MLS-C01 Mock Exam 87 — 25 Questions

Question 1

A data scientist uses Amazon SageMaker to train a model. The training dataset is 10 GB and stored in S3. The training job uses a ml.m5.large instance. The data must be available on the local file system during training. Which input mode should be used?

Accepted Answer

File input mode. File input mode is correct because it downloads the entire training dataset from S3 to the local file system of the ml.m5.large instance before training begins, ensuring the data is available locally as required. This mode is suitable for datasets up to 10 GB, as the instance's local storage (typically 8 GB for ml.m5.large) may be insufficient, but SageMaker uses the instance's Amazon EBS volume (up to 512 GB) for file input mode, making it viable.

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Local input mode

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Batch input mode

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Pipe input mode

Question 2

An organization stores sensitive customer data in S3. A data pipeline uses AWS Glue to transform the data and load it into Amazon Redshift. The security team requires that data be encrypted at rest in S3 and in transit between S3 and Glue, and between Glue and Redshift. Which configuration meets these requirements?

Accepted Answer

Enable S3 default encryption with SSE-KMS, use a VPC endpoint for S3, and configure Glue to use SSL for Redshift connection.. Option D is correct because it ensures encryption at rest in S3 via SSE-KMS, encrypts data in transit between S3 and Glue by using a VPC endpoint (which enforces HTTPS/TLS), and encrypts data in transit between Glue and Redshift by configuring SSL for the Redshift connection. SSE-KMS provides envelope encryption with a customer-managed key, while the VPC endpoint and SSL satisfy the in-transit encryption requirements.

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Use S3 client-side encryption, and use VPC Peering between Glue and Redshift.

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Use S3 default encryption with SSE-KMS, and use Network Load Balancer for Redshift.

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Enable S3 server-side encryption with SSE-S3, and use SSL for both Glue connections.

Question 3

A company uses Amazon Kinesis Data Streams to ingest real-time clickstream data from a website. The data is consumed by a Lambda function that writes records to an S3 bucket. Recently, the number of shards was increased from 2 to 4 to handle higher throughput. After the change, the Lambda function started processing records with increased latency and some records were being written out of order. What is the MOST likely cause?

Accepted Answer

The partition key used by the producer does not ensure that related records go to the same shard after resharding.. Option D is correct because after resharding from 2 to 4 shards, the mapping of partition keys to shards changes. If the producer does not use a partition key that ensures related records (e.g., same user session) are routed to the same shard, records that were previously ordered within a shard may now be split across multiple shards. Since the Lambda consumer processes shards independently, records from the same logical sequence can arrive out of order, and the increased shard count can also cause higher latency if the consumer is not properly parallelized.

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The S3 bucket is not configured with versioning, causing overwrites.

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The Lambda function is reading from the oldest sequence number, causing high IteratorAgeSeconds.

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The Lambda function’s reserved concurrency is too low for the increased shard count.

Question 4

A company uses Amazon Redshift as a data warehouse. They need to load 50 TB of clickstream data from S3 into Redshift daily. The data arrives in 5-minute intervals as gzipped CSV files. The target table has a sort key and a distribution key. The load must complete within 2 hours. Which approach is MOST efficient?

Accepted Answer

Use the COPY command with a manifest file and gzip compression.. The COPY command is the most efficient way to load large volumes of data into Amazon Redshift because it uses the cluster's massively parallel processing (MPP) architecture to read data directly from S3 in parallel across all nodes. With a manifest file, you can specify multiple gzipped CSV files, and the gzip compression reduces network I/O and storage overhead. This approach can easily load 50 TB within 2 hours, especially when the target table has a sort key and distribution key, as COPY automatically leverages these for optimal data distribution and sorting during the load.

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Use AWS Glue to transform the data and write to Redshift using JDBC.

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Use a staging table and then merge using a stored procedure.

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Use a series of INSERT statements from a Lambda function.

Question 5

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.. Option C is correct because 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.

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Increase the EBS volume size to 5 TB.

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Mount the S3 bucket as a file system using s3fs.

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Use SageMaker File input mode in the notebook.

