitioning provides the best performance and cost savings because Athena can use predicate pushdown and column pruning, scanning less data. Option A (using Glue to gzip compress) still uses CSV which requires full scan. Option B (S3 event notification to warm up Athena) is not relevant because Athena caches results but doesn't need warming. Option C (only partitioning) helps but CSV is still row-based and less efficient than Parquet.

Answer

Use AWS Glue to compress the CSV files with gzip

Answer

Create an S3 event notification to trigger a Lambda function that warms up Athena

Answer

Keep CSV format but ensure partitions are in the format year=YYYY/month=MM/day=DD

Question 15

A company is using Amazon Kinesis Data Firehose to deliver streaming data to an S3 bucket. The data is JSON and must be transformed into Parquet format before delivery. Which approach should the data engineer use?

Accepted Answer

Configure Kinesis Data Firehose to convert the record format to Parquet using a schema from AWS Glue Data Catalog. Amazon Kinesis Data Firehose can directly convert incoming JSON records to Parquet format by referencing a schema stored in the AWS Glue Data Catalog. This is a built-in feature of Firehose that does not require additional services for the conversion. Option A is wrong because Kinesis Data Analytics is for real-time analytics, not format conversion. Option C is wrong because while Lambda can transform data, using it for Parquet conversion adds latency and complexity; Firehose's native conversion is simpler. Option D is wrong because an AWS Glue ETL job is for batch processing, not real-time streaming transformation.

Answer

Send the data to Amazon Kinesis Data Analytics to convert to Parquet

Answer

Use an AWS Lambda function to transform JSON to Parquet and write to S3

Answer

Use an AWS Glue ETL job to read from Firehose and write Parquet to S3