MLS-C01 Practice Test 10 — 10 Questions

Question 1

A Glue job fails with an AccessDenied error when trying to write to the S3 bucket my-data-lake. The IAM policy attached to the job role is shown in the exhibit. What is the MOST likely reason for the failure?

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

Accepted Answer

The job role does not have permissions to decrypt the KMS key used for server-side encryption. The policy allows s3:PutObject on the bucket, so write access seems granted. However, if the bucket is encrypted with SSE-KMS, the job also needs kms:Decrypt and kms:GenerateDataKey permissions. The policy does not include KMS actions. The bucket policy might also deny, but the most common issue is KMS encryption.

Answer

The s3:ListBucket action is missing on the bucket level

Answer

The s3:PutObject action is not sufficient; the job needs s3:PutObjectAcl

Answer

The resource ARN for s3:PutObject should include a specific prefix

Question 2

A data scientist runs the AWS CLI command shown in the exhibit to list objects larger than 100 KB in an S3 bucket. The data scientist wants to understand the size distribution of these files. What is the most significant limitation of this approach for EDA?

Accepted Answer

The command may return incomplete results if there are more than 1000 objects.. The AWS CLI `list-objects` command returns a maximum of 1000 objects by default. If the bucket contains more than 1000 objects larger than 100 KB, the command will only return the first 1000, leading to incomplete results for EDA. Option A is incorrect because the command uses `> 100000` which excludes objects exactly 100 KB, but that is not the most significant limitation. Option C is incorrect because the query syntax is valid. Option D is incorrect because the command does return the object keys (file names) as well as sizes.

Answer

The command only returns objects larger than 100 KB, not equal to.

Answer

The command uses the wrong query syntax and will fail.

Answer

The command does not return the file names, only sizes.

Question 3

During exploratory data analysis, a data scientist notices that a feature has a highly skewed distribution. Which transformation is most likely to make the distribution approximately normal?

Accepted Answer

Log transformation. Log transformation is commonly used to reduce right skewness and make the distribution approximately normal. Option B (min-max scaling) is incorrect because it does not change the shape of the distribution. Option C (one-hot encoding) is incorrect because it is used for categorical variables, not for transforming continuous skewed data. Option D (standardization) is incorrect because it does not change the shape of the distribution.

Answer

Min-max scaling

Answer

One-hot encoding

Answer

Standardization (z-score)

Question 4

A data scientist is performing EDA on a dataset stored in Amazon S3 using Amazon Athena. The dataset is partitioned by date, and each partition contains CSV files. The data scientist notices that some queries return zero rows for partitions that should have data. Which THREE steps should the data scientist take to troubleshoot? (Choose 3.)

Accepted Answer

Verify that the CSV files exist in the S3 bucket for the specific partition.. Verifying that the CSV files actually exist in the S3 bucket for the specific partition confirms whether data is present. Option B is correct because MSCK REPAIR TABLE adds new partitions to the Glue Data Catalog that may not have been registered automatically. Option D is correct because incorrect data types in the query's WHERE clause can cause filters to exclude rows, resulting in zero returned rows. Option C is incorrect because converting to Parquet is not a troubleshooting step for this issue—it is an optimization. Option E is incorrect because adding a LIMIT clause does not force partition discovery; it only limits the number of rows returned.

Answer

Convert the CSV files to Parquet format.

Answer

Re-run the query with a LIMIT clause to force partition discovery.

Question 5

Refer to the exhibit. A data scientist is assigned an IAM policy to deploy a SageMaker model. When the scientist tries to create an endpoint, the action fails with an authorization error. What is the missing permission?

Accepted Answer

iam:PassRole. The error occurs because the IAM policy does not include the `iam:PassRole` permission. When creating a SageMaker endpoint, the service must assume an IAM role to access resources (e.g., S3 buckets, CloudWatch). The `iam:PassRole` permission allows the user to pass that role to SageMaker. The other actions listed are either for inference (`InvokeEndpoint`), listing endpoints (`ListEndpoints`), or updating endpoints (`UpdateEndpoint`), which are not relevant to the creation process. Therefore, the missing permission is `iam:PassRole` (Option A).

