MLS-C01 Practice Test 17 — 15 Questions

Question 1

During EDA, a data scientist plots the distribution of a numeric feature and observes that it is right-skewed. The feature will be used as input to a linear model. Which transformation should the data scientist apply?

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

Accepted Answer

Log transformation. A right-skewed distribution indicates that the feature has a long tail on the right, which can violate the linear model assumption of normally distributed errors. The log transformation compresses the high values and expands the low values, making the distribution more symmetric and stabilizing variance, which improves linear model performance.

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Square transformation

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One-hot encoding

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Standardization (Z-score)

Question 2

A data engineer runs the AWS CLI command above to inspect an object in S3. The engineer wants to query this metadata (kafka-offset) using Amazon Athena to track processing progress. How can the engineer make this metadata available for Athena queries without modifying the existing data pipeline?

Accepted Answer

Use AWS Glue to create a table that includes the metadata as a column by running an ETL job.. AWS Glue ETL jobs can read the S3 object's user-defined metadata (e.g., 'kafka-offset') and write it as a column in a new or transformed dataset, which Athena can then query. This approach does not modify the existing data pipeline, as the original objects remain unchanged; the metadata is extracted and stored in a queryable format (e.g., Parquet or CSV) in a separate location. Glue's ability to access S3 object metadata via the `getObjectMetadata` API during ETL processing makes this a clean, pipeline-agnostic solution.

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Use S3 object tags instead of metadata and query the tags using Athena.

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Use an AWS Lambda function to copy the metadata into the object's content as a new line.

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Use Amazon Athena to query the object metadata directly by referencing the metadata field.

Question 3

A team is using Amazon SageMaker Autopilot to automatically build models. The dataset has 50 features and 1 million rows. After training, Autopilot generates multiple candidates. The team wants to deploy the model with the highest accuracy. What is the best practice to select and deploy the model?

Accepted Answer

Deploy the candidate with the highest objective metric value from the Autopilot leaderboard. Amazon SageMaker Autopilot automatically generates a leaderboard ranking candidate models by their objective metric (e.g., accuracy, F1, AUC). The highest-ranked candidate represents the best-performing model based on the validation data, and deploying it directly is the recommended best practice. Autopilot handles preprocessing, algorithm selection, and hyperparameter tuning internally, so manual intervention is unnecessary.

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Deploy all candidates behind a multi-model endpoint and route traffic based on request features

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Select the model with the highest validation accuracy after performing additional hyperparameter tuning

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Manually review each candidate's architecture and select the one with the simplest design

Question 4

A data scientist is using SageMaker to train a TensorFlow model. The training script uses tf.data.Dataset to load data from S3. Training is slow because of I/O bottleneck. Which change should the data scientist make to improve I/O performance?

Accepted Answer

Use Pipe input mode for the training channel.. Pipe input mode streams data directly from S3 into the training algorithm without writing to disk, eliminating the I/O bottleneck caused by downloading entire files. This is particularly effective with tf.data.Dataset, as the pipeline can consume data incrementally, reducing latency and improving throughput for large datasets.

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Enable EBS optimization on the training instance.

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Use SageMaker local mode for training.

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Convert the dataset to RecordIO format.

Question 5

A company is building a recommendation system using Amazon SageMaker. The training data includes user-item interactions stored in a DataFrame with over 100 million rows. The data scientist wants to perform feature engineering, including one-hot encoding of categorical features with high cardinality. Which approach is MOST cost-effective and scalable?

Accepted Answer

Use SageMaker Processing with a Spark container to distribute the encoding job.. SageMaker Processing with a Spark container allows distributed execution of one-hot encoding on high-cardinality categorical features across a managed cluster, scaling horizontally to handle over 100 million rows without manual infrastructure management. This approach is cost-effective as you pay only for the processing time, and it integrates natively with SageMaker for seamless data pipeline orchestration.

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Use Amazon EMR with Spark and store the processed data in HDFS.

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Use a SageMaker notebook instance with scikit-learn to perform the encoding in memory.

