MLA-C01 Practice Test 30 — 15 Questions

Question 1

A machine learning engineer is building a pipeline to preprocess text data for a sentiment analysis model. The data consists of customer reviews. The engineer wants to convert the text into numerical features while preserving the semantic meaning of words. Which technique should be used?

Accepted Answer

Word embeddings (e.g., Word2Vec or GloVe). Word embeddings (like Word2Vec or GloVe) are dense vector representations that capture semantic relationships between words based on their context in a large corpus. For sentiment analysis, preserving semantic meaning (e.g., 'good' and 'excellent' having similar vectors) is critical, and embeddings directly encode this, unlike sparse or count-based methods.

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One-hot encoding of each word

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Bag-of-words with TF-IDF

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Hashing vectorizer

Question 2

A data scientist is using SageMaker Data Wrangler to prepare a large dataset. The data contains duplicate rows, which could bias the model. Which built-in step in Data Wrangler can automatically detect and remove duplicates?

Accepted Answer

Handle Duplicates transform in Data Wrangler. The Handle Duplicates transform is a built-in step in SageMaker Data Wrangler specifically designed to detect and remove duplicate rows from a dataset. It provides configurable options such as selecting a subset of columns for duplicate detection and choosing whether to keep the first or last occurrence, directly addressing the bias risk from duplicate rows in ML training data.

Answer

Amazon QuickSight duplicate detection

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AWS Glue Studio FindDuplicates transform

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Amazon DataZone catalog

Question 3

A data engineer is using AWS Glue to prepare a dataset for ML. The engineer wants to split the dataset into training and testing sets while preserving the distribution of the target variable. Which TWO methods achieve this goal? (Select TWO)

Accepted Answer

Use the `train_test_split` function from scikit-learn in a SageMaker notebook. Option B is correct because the `train_test_split` function from scikit-learn supports the `stratify` parameter, which preserves the distribution of the target variable when splitting a dataset into training and testing sets. This is a standard, reliable method for stratified splitting in Python-based ML workflows, and it can be used directly in a SageMaker notebook.

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Use Amazon Athena to create views with random sampling

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Use AWS Glue's built-in random split transform

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Use Amazon SageMaker's built-in SplitType parameter in a Processing Job

Question 4

A data scientist runs the exhibit AWS Glue ETL job. The job fails with a Spark stage failure error. What is the most likely cause?

Accepted Answer

The data type mapping in ApplyMapping is incorrect; "value" column contains non-numeric strings that cannot be cast to double.. The Spark stage failure error in an AWS Glue ETL job is most likely caused by a data type mismatch during the ApplyMapping transformation. When the 'value' column contains non-numeric strings that cannot be cast to double, Spark throws a stage failure because it cannot complete the required type conversion, leading to task failures and job termination.

Answer

The output path is missing.

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The S3 bucket does not exist.

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The job does not have enough memory.

Question 5

A team is building a machine learning model for natural language processing using SageMaker BlazingText. The data preparation step must format the training data correctly. What format does BlazingText require for supervised text classification?

Accepted Answer

Tokenized words separated by spaces, with text and labels combined in a single line (e.g., '__label__positive great product'). BlazingText for supervised text classification expects the training data in a specific format where each line contains the text and its labels, with labels prefixed by '__label__'. This format allows BlazingText to efficiently parse and process the data for training the word2vec or classification model without additional preprocessing. Option C correctly describes this format, where the label and text are space-separated on a single line.

Answer

One-hot encoded feature vectors stored in CSV

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JSON lines with a 'text' and 'label' field

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TFRecord files with sequence features

Question 6

A company collects sensor data from IoT devices. The data arrives with missing timestamps due to network issues. For anomaly detection, the engineer needs to create features that capture rolling statistics over fixed windows. Which data preprocessing step is essential before feature generation?

Accepted Answer

Resample data to a fixed frequency. Resampling the data to a fixed frequency is essential because rolling window statistics require a consistent time index to compute accurate aggregations over fixed windows. Without a uniform timestamp grid, the window boundaries become ambiguous and the resulting features will be misaligned or incomplete, undermining the anomaly detection model.

Answer

Remove missing timestamps

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Sort data by device ID

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Impute missing values with forward fill

Question 7

A team is using AWS Glue to process streaming data from Amazon Kinesis. The streaming data contains both structured and semi-structured fields. The team needs to flatten the semi-structured fields into columns for downstream ML training. Which Glue feature is BEST suited?

Accepted Answer

Relationalize transform. The Relationalize transform is specifically designed to flatten nested JSON or semi-structured fields into a relational structure, making it ideal for converting complex streaming data from Kinesis into flat columns for ML training. It automatically handles arrays and structs by creating separate tables or columns, which is exactly what the team needs for downstream processing.

Answer

Spigot transform

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ResolveChoice transform

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ApplyMapping transform

Question 8

A data scientist needs to split a dataset into training, validation, and test sets. The dataset has a categorical target variable with imbalanced class distribution. Which splitting technique ensures that each subset has a similar proportion of each class?

Accepted Answer

Stratified split. Option C is correct because stratified splitting preserves the original class proportions in each subset (training, validation, test) by sampling each class independently. This is critical for imbalanced datasets to avoid skewed distributions that could bias model evaluation or training.

Answer

K-fold cross-validation split

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Chronological split

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Random split

Question 9

A data scientist is preparing a dataset for a binary classification model. The dataset has 10,000 records with 100 features. The target variable is imbalanced, with 95% negative class and 5% positive class. Which data preparation step should the data scientist take to address the imbalance before training?

