MLS-C01 Mock Exam 89 — 25 Questions

Question 1

A data scientist is analyzing a dataset with 100 features and 10,000 observations. The target variable is binary (0/1). Initial exploratory data analysis reveals that many features have missing values, high correlation with each other, and non-normal distributions. The data scientist wants to identify the most important features for predicting the target while reducing dimensionality. Which TWO actions should the data scientist take? (Choose two.)

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

Accepted Answer

Apply Principal Component Analysis (PCA) to reduce dimensionality.. B is correct because Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms correlated features into a set of linearly uncorrelated principal components, effectively handling high correlation and reducing the feature space. It does not require normality assumptions and can work with missing values after imputation, making it suitable for this dataset.

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Use chi-squared test to rank features by p-value.

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Perform a t-test for each feature to compare means between classes.

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Calculate Pearson correlation coefficients between features and target.

Question 2

A data scientist is using an IAM role with the policy shown in the exhibit to train a model in SageMaker. The training job fails with a permissions error. What is the missing permission?

Accepted Answer

iam:PassRole. The training job fails because SageMaker needs to assume the IAM role specified in the training job configuration to access resources like S3 buckets. The `iam:PassRole` permission is required to allow the SageMaker service to pass that role to the training job. Without it, SageMaker cannot assume the role and thus cannot perform actions such as reading training data from S3.

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sagemaker:InvokeEndpoint

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sagemaker:DescribeTrainingJob

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s3:ListBucket

Question 3

A data engineer needs to store streaming data from thousands of IoT devices for real-time analytics. Which AWS service is most suitable for ingesting and storing this data for subsequent processing by Amazon Kinesis Data Analytics?

Accepted Answer

Amazon Kinesis Data Streams. Amazon Kinesis Data Streams is the most suitable service for ingesting and storing streaming data from thousands of IoT devices because it is designed for real-time data ingestion, can handle high throughput from many sources, and integrates natively with Amazon Kinesis Data Analytics for real-time analytics without needing additional storage layers. Its shard-based architecture allows for scalable, durable storage of streaming records for up to 365 days, enabling immediate processing by Kinesis Data Analytics.

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Amazon S3

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Amazon RDS

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Amazon DynamoDB

Question 4

A data analyst wants to check for duplicate rows in a dataset stored in S3. Which AWS service can be used to run a SQL query to count duplicates without moving the data?

Accepted Answer

Amazon Athena. Amazon Athena is a serverless interactive query service that allows running standard SQL queries directly on data stored in Amazon S3, without needing to move the data. It can easily count duplicate rows using SQL GROUP BY and HAVING clauses. Option B (Amazon Redshift Spectrum) is wrong because although it can query data in S3, it requires an active Redshift cluster, which is unnecessary for this simple ad-hoc query. Option C (AWS Glue) is wrong because Glue is an ETL service for data preparation and cataloging, not a query engine. Option D (Amazon SageMaker Studio) is wrong because it is an integrated development environment for machine learning, not a SQL query service.

Answer

Amazon Redshift Spectrum

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Amazon SageMaker Studio

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AWS Glue

Question 5

A company is using Amazon SageMaker to train a time series forecasting model using the DeepAR algorithm. The training data contains multiple time series. The model is overfitting. Which action is LEAST likely to reduce overfitting?

Accepted Answer

Reduce the number of time series in the training set.. Reducing the number of time series in the training set reduces the diversity of training data, which typically increases overfitting rather than reducing it. DeepAR relies on learning patterns across multiple related time series to generalize well; fewer time series mean less shared statistical strength, making the model more likely to memorize noise in the remaining series.

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Decrease the number of layers in the neural network.

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Increase the dropout rate.

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Decrease the context length.

Question 6

A data scientist is training a binary classifier using logistic regression. The dataset has 100,000 samples and 500 features. After training, the model achieves 95% accuracy on the training set but only 70% on the test set. The data scientist suspects overfitting. Which technique would best reduce overfitting while preserving interpretability?