Question 6

A company uses Amazon Kinesis Data Analytics for Apache Flink to process streaming data. The application reads from a Kinesis data stream and writes results to a sink. The application is failing with an 'OutOfMemoryError'. The application has parallelism set to 4 and uses 1 Kinesis Processing Unit (KPU). What is the MOST likely cause and solution?

Accepted Answer

The heap memory per operator is too low; increase parallelism to 8.. With parallelism set to 4 but only 1 KPU, each operator slot receives a fraction of the available heap memory, leading to an OutOfMemoryError. Increasing parallelism to 8 distributes the workload across more slots, but more importantly, it forces Kinesis Data Analytics to allocate additional KPUs (each KPU provides 4 GB of memory), thereby increasing the total heap memory available to the application.

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The application is using too many operators; reduce parallelism to 2.

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The checkpoint interval is too short; increase it to 5 minutes.

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The buffer timeout is too high; reduce it to 50 ms.

Question 7

A company uses AWS Glue ETL jobs to process data from multiple sources. The job fails with the error: 'An error occurred while calling o123.pyWriteDynamicFrame. Insufficient memory.' The job runs on a G.1X worker type with 10 workers. What should be changed to resolve this error?

Accepted Answer

Increase the number of workers to 20.. The error 'Insufficient memory' in AWS Glue ETL jobs typically indicates that the total memory across all executors is insufficient for the data being processed. Increasing the number of workers from 10 to 20 doubles the total memory and compute capacity available, allowing the job to handle larger datasets without running out of memory. This is the most direct and effective fix for a memory exhaustion error when using the G.1X worker type.

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Enable the Spark UI to monitor the job.

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Change the worker type to G.2X.

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Reduce the number of partitions in the DynamicFrame.

Question 8

A data engineer needs to transform large CSV files stored in S3 into Parquet format and load them into a data warehouse for analysis. The transformation must be cost-effective and serverless. Which AWS service should be used?

Accepted Answer

AWS Glue. AWS Glue is the correct choice because it provides a fully managed, serverless ETL service that can automatically convert CSV files from S3 into Parquet format using its built-in Spark engine. It is cost-effective as you only pay for the resources consumed during the job execution, and it integrates directly with data warehouses like Amazon Redshift for loading transformed data.

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Amazon Athena

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Amazon EMR with Spark

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AWS Data Pipeline

Question 9

A company uses Amazon Kinesis Data Firehose to deliver streaming data to an S3 bucket. The data is JSON and must be partitioned by year, month, and day. The delivery stream is configured with a buffer interval of 60 seconds and buffer size of 5 MB. The data producer sends about 1 MB per second. The data is arriving in S3 but the partitions are not being created as expected. What is the MOST likely reason?

Accepted Answer

The delivery stream does not have dynamic partitioning enabled with the appropriate custom prefix.. Option B is correct because Kinesis Data Firehose requires dynamic partitioning to be explicitly enabled and configured with a custom prefix (e.g., 'year=!{timestamp:yyyy}/month=!{timestamp:MM}/day=!{timestamp:dd}/') to automatically partition data by year, month, and day. Without this setting, Firehose writes all data to a single S3 prefix, ignoring the desired partition structure.

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The data is encrypted with AWS KMS and Firehose cannot write to encrypted buckets.

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The buffer interval is too short for the data volume, causing incomplete records.

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The S3 bucket has versioning enabled, which prevents partitioning.

Question 10

An ML team is building a recommendation system. The training data includes user-item interactions stored in Amazon DynamoDB. The team wants to export this data to S3 in Parquet format for use with Amazon SageMaker. The export should be incremental (only new or changed records) and run daily. Which approach meets these requirements with MINIMAL operational overhead?

Accepted Answer

Use DynamoDB Streams with AWS Lambda to write changes to S3 in Parquet format.. Option B is correct because DynamoDB Streams capture every change (insert, update, delete) in near real-time, and AWS Lambda can process these events to write only the changed records to S3 in Parquet format. This approach provides incremental, daily exports with minimal operational overhead, as it is fully serverless and requires no infrastructure management.

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Use the DynamoDB Export to S3 feature and schedule it daily with AWS Glue.

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Use a script that scans the DynamoDB table and filters by last updated timestamp.