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Use AWS Glue ETL jobs to perform the encoding and store the result in S3.

Question 6

Match each SageMaker built-in algorithm to its primary use case.

Accepted Answer

Linear Learner: Used for regression and binary/multiclass classification.. Correct matches: Linear Learner → regression/classification, Object Detection → image object detection. Common confusions: swapping XGBoost (gradient boosting) with BlazingText (text vectors).

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XGBoost: Used for word2vec and text classification.

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BlazingText: Gradient boosting algorithm for classification and regression.

Question 7

Match each SageMaker built-in metric to its meaning.

Accepted Answer

accuracy: The ratio of correctly predicted observations to total observations. Accuracy measures overall correctness, precision measures positive predictive value, and recall measures sensitivity. Common confusions occur when precision and recall definitions are swapped.

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accuracy: The ratio of true positives to the sum of true positives and false positives

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precision: The ratio of true positives to the sum of true positives and false negatives

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recall: The ratio of correctly predicted observations to total observations

Question 8

A company uses Amazon DynamoDB as the primary data store for a real-time recommendation engine. The data engineering team needs to export a daily snapshot of the DynamoDB table to S3 for offline analytics. The table is large (10 TB) and has a high read/write throughput. Which method will export the data with the least impact on the production workload?

Accepted Answer

Use the DynamoDB export to S3 feature available in the AWS Console or CLI.. The native DynamoDB export to S3 feature uses the table's internal backup mechanism (point-in-time recovery) to export data without consuming any read capacity units (RCUs) from the production table. This ensures zero impact on the live workload, even for a 10 TB table with high throughput.

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Use AWS Data Pipeline to export the DynamoDB table to S3.

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Use DynamoDB Scan API with parallel scans to export data to S3.

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Use AWS Glue ETL job with a DynamoDB connection to export data.

Question 9

A company is building a data lake on Amazon S3. They need to enforce encryption at rest for all objects. Which combination of actions will achieve this? (Assume the bucket is versioned.)

Accepted Answer

Enable S3 default encryption and set a bucket policy to deny PutObject without encryption headers. Combining S3 default encryption with a bucket policy that denies PutObject requests lacking encryption headers ensures that every object stored in the bucket is encrypted at rest, even if the PutObject call does not include encryption parameters. Default encryption alone can be overridden by a client that explicitly sets encryption headers, but the bucket policy enforces encryption for all uploads, closing that loophole. This dual approach guarantees compliance with encryption-at-rest requirements for a versioned bucket.

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Use AWS KMS with automatic key rotation

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Enable S3 default encryption only

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Enable S3 Block Public Access

Question 10

A machine learning engineer is training a regression model to predict house prices using Amazon SageMaker. The dataset contains 10,000 samples and 50 numerical features. After training a linear regression model, the engineer notices that the training loss is low, but the validation loss is high. The engineer suspects overfitting. The dataset is already normalized. Which action should the engineer take to reduce overfitting?

Accepted Answer

Add L2 regularization (weight decay) to the loss function.. L2 regularization (weight decay) adds a penalty term to the loss function that discourages large weight values, effectively reducing model complexity and overfitting. Option A (increasing learning rate) can cause the model to diverge or overshoot minima, and does not directly prevent overfitting. Option B (PCA) reduces the number of features, which can help with overfitting but may discard important information; regularization is a more targeted approach for linear models. Option D (decreasing mini-batch size) introduces more noise into gradient estimates, which can sometimes act as a regularizer but is less effective and reliable than L2 regularization for this scenario.

Answer

Increase the learning rate to speed up convergence.

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Reduce the number of features using PCA.

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Decrease the mini-batch size during training.

Question 11

A company uses AWS Lake Formation to manage permissions on a data lake stored in Amazon S3. A data analyst tries to query a table using Amazon Athena but receives an 'Access Denied' error. The analyst has SELECT permission on the table in Lake Formation. What is the most likely cause?