Accepted Answer

Apply SMOTE to oversample the positive class. Option D is correct because SMOTE (Synthetic Minority Oversampling Technique) generates synthetic samples for the minority class (positive class, 5%) by interpolating between existing minority instances. This addresses the severe class imbalance (95:5) without discarding data, allowing the model to learn decision boundaries for the minority class more effectively than simple duplication.

Answer

Normalize all features to a 0-1 range

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Use cross-validation to handle imbalance

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Remove enough instances of the negative class to achieve balance

Question 10

A data scientist is using SageMaker Data Wrangler to prepare features for a classification model. Which TWO statements about feature engineering in Data Wrangler are correct?

Accepted Answer

Transformations created in Data Wrangler can be exported as a SageMaker Processing script. Option C is correct because SageMaker Data Wrangler allows you to export the entire data flow, including all transformations, as a SageMaker Processing script. This script can be run at scale on managed infrastructure, enabling you to operationalize the feature engineering pipeline for training or inference without manual rework.

Answer

Data Wrangler only supports CSV and Parquet input formats

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Data Wrangler enables writing custom PySpark transformations

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Data Wrangler automatically scales features for XGBoost models

Question 11

A machine learning engineer is using SageMaker Processing to run a scikit-learn preprocessing script. The script reads a CSV file from S3, applies a StandardScaler, and writes the output. The job fails with a 'MemoryError'. Which change should the engineer make to the data preparation process?

Accepted Answer

Increase the instance memory size for the processing job. The MemoryError indicates that the processing job's instance does not have enough RAM to hold the dataset and the intermediate results of the StandardScaler (which computes mean and variance in memory). Increasing the instance memory size (Option B) directly resolves this by providing more RAM for the scikit-learn operations. SageMaker Processing jobs allow you to choose instances with larger memory, such as the r5 or r6i families, to accommodate larger datasets.

Answer

Use a SageMaker Spark container instead of scikit-learn

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Write the output as Parquet instead of CSV

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Standardize the features before loading into the DataFrame

Question 12

A data engineer notices that an AWS Glue ETL job is failing with an Out of Memory error when processing a large dataset. The dataset is 500 GB in size, and the worker type is G.1X. Which change is MOST likely to resolve the issue?

Accepted Answer

Use a larger worker type like G.2X. The G.1X worker type provides 16 GB of memory per worker. A 500 GB dataset requires sufficient aggregate memory across workers for processing. Increasing the worker type to G.2X (which doubles memory to 32 GB per worker) increases the memory per executor, allowing each task to handle larger data partitions without running out of memory. This directly addresses the Out of Memory error by providing more heap space for Spark operations.

Answer

Partition the input data into smaller files

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Use a Spark DataFrame instead of RDD

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Increase the number of workers

Question 13

A company uses SageMaker Processing jobs to clean customer transaction data. The processing script runs on a single ml.m5.large instance and takes 30 minutes to process 50 GB of data in CSV format. To reduce processing time, the company wants to process 200 GB of data within 1 hour. Which combination of changes should the company make?

Accepted Answer

Increase InstanceCount to 4 and convert the data to Parquet format.. Option C is correct because increasing InstanceCount to 4 allows parallel processing of the 200 GB dataset across multiple ml.m5.large instances, each handling 50 GB, which directly reduces processing time. Converting the data from CSV to Parquet format further accelerates processing by enabling columnar storage and predicate pushdown, reducing I/O and CPU overhead. Together, these changes can achieve the goal of processing 200 GB within 1 hour, as the original 50 GB took 30 minutes on a single instance.

Answer

Run the job in local mode with a larger EBS volume.

Answer

Increase VolumeSizeInGB to 100 and use gzip compression.

Answer

Use a larger instance type (e.g., ml.r5.4xlarge) and keep the same script.

Question 14

A company runs an online retail business and wants to build a product recommendation system. They have a dataset of customer purchases stored in Amazon S3 as CSV files. The dataset includes columns: 'customer_id', 'product_id', 'purchase_date', 'quantity', 'price', and 'category'. The data science team plans to use Amazon SageMaker to train a factorization machines model. During data exploration, they discover that the 'category' column has 1,200 unique values, and many categories appear only a few times. The 'product_id' column has 50,000 unique values. They want to include both features in the model. The team is concerned about the high cardinality of these features. Which approach should they take to prepare these features for the factorization machines model?

Accepted Answer

Encode both columns as integer indices and feed them directly to the factorization machines algorithm as categorical features.. Option C is correct because Amazon SageMaker's factorization machines algorithm natively supports categorical features encoded as integer indices (0-based). This avoids the explosion of features from one-hot encoding (which would create 51,200 columns) and leverages the algorithm's ability to learn interactions between high-cardinality features via factorized parameters, making it both memory-efficient and effective for sparse data.

Answer

Apply one-hot encoding to both 'product_id' and 'category' columns.

Answer

Drop the 'category' column and only use 'product_id' since it has more granularity.

Answer

Apply principal component analysis (PCA) to reduce the dimensionality of the categorical features.

Question 15

A data engineer needs to provide the data science team with access to various data sources for machine learning. The team uses Amazon SageMaker Studio. Which TWO data sources can be accessed directly from SageMaker Studio notebooks without additional infrastructure? (Choose two.)

Accepted Answer

Amazon S3.. Amazon SageMaker Studio notebooks have a built-in SageMaker SDK that can directly read from and write to Amazon S3 using the `s3fs` filesystem or the SageMaker `s3_utils` module. This integration requires no additional infrastructure because S3 is the default storage backend for SageMaker, and the notebook environment is pre-configured with the necessary IAM roles and boto3 libraries to access S3 buckets directly.

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Amazon Redshift.

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Amazon DynamoDB.

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Amazon RDS (MySQL).