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Set up an Amazon EMR cluster running Spark jobs to read DynamoDB and write to S3.

Question 11

Match each SageMaker optimization technique to its description.

Question 12

Drag and drop the steps to perform hyperparameter tuning using SageMaker Automatic Model Tuning in the correct order.

Question 13

Drag and drop the steps to set up cross-validation in a SageMaker training job using the built-in XGBoost algorithm in the correct order.

Question 14

Match each AWS security service to its function in ML.

Question 15

A company uses Amazon SageMaker to deploy a real-time inference endpoint for a regression model. The endpoint is experiencing high latency during spikes in traffic. The data scientist needs to reduce latency while maintaining cost efficiency. Which action should the data scientist take?

Accepted Answer

Enable automatic scaling for the endpoint. Option D is correct because enabling automatic scaling for the SageMaker endpoint allows the number of instances to dynamically adjust based on traffic patterns, reducing latency during spikes by adding capacity when needed and removing it during low traffic to maintain cost efficiency. Automatic scaling uses CloudWatch metrics (e.g., InvocationsPerInstance or CPUUtilization) to trigger scale-out and scale-in policies, ensuring the endpoint can handle bursts without over-provisioning.

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Use batch transform instead of real-time inference

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Use a larger instance type for the endpoint

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Deploy the model on a multi-model endpoint

Question 16

A data scientist is training a deep learning model on Amazon SageMaker using a large dataset stored in S3. The training job is taking too long due to high I/O latency waiting for data to be downloaded from S3. Which action would MOST effectively reduce the I/O latency?

Accepted Answer

Use Pipe mode for the training channel. Pipe mode streams data directly from S3 into the training algorithm without writing to disk, eliminating the I/O latency caused by downloading files to the local storage. This is the most effective solution because the bottleneck is data transfer from S3, and Pipe mode reduces it to near-zero latency by feeding data on the fly.

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Use File mode for the training channel

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Increase the number of training instances

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Use Amazon SageMaker Elastic Inference

Question 17

A data scientist is training a model and wants to monitor training progress. Which AWS service can be used to track metrics like loss and accuracy in real time?

Accepted Answer

Amazon CloudWatch. Amazon CloudWatch is the correct service because it provides real-time monitoring of metrics such as loss and accuracy during model training. When using SageMaker, training jobs automatically emit metrics to CloudWatch via the CloudWatch agent, allowing you to view logs and set alarms on metric thresholds in near real-time.

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Amazon SageMaker Ground Truth

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Amazon SageMaker Automatic Model Tuning

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AWS Glue

Question 18

Which THREE of the following are valid approaches for deploying a machine learning model to an Amazon SageMaker endpoint for real-time inference?

Accepted Answer

Use a SageMaker Inference Pipeline with multiple containers. Option A is correct because SageMaker Inference Pipelines allow you to chain multiple containers (e.g., preprocessing, prediction, postprocessing) into a single endpoint, enabling complex workflows for real-time inference. This is achieved by defining a sequence of Docker containers in the model definition, where each container's output is passed as input to the next, all within the same SageMaker endpoint.

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Use Amazon EMR to host the model

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Deploy the model as an AWS Lambda function

Question 19

Which TWO approaches are valid for handling missing categorical values in a dataset before training a machine learning model?

Accepted Answer

Impute missing values with the mode of the column. Option B is correct because the mode (most frequent value) is the only valid measure of central tendency for categorical data, as it identifies the most common category. Imputing with the mode preserves the distribution of categories and is a standard technique for handling missing categorical values in preprocessing pipelines like scikit-learn's SimpleImputer with strategy='most_frequent'.

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Remove all rows with missing values

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Impute missing values with the median of the column

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Impute missing values with the mean of the column

Question 20

A data scientist needs to evaluate a binary classification model's performance. Which metric is most appropriate when the cost of false positives is very high?

Accepted Answer

Precision. Precision is the most appropriate metric when the cost of false positives is very high because it measures the proportion of positive identifications that were actually correct. In binary classification, precision = TP / (TP + FP), so a high precision means very few false positives occur, directly minimizing the costly error type.

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Accuracy

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F1 score

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Recall