MLA-C01 Practice Test 9 — 10 Questions

Question 1

A data scientist is exploring data stored in an Amazon Redshift cluster. The data includes timestamp columns with different formats. The scientist wants to create a new column that standardizes the timestamp format to UTC. Which approach is MOST efficient?

Accepted Answer

Use a SELECT with CONVERT_TIMEZONE in Redshift and export to S3. Option B is correct because `CONVERT_TIMEZONE` in Amazon Redshift is a native SQL function that directly converts timestamps to UTC without moving data outside the cluster. This approach avoids the overhead of external services, leverages Redshift's massively parallel processing (MPP) engine, and is the most efficient for in-database transformations.

Answer

Use AWS Glue to read the Redshift table and apply a custom transform

Answer

Use a SageMaker notebook to query Redshift and transform

Answer

Use Amazon QuickSight to transform the timestamp

Question 2

An organization stores raw data in Amazon S3 as CSV files. They need to perform serverless data transformation and convert the data to Parquet format for efficient ML training. Which AWS service is most appropriate?

Accepted Answer

AWS Glue. AWS Glue is the most appropriate service because it is a fully managed, serverless ETL service designed specifically for data transformation tasks like converting CSV to Parquet. It automatically handles schema inference, data partitioning, and optimization for ML training workloads without requiring infrastructure management.

Answer

Amazon EMR

Answer

Amazon Athena

Answer

Amazon Redshift

Question 3

A company has a dataset of 2 billion records stored as text files in Amazon S3. The data is partitioned by year and month. The data science team wants to read only the last 6 months of data for model training using SageMaker. To minimize data scanned and reduce costs, which approach should the team use?

Accepted Answer

Use AWS Glue to create a catalog table with partitions, then query with Athena to create a filtered dataset in S3.. Option B is correct because AWS Glue can crawl the S3 data to create a catalog table with partitions by year and month. Athena can then query only the partitions corresponding to the last 6 months, scanning minimal data and writing the filtered results back to S3 for SageMaker training. This approach leverages partition pruning to reduce costs and avoids loading or processing the full 2 billion records.

Answer

Use S3 Select to retrieve only the last 6 months of data by applying an SQL expression on each object.

Answer

Use SageMaker Processing with a script that lists all objects in the bucket and reads only those with the desired prefixes.

Answer

Use SageMaker Processing with Input Mode 'File' and specify the S3 prefix for the last 6 months.

Question 4

A data scientist is preparing a dataset for a linear regression model. The dataset has a few missing values in a numerical feature with a normal distribution and no outliers. Which imputation method is most appropriate?

Accepted Answer

Impute with mean. Option B is correct because mean imputation is suitable for normally distributed data without outliers. Option A (drop rows) reduces sample size. Option C (median) is robust to outliers but not needed. Option D (mode) is for categorical data.

Answer

Impute with mode

Answer

Impute with median

Answer

Drop rows with missing values

Question 5

You are preparing a time-series dataset for a forecasting model. Which three steps are critical to prevent data leakage during preprocessing? (Choose three.)

Accepted Answer

Standardize features using parameters computed only from the training set. Standardizing features using parameters computed only from the training set is critical because it prevents information from the test set from influencing the training data. If you compute the mean and standard deviation from the entire dataset before splitting, the test set's distribution leaks into the training process, causing the model to see future data during training. This violates the temporal order and leads to overly optimistic performance estimates.

Answer

Impute missing values using the mean of the entire dataset

Answer

Shuffle the data randomly before splitting

Question 6

A machine learning engineer needs to handle missing values in a dataset containing numerical features. The missingness is completely at random (MCAR). Which imputation strategy is most robust for downstream model performance?

Accepted Answer

Impute with median of each feature. When missingness is completely at random (MCAR), imputing with the median is robust because it preserves the central tendency of the distribution without introducing bias or distorting variance. Unlike mean imputation, the median is resistant to outliers, making it a safe default for numerical features in downstream models that assume normally distributed inputs or are sensitive to skewed data.

Answer

Impute with a constant like -1

Answer

Use a model to predict missing values

Answer

Remove all rows with missing values

Question 7

An ML engineer needs to convert a raw dataset from CSV to Parquet format in a serverless manner for cost efficiency. Which AWS service can be used to perform this conversion without managing servers?

Accepted Answer

AWS Glue. AWS Glue is correct because it provides a serverless ETL service that can automatically convert CSV to Parquet using its built-in transform capabilities, such as the `ChangeSchema` or `ConvertToParquet` transforms in a Glue ETL job. This eliminates the need to provision or manage any servers, aligning with the cost-efficiency requirement.

Answer

Amazon S3 Select

Answer

Amazon EMR

Answer

AWS Lambda

Question 8

A SageMaker Processing job fails with the error: 'Unable to parse CSV file due to inconsistent number of columns'. The data is stored as CSV in S3. What is the most likely cause?

Accepted Answer

Some rows have missing values causing fewer columns. Option D is correct because inconsistent number of columns often results from rows with missing values where some fields are omitted. Option A (missing header) would cause a parsing error but not column count inconsistency. Option B (quoted commas) is handled by CSV parsers. Option C (delimiter) would cause consistent parsing issues.

Answer

The CSV file is missing a header row

Answer

The file uses a different delimiter like tab

Answer

Some fields contain quoted commas

Question 9

A team is collaborating on a machine learning project and needs to ensure that data used for training is consistent across experiments. The team wants to version datasets, track data lineage, and be able to reproduce past experiments. The team uses SageMaker for model training. Which combination of services and features should the team use?

Accepted Answer

Use SageMaker with AWS Lake Formation to manage data access, version datasets in S3, and use SageMaker Experiments to track training jobs.. Option C is correct because it combines AWS Lake Formation for fine-grained data access control and governance, S3 versioning for dataset versioning, and SageMaker Experiments to track training jobs and lineage. This trio directly addresses the need for consistent data across experiments, versioning, lineage tracking, and reproducibility in SageMaker.

Answer

Use SageMaker Pipelines to automate training and store datasets in S3 with versioning enabled.

Answer

Store datasets in Amazon DynamoDB and use Amazon Athena to query specific versions.

Answer

Use S3 versioning to store all dataset versions and AWS Glue Data Catalog to track schema changes.

Question 10

A data engineer is building a feature engineering pipeline in AWS Glue ETL to process streaming data from Amazon Kinesis. The data includes a nested JSON structure with arrays. The engineer needs to flatten the nested structures into a tabular format for machine learning. Which THREE approaches are valid for this task? (Choose 3.)

Accepted Answer

Use Python's json.loads in a map function. Option A is correct because Python's json.loads can be used within a PySpark map function to parse nested JSON strings from streaming data in AWS Glue ETL. This allows you to extract and flatten nested fields into a tabular structure by iterating over each record and converting the JSON into a flat dictionary, which can then be mapped to DataFrame columns.

Answer

Use Athena's UNNEST function on the raw data

Answer

Use Amazon SageMaker Processing with scikit-learn