Answer

sagemaker:ListEndpoints

Answer

sagemaker:InvokeEndpoint

Answer

sagemaker:UpdateEndpoint

Question 6

A data scientist is performing EDA on a dataset with 1 million rows and 50 features. The dataset includes a column 'user_id' with unique identifiers, a column 'event_date' with timestamps, and other columns. Which TWO actions should the data scientist take to understand data quality issues?

Accepted Answer

Analyze missing value patterns across columns. Analyzing missing value patterns (A) is a fundamental EDA step to identify data quality issues such as incomplete records. Checking for duplicate rows based on 'user_id' and 'event_date' (B) helps ensure data integrity, as duplicates can skew analysis. Option C (dropping 'user_id') is premature; identifier columns can be useful for deduplication and merging. Option D (PCA) is a dimensionality reduction technique used later, not for initial data quality checks. Option E (training a model) is part of modeling, not EDA.

Answer

Drop the 'user_id' column to reduce dimensionality

Answer

Use PCA to reduce dimensions and visualize

Answer

Train a random forest model to identify feature importance

Question 7

A data scientist is using SageMaker to train a deep learning model with a large dataset stored in S3. The training is taking a long time. Which action would most likely reduce training time without sacrificing accuracy?

Accepted Answer

Use SageMaker Pipe Input mode. SageMaker Pipe Input mode streams training data directly from S3 into the algorithm without first downloading it to the local EBS volume. This eliminates the I/O bottleneck caused by large dataset downloads, significantly reducing training time while preserving accuracy because the model sees the same data.

Answer

Increase the batch size

Answer

Use a smaller instance type

Answer

Reduce the number of epochs

Question 8

A data scientist is performing exploratory data analysis on a dataset stored in Amazon S3 using Amazon SageMaker Studio. The dataset has missing values in several columns. Which approach is the MOST efficient way to handle missing values within SageMaker Studio?

Accepted Answer

Use SageMaker Data Wrangler to impute missing values with mean, median, or mode.. SageMaker Data Wrangler provides a visual interface to handle missing values efficiently within SageMaker Studio, allowing imputation with mean, median, or mode without writing custom code. Option A is inefficient because it requires moving data out of SageMaker. Option C uses an external service (AWS Glue) which adds complexity and overhead. Option D, while possible, is less efficient than using Data Wrangler's built-in capabilities.

Answer

Run a Jupyter notebook on a local machine to clean the data and upload back to S3.

Answer

Use AWS Glue to run a find-and-replace operation.

Answer

Write a custom Python script using pandas to drop rows with missing values.

Question 9

A data scientist needs to store and version machine learning models, along with metadata such as hyperparameters and metrics. Which AWS service is designed for this purpose?

Accepted Answer

Amazon SageMaker Model Registry. Amazon SageMaker Model Registry is a purpose-built service for cataloging, versioning, and managing machine learning models along with their metadata such as hyperparameters and metrics. It provides a central repository to track model versions, lineage, and approval status. Option A (S3 with versioning) is object storage that can store model artifacts but lacks metadata management and versioning capabilities for ML models. Option C (DynamoDB) is a NoSQL database, not designed for ML model management. Option D (ECR) is for storing container images, not ML models directly.

Answer

Amazon S3 with versioning enabled

Answer

Amazon DynamoDB

Answer

Amazon Elastic Container Registry (ECR)

Question 10

A data scientist needs to deploy a model with a custom inference container. Which THREE requirements must the container meet for SageMaker hosting?

Accepted Answer

Implement a /ping endpoint for health checks. SageMaker requires custom inference containers to implement the /ping endpoint for health checks (C), serve on port 8080 (D), and implement the /invocations endpoint for predictions (E). Option A is for training containers, not inference. Option B is unnecessary; the container can load the model using any method.

Answer

Provide a training script at /opt/ml/input/data

Answer

Use the SageMaker Python SDK to load the model