Accepted Answer

Apply L1 regularization (Lasso). L1 regularization (Lasso) adds a penalty equal to the absolute value of the magnitude of coefficients, which drives many feature weights to exactly zero. This performs automatic feature selection, reducing model complexity and overfitting while keeping the model as a simple linear logistic regression, thus preserving interpretability.

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Increase the maximum number of iterations

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Add polynomial features

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Use a random forest model instead

Question 7

A data scientist runs a SageMaker training job and receives the above error. The S3 bucket 'my-bucket' contains a folder 'data' with a file 'data.csv'. What is the MOST likely cause of the error?

Accepted Answer

The S3 URI should be s3://my-bucket/data/data.csv instead of s3://my-bucket/data. The error occurs because the SageMaker training job expects a specific S3 object URI (pointing to a file), not a prefix (pointing to a folder). When you specify `s3://my-bucket/data`, SageMaker interprets it as a prefix and attempts to list objects under that prefix, but the training channel requires a direct file reference. Using `s3://my-bucket/data/data.csv` provides the exact object path, allowing SageMaker to download the file correctly.

Answer

The instance type ml.m5.large does not have enough memory

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The VolumeSizeInGB is too small to download the data

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The S3 bucket and the training job are in different regions

Question 8

A team is deploying a real-time inference endpoint using Amazon SageMaker. The model is a large deep learning model that requires GPU for inference. The endpoint must handle variable traffic patterns with minimal latency. Which deployment strategy should the team use?

Accepted Answer

Use a SageMaker multi-model endpoint with GPU instance type.. B is correct because SageMaker multi-model endpoints (MMEs) allow multiple models to be hosted on a single GPU-backed endpoint, dynamically loading and unloading models from disk to GPU memory as needed. This reduces cost and cold-start latency compared to single-model endpoints, while still providing GPU acceleration for deep learning inference. MMEs are ideal for variable traffic patterns because they can scale horizontally and share GPU resources efficiently.

Answer

Deploy a single model endpoint with an auto-scaling policy.

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Deploy a serverless endpoint using SageMaker Serverless Inference.

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Use SageMaker Batch Transform to process requests in batches.

Question 9

Which THREE factors should be considered when selecting the appropriate algorithm for a regression problem? (Choose 3.)

Accepted Answer

The number of features relative to the number of samples. The ratio of features to samples directly impacts model complexity and overfitting risk. In high-dimensional settings (e.g., p >> n), algorithms like linear regression may fail due to singular covariance matrices, while regularized methods (Ridge, Lasso) or tree-based models become necessary. This is a core consideration in the bias-variance tradeoff for regression problems.

Answer

The time of day the training will occur

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The color of the data scientist's laptop

Question 10

A data scientist is training a deep learning model on Amazon SageMaker using a custom Docker container. The training job fails with an error 'OutOfMemoryError: CUDA out of memory'. The instance type is ml.p3.2xlarge (8 GB GPU memory). The model has 50 million parameters. What is the most likely cause and solution?

Accepted Answer

The model uses FP32 precision; enable mixed precision training. A model with 50 million parameters in FP32 precision requires approximately 200 MB per parameter (4 bytes each = 200 MB for 50M), plus additional memory for activations, gradients, and optimizer states, which can easily exceed the 8 GB GPU memory of ml.p3.2xlarge. Mixed precision training (FP16) halves the memory usage for tensors, reducing the overall footprint and often fitting the model within GPU limits. Option A (instance type) may solve the problem but is more expensive and unnecessary if mixed precision works. Option B (batch size) is a contributing factor but not the most likely root cause, as even a batch size of 1 may still cause OOM due to parameter storage. Option C (gradient checkpointing) trades compute for memory by recomputing activations, but does not address the primary issue of parameter storage in FP32. Therefore, enabling mixed precision is the most direct and cost-effective solution.

Answer

The instance type is insufficient; switch to ml.p3.8xlarge

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The batch size is too large; reduce batch size

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Enable gradient checkpointing to reduce memory

Question 11

Which THREE AWS services can be used together to build a serverless data pipeline that ingests streaming data, transforms it, and loads it into Amazon Redshift for analysis?

