MLA-C01 Timed Practice Test 3 — 10 Questions

Question 1

A company wants to use a pre-trained NLP model from SageMaker JumpStart for sentiment analysis. Which step is required to make predictions?

Accepted Answer

Deploy the model to an endpoint. D is correct because SageMaker JumpStart provides pre-trained models that are ready for inference without additional training. To make predictions, you must deploy the model to a SageMaker endpoint, which creates a hosted inference endpoint that can accept input data and return sentiment analysis results.

Answer

Label the dataset for fine-tuning

Answer

Train the model from scratch on the company's data

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Convert the model to ONNX format

Question 2

A data scientist is training a binary classification model using imbalanced data where the positive class is only 1% of the dataset. The scientist wants to maximize the recall for the positive class while maintaining reasonable precision. Which evaluation metric is most appropriate to tune during model selection?

Accepted Answer

F1 score. The F1 score is the harmonic mean of precision and recall, making it ideal for imbalanced datasets where the positive class is only 1%. By tuning the F1 score, the data scientist directly balances the trade-off between maximizing recall (capturing true positives) and maintaining reasonable precision (avoiding false positives), which aligns with the stated goal.

Answer

Log loss

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Area under the ROC curve (AUC)

Answer

Accuracy

Question 3

An ML engineer needs to split a dataset into training, validation, and test sets. The dataset has a time-based column that should not be leaked. Which split method is most appropriate?

Accepted Answer

Temporal split based on date. Option B is correct because a temporal split ensures that the time-based column is not leaked by preserving the chronological order of the data. This method uses the date column to assign earlier records to the training set and later records to the validation and test sets, preventing future information from influencing the model during training.

Answer

Stratified split based on target

Answer

Random split with 70/20/10

Answer

K-fold cross-validation

Question 4

A team is building a regression model on a dataset with missing values in multiple features. They decide to use a k-Nearest Neighbors (k-NN) imputer. The dataset has 100,000 rows and 50 features. Which step should the team take to ensure the imputation is efficient and accurate?

Accepted Answer

Standardize the features before applying k-NN imputation. Standardizing features before applying k-NN imputation is critical because k-NN relies on distance calculations (e.g., Euclidean distance). If features are on different scales (e.g., one feature ranges 0–1 and another 0–100,000), the distance metric will be dominated by the larger-scale feature, leading to biased neighbor selection and inaccurate imputation. Standardization (e.g., z-score scaling) ensures each feature contributes equally to the distance computation, improving both efficiency and accuracy.

Answer

Set k=1 to minimize bias

Answer

Use all 100,000 rows to find neighbors for each missing value

Answer

Use only the feature with missing values to find neighbors

Question 5

A company wants to deploy a machine learning model that was trained on-premises using TensorFlow. The model is a TensorFlow SavedModel. The company uses AWS and wants to minimize operational overhead. Which deployment option meets these requirements?

Accepted Answer

Deploy the model using Amazon SageMaker with a TensorFlow inference container.. Amazon SageMaker provides a fully managed TensorFlow inference container that directly supports TensorFlow SavedModel format, enabling deployment without any custom infrastructure management. This minimizes operational overhead compared to self-managed options like ECS or Lambda, as SageMaker handles scaling, load balancing, and model updates automatically.

Answer

Deploy the model on Amazon ECS using a custom Docker image.

Answer

Deploy the model as an AWS Lambda function with the TensorFlow runtime.

Answer

Deploy the model using Amazon SageMaker Studio.

Question 6

A SageMaker Processing job fails with 'Access Denied' when listing objects in an S3 bucket, despite the IAM policy shown in the exhibit. What is the most likely cause?

Accepted Answer

The policy lacks `s3:ListBucket` permission.. The error 'Access Denied' when listing objects in an S3 bucket indicates that the IAM role used by the SageMaker Processing job lacks the `s3:ListBucket` permission. This permission is required for the `ListObjectsV2` API call, which is necessary to enumerate objects in the bucket. Even if the role has `s3:GetObject` and `s3:PutObject` permissions, without `s3:ListBucket`, the job cannot list the bucket contents and will fail with an access denied error.

Answer

The role does not have a trust relationship with SageMaker.

Answer

The bucket policy denies the access.

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The bucket is in a different region.

Question 7

Refer to the exhibit. A Glue job runs successfully the first time but on subsequent runs with new data (added to the same input location), the job does not process the new data. What is the most likely cause?

Accepted Answer

The job bookmark is enabled, causing the job to skip already processed data. Option C is correct because when a Glue job bookmark is enabled, the job tracks previously processed data using a persistent state stored in a DynamoDB table. On subsequent runs, the bookmark mechanism skips files that have already been processed, so new data added to the same input location is ignored unless the bookmark is reset or the job is configured to process new partitions. This explains why the first run succeeds but later runs do not process new data.

Answer

The script location is incorrect

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The MaxRetries is set to 0, so the job does not retry on failure

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The WorkerType is Standard, which does not support incremental processing

Question 8

A company ingests daily log data into an S3 bucket. They need to update the existing ML training dataset with new data without reprocessing the entire history. Which two strategies should they adopt? (Choose two.)

Accepted Answer

Use AWS Glue to incrementally process new partitions. AWS Glue can perform incremental processing by using job bookmarks to track previously processed data and only process new partitions or files. This avoids reprocessing the entire historical dataset, making it efficient for updating ML training datasets with daily log data.

Answer

Store all data in a single large file and use append operations

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Manually copy new files to the same S3 bucket

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Overwrite the entire existing dataset with the new data

Question 9

Refer to the exhibit. The Glue job reads a CSV file and attempts to write to a Parquet table. What is the most likely cause of this error?

Accepted Answer

The 'price' column contains non-numeric values in some rows. Option C is correct because the error message indicates a 'NumberFormatException' when parsing the 'price' column, which occurs when Spark attempts to convert a string value to a numeric type. Since the Glue job's schema inference likely detected 'price' as a numeric column based on the majority of rows, any row containing a non-numeric value (e.g., 'N/A', 'null', or a currency symbol) will cause this parsing failure during the write to Parquet.

Answer

The 'price' column is missing from some rows

Answer

The schema inference incorrectly detected the column as String

Answer

The CSV file is compressed and not properly decompressed

Question 10

A data scientist is training a binary classifier on a highly imbalanced dataset (1:100 class ratio). The dataset contains 500,000 rows and 30 features. The data is stored in S3 in Parquet format. The data scientist wants to use SageMaker's built-in XGBoost algorithm. Which data preparation technique should the data scientist apply to best address the class imbalance without causing data leakage?

Accepted Answer

Use the scale_pos_weight parameter in XGBoost to assign higher weight to the minority class.. The scale_pos_weight parameter in XGBoost directly adjusts the loss function to penalize misclassifications of the minority class more heavily, effectively handling class imbalance without modifying the dataset. This avoids data leakage because the weighting is applied during training only, not during preprocessing, and does not involve any synthetic data generation or resampling that could inadvertently expose test information.

Answer

Undersample the majority class to create a balanced dataset, then split.

Answer

Oversample the minority class using SMOTE on the entire dataset before splitting into train/validation sets.

Answer

Randomly oversample the minority class by duplicating rows, then perform stratified train/test split.