Accepted Answer

The S3 bucket is not registered with Lake Formation. When Lake Formation manages permissions on a data lake, it requires that the underlying S3 bucket be registered with Lake Formation. If the bucket is not registered, Lake Formation cannot enforce its fine-grained access controls, and Athena will fail with an 'Access Denied' error even if the analyst has SELECT permission on the table in Lake Formation. Registering the bucket allows Lake Formation to integrate with S3 and apply its permission model.

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The S3 bucket is encrypted with a KMS key that the analyst does not have access to

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The table does not have any partitions defined

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The IAM role used by Athena does not have lakeformation:GetDataAccess permission

Question 12

A data engineer is troubleshooting an AWS Glue job that reads from and writes to the S3 bucket 'data-lake-bucket'. The job fails when trying to write to the 'sensitive/' prefix. The IAM policy attached to the Glue job's IAM role is shown in the exhibit. What is the MOST likely reason for the failure?

Accepted Answer

The IAM role has an explicit deny for s3:PutObject on the 'sensitive/' prefix. Even though the first statement allows s3:PutObject on the entire bucket, the second statement explicitly denies s3:PutObject on the 'sensitive/' prefix. Explicit deny overrides any allow. Option A is wrong because the policy allows GetObject. Option C is wrong because the policy covers the bucket. Option D is wrong because there is a deny statement.

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The IAM role does not have permission to read objects from the bucket

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The IAM policy does not specify the bucket resource correctly

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The IAM policy lacks a required condition for encryption

Question 13

A company is using Amazon Kinesis Data Streams to ingest clickstream data. The data is consumed by a fleet of EC2 instances running a custom consumer application. The consumer is falling behind and the shard iterator age is increasing. Which TWO actions should the data engineer take to improve consumer performance? (Choose TWO.)

Accepted Answer

Increase the number of shards in the stream. Increasing the number of shards in the stream directly increases the total read capacity of the Kinesis Data Stream. Each shard provides a fixed read throughput of 2 MB/s (or 5 read transactions per second), so adding shards allows the consumer fleet to parallelize processing across more data partitions, reducing the backlog and shard iterator age.

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Decrease the data retention period

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Use an AWS Lambda function to process the data

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Switch to the Kinesis Client Library (KCL)

Question 14

A company is using Amazon Kinesis Data Streams to ingest real-time clickstream data. The data is consumed by a Lambda function that writes to an S3 bucket. Recently, the Lambda function started failing with 'ProvisionedThroughputExceededException' errors. What is the MOST likely cause?

Accepted Answer

The Kinesis stream has too few shards for the data volume.. The 'ProvisionedThroughputExceededException' error in Amazon Kinesis Data Streams indicates that the data ingestion rate exceeds the write capacity of the stream's shards. Each shard supports up to 1 MB/s or 1,000 records/s for writes. If the clickstream data volume surpasses this limit, the Lambda function, which reads from the stream, will encounter this exception. Increasing the number of shards scales the write capacity to match the data volume.

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The data retention period of the stream is too short.

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The S3 bucket has insufficient write capacity.

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The Lambda function's reserved concurrency is set too high.

Question 15

An IAM policy is attached to a SageMaker execution role. A data scientist tries to create a training job using a custom algorithm stored in an ECR repository. The training job fails with an 'AccessDenied' error when pulling the Docker image from ECR. What is the missing permission?

Accepted Answer

ecr:GetDownloadUrlForLayer and ecr:BatchGetImage on the ECR repository. When SageMaker pulls a custom Docker image from ECR during training job creation, the execution role needs permissions to download the image layers. The required actions are ecr:GetDownloadUrlForLayer (to generate pre-signed URLs for each layer) and ecr:BatchGetImage (to retrieve image metadata and layer manifests). Without these, the 'AccessDenied' error occurs because SageMaker cannot authenticate or fetch the container image from the ECR repository.

Answer

ecr:PutImage on the ECR repository

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s3:GetObject on the ECR repository

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sagemaker:CreateTrainingJob on the ECR resource