Accepted Answer

Amazon Kinesis Data Firehose. Amazon Kinesis Data Firehose (C) is the correct service because it is designed to reliably capture, transform, and load streaming data into Amazon Redshift with near-real-time latency. It can invoke AWS Lambda for on-the-fly data transformation (e.g., converting JSON to Parquet) and directly stream the processed records into Redshift via the Redshift COPY command, making it the central orchestration component for a serverless pipeline.

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Amazon EMR

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Amazon SQS

Question 12

A data engineer is exploring a dataset with 1 million rows and 50 features. They notice that some features have missing values. The 'Age' column has 5% missingness, and 'Income' has 20% missingness. The target variable is 'LoanDefault' (binary). The engineer wants to impute missing values. Which of the following strategies is most appropriate?

Accepted Answer

Impute missing 'Age' with median and 'Income' with median.. Median imputation is robust to outliers and appropriate for numerical features like Age and Income. Using median preserves the central tendency without being affected by extreme values. Option B is incorrect because mode is suitable for categorical features, not continuous numerical ones. Option C is incorrect because k-NN imputation, while possible, is more complex and typically used after simpler methods in EDA. Option D is incorrect because dropping rows with missing values would discard a significant portion of the dataset (up to 25% if missingness is independent), which is not ideal for initial analysis.

Answer

Impute missing 'Age' with mode and 'Income' with mode.

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Use a k-NN model to predict missing values.

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Drop all rows with missing values.

Question 13

A machine learning team is using SageMaker to train a custom TensorFlow model on a dataset that fits in memory. The training job is taking too long. The team wants to reduce training time without changing the model architecture. Which approach is most effective?

Accepted Answer

Switch the input mode from File to Pipe. Switching the input mode from File to Pipe is the most effective approach because it streams data directly from Amazon S3 to the training container, eliminating the need to download the entire dataset to the local storage before training begins. This reduces the I/O bottleneck and significantly cuts down the time spent on data loading, especially for datasets that fit in memory, as the model can start training almost immediately while data is being streamed.

Answer

Use SageMaker managed spot training

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Use Amazon EFS as the input data source instead of S3

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Use a larger instance type with more vCPUs

Question 14

Refer to the exhibit. An IAM policy is attached to a SageMaker notebook instance role. A data scientist is trying to train a model using the SageMaker built-in XGBoost algorithm with training data in 'my-bucket/training-data/' and expects output in 'my-bucket/output/'. The training job fails with an access denied error. What is the most likely missing permission?

Accepted Answer

iam:PassRole on the SageMaker execution role.. The training job fails with 'access denied' because the IAM policy attached to the SageMaker notebook instance role does not include the `iam:PassRole` permission. SageMaker requires this permission to pass the execution role specified in the `CreateTrainingJob` API call. Without it, the API call is denied. The other permissions (ECR, S3 list, DescribeTrainingJob) are either not needed at this stage or are already granted via the SageMaker service role, not the notebook role.

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ecr:GetAuthorizationToken on the ECR repository.

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s3:ListBucket on the S3 bucket.

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sagemaker:DescribeTrainingJob on the training job.

Question 15

A company is deploying a SageMaker model for real-time inference. The endpoint must be highly available and cost-effective. Which TWO actions should the company take? (Select TWO.)

Accepted Answer

Deploy the endpoint with at least two instances in different Availability Zones. Deploying a SageMaker endpoint with at least two instances in different Availability Zones (AZs) ensures high availability by eliminating a single point of failure. If one AZ goes down, traffic is automatically routed to the healthy instance in the other AZ, meeting the requirement for a highly available real-time inference endpoint.

Answer

Use managed spot training for inference

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Use GPU instances for all models even if not required

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Use a single large instance to handle peak load

Question 16

A data engineering team needs to build a data lake on Amazon S3 that will be queried by Amazon Athena and Amazon Redshift Spectrum. The data will be ingested from multiple sources in various formats (CSV, JSON, Parquet). Which partitioning strategy will provide the best query performance for date-range queries?

Accepted Answer

Partition by year, month, and day in a hierarchical structure.. Partitioning by year, month, and day in a hierarchical structure minimizes the amount of data scanned by Amazon Athena and Redshift Spectrum for date-range queries. Athena and Redshift Spectrum both charge per byte scanned, so reducing the scan size directly improves performance and reduces cost. A hierarchical partition structure (e.g., s3://bucket/year=2023/month=11/day=01/) allows the query engine to prune partitions at each level, efficiently skipping irrelevant directories for queries like WHERE date BETWEEN '2023-11-01' AND '2023-11-30'.

Answer

Partition by date with one partition per day in a flat structure.

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Do not partition; let Athena scan the entire dataset.

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Partition by source system first, then by date.

Question 17

During EDA, a data scientist notices that a feature has a high proportion of missing values (e.g., 70%). The feature is continuous and expected to be important based on domain knowledge. What is the best approach to handle this?

Accepted Answer

Create a binary indicator for missingness and impute the continuous values with the median.. It preserves the predictive signal from the feature while accounting for the pattern of missingness. Creating a binary indicator allows the model to learn whether missingness itself is informative, and median imputation is robust to outliers for a continuous feature. This approach avoids the bias of dropping the feature entirely and is more principled than arbitrary out-of-range imputation.

Answer

Remove the feature entirely to avoid bias.

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Impute missing values with -1 since it is out of range.

Answer

Drop all rows with missing values in that feature.

Question 18

A machine learning engineer needs to deploy a model that performs real-time inference with strict latency requirements of under 100 milliseconds. The model is a large ensemble of 10 deep learning models. Which SageMaker deployment strategy is MOST appropriate?

Accepted Answer

Use a SageMaker Inference Pipeline with serial inference within a single endpoint.. A SageMaker Inference Pipeline allows you to chain multiple containers (e.g., the 10 deep learning models) within a single endpoint, enabling serial inference with low latency. This approach avoids the network overhead of routing between separate endpoints and keeps the entire ensemble under the 100 ms threshold by processing sequentially in one HTTPS request.

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Use batch transform and cache predictions.

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Deploy each model as a separate endpoint and route traffic using Application Load Balancer.

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Use a multi-model endpoint to host all models.

Question 19

A data engineer needs to transfer 50 TB of historical data from an on-premises Hadoop cluster to Amazon S3. The company has a 1 Gbps internet connection and wants to complete the transfer within 5 days. What is the MOST cost-effective and reliable solution?

Accepted Answer

Use AWS Snowball Edge device to physically ship the data. AWS Snowball Edge is the most cost-effective and reliable solution because transferring 50 TB over a 1 Gbps internet connection would take approximately 5.5 days under ideal conditions (50 TB * 1024 GB/TB * 8 bits/byte / (1 Gbps * 86400 seconds/day) ≈ 4.74 days, but real-world overhead, congestion, and retransmissions push it beyond 5 days). Snowball Edge provides a physical appliance that can be shipped, avoiding network bandwidth limitations entirely, and is designed for large-scale data transfers where internet speeds are insufficient.

Answer

Use S3 multipart upload over the internet

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Set up AWS Direct Connect and transfer over the dedicated line

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Use S3 Transfer Acceleration to speed up the transfer

Question 20

A data engineer is querying the AWS Glue Data Catalog table shown in the exhibit. The engineer runs an Athena query: SELECT * FROM transactions WHERE year=2023. The query returns results quickly. However, a subsequent query: SELECT * FROM transactions WHERE amount > 100 takes a long time. What is the most likely reason for the performance difference?

Accepted Answer

The first query uses a partition column (year), allowing partition pruning, while the second query does not.. The table is partitioned by year and month. The first query filters on a partition column (year), so Athena prunes partitions and scans only the relevant data. The second query filters on a non-partition column (amount), so Athena scans all partitions, resulting in a longer execution time. Option A is incorrect because compression does not directly affect partition pruning; it reduces storage size but not scan time in this context. Option C is incorrect because the data format (Parquet) could help with columnar pruning, but the key difference here is partition pruning, not file format. Option D is incorrect because using SELECT * does not inherently cause slow performance; the lack of partition pruning is the main issue.

Answer

The data is compressed, and the first query benefits from compression.

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The data is stored in Parquet format, which is optimized for columnar access.

Answer

The second query is not optimized because it uses 'SELECT *'.