MLS-C01 Test Guide - Latest MLS-C01 Test Questions

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Amazon AWS-Certified-Machine-Learning-Specialty (AWS Certified Machine Learning - Specialty) Exam is a certification program designed to validate the skills and knowledge of individuals in the field of machine learning. MLS-C01 Exam is intended for experienced practitioners who have a deep understanding of the core principles and best practices of machine learning. It is also ideal for those who are interested in pursuing a career in machine learning and want to demonstrate their expertise to potential employers.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q115-Q120):

NEW QUESTION # 115
Amazon Connect has recently been tolled out across a company as a contact call center The solution has been configured to store voice call recordings on Amazon S3 The content of the voice calls are being analyzed for the incidents being discussed by the call operators Amazon Transcribe is being used to convert the audio to text, and the output is stored on Amazon S3 Which approach will provide the information required for further analysis?

A. Use Amazon Comprehend with the transcribed files to build the key topics
B. Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the transcribed files to generate a word embeddings dictionary for the key topics
C. Use the AWS Deep Learning AMI with Gluon Semantic Segmentation on the transcribed files to train and build a model for the key topics
D. Use Amazon Translate with the transcribed files to train and build a model for the key topics

Answer: D

NEW QUESTION # 116
A data scientist is working on a forecast problem by using a dataset that consists of .csv files that are stored in Amazon S3. The files contain a timestamp variable in the following format:
March 1st, 2020, 08:14pm -
There is a hypothesis about seasonal differences in the dependent variable. This number could be higher or lower for weekdays because some days and hours present varying values, so the day of the week, month, or hour could be an important factor. As a result, the data scientist needs to transform the timestamp into weekdays, month, and day as three separate variables to conduct an analysis.
Which solution requires the LEAST operational overhead to create a new dataset with the added features?

A. Create an AWS Glue job. Develop code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3.
B. Create an Amazon EMR cluster. Develop PySpark code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3.
C. Create a new flow in Amazon SageMaker Data Wrangler. Import the S3 file, use the Featurize date
/time transform to generate the new variables, and save the dataset as a new file in Amazon S3.
D. Create a processing job in Amazon SageMaker. Develop Python code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3.

Answer: C

Explanation:
The solution C will create a new dataset with the added features with the least operational overhead because it uses Amazon SageMaker Data Wrangler, which is a service that simplifies the process of data preparation and feature engineering for machine learning. The solution C involves the following steps:
* Create a new flow in Amazon SageMaker Data Wrangler. A flow is a visual representation of the data preparation steps that can be applied to one or more datasets. The data scientist can create a new flow in the Amazon SageMaker Studio interface and import the S3 file as a data source1.
* Use the Featurize date/time transform to generate the new variables. Amazon SageMaker Data Wrangler provides a set of preconfigured transformations that can be applied to the data with a few clicks. The Featurize date/time transform can parse a date/time column and generate new columns for the year, month, day, hour, minute, second, day of week, and day of year. The data scientist can use this transform to create the new variables from the timestamp variable2.
* Save the dataset as a new file in Amazon S3. Amazon SageMaker Data Wrangler can export the transformed dataset as a new file in Amazon S3, or as a feature store in Amazon SageMaker Feature Store. The data scientist can choose the output format and location of the new file3.
The other options are not suitable because:
* Option A: Creating an Amazon EMR cluster and developing PySpark code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3 will incur more operational overhead than using Amazon SageMaker Data Wrangler. The data scientist will have to manage the Amazon EMR cluster, the PySpark application, and the data storage. Moreover, the data scientist will have to write custom code for the date/time parsing and feature generation, which may require more development effort and testing4.
* Option B: Creating a processing job in Amazon SageMaker and developing Python code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3 will incur more operational overhead than using Amazon SageMaker Data Wrangler.
The data scientist will have to manage the processing job, the Python code, and the data storage. Moreover, the data scientist will have to write custom code for the date/time parsing and feature generation, which may require more development effort and testing5.
* Option D: Creating an AWS Glue job and developing code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3 will incur more operational overhead than using Amazon SageMaker Data Wrangler. The data scientist will have to manage the AWS Glue job, the code, and the data storage. Moreover, the data scientist will have to write custom code for the date/time parsing and feature generation, which may require more development effort and testing6.
References:
* 1: Amazon SageMaker Data Wrangler
* 2: Featurize Date/Time - Amazon SageMaker Data Wrangler
* 3: Exporting Data - Amazon SageMaker Data Wrangler
* 4: Amazon EMR
* 5: Processing Jobs - Amazon SageMaker
* 6: AWS Glue

NEW QUESTION # 117
A Machine Learning Specialist must build out a process to query a dataset on Amazon S3 using Amazon Athena The dataset contains more than 800.000 records stored as plaintext CSV files Each record contains
200 columns and is approximately 1 5 MB in size Most queries will span 5 to 10 columns only How should the Machine Learning Specialist transform the dataset to minimize query runtime?

A. Convert the records to XML format
B. Convert the records to GZIP CSV format
C. Convert the records to JSON format
D. Convert the records to Apache Parquet format

Answer: D

Explanation:
Amazon Athena is an interactive query service that allows you to analyze data stored in Amazon S3 using standard SQL. Athena is serverless, so you only pay for the queries that you run and there is no infrastructure to manage.
To optimize the query performance of Athena, one of the best practices is to convert the data into a columnar format, such as Apache Parquet or Apache ORC. Columnar formats store data by columns rather than by rows, which allows Athena to scan only the columns that are relevant to the query, reducing the amount of data read and improving the query speed. Columnar formats also support compression and encoding schemes that can reduce the storage space and the data scanned per query, further enhancing the performance and reducing the cost.
In contrast, plaintext CSV files store data by rows, which means that Athena has to scan the entire row even if only a few columns are needed for the query. This increases the amount of data read and the query latency.
Moreover, plaintext CSV files do not support compression or encoding, which means that they take up more storage space and incur higher query costs.
Therefore, the Machine Learning Specialist should transform the dataset to Apache Parquet format to minimize query runtime.
Top 10 Performance Tuning Tips for Amazon Athena
Columnar Storage Formats
Using compressions will reduce the amount of data scanned by Amazon Athena, and also reduce your S3 bucket storage. It's a Win-Win for your AWS bill. Supported formats: GZIP, LZO, SNAPPY (Parquet) and ZLIB.
Reference: https://www.cloudforecast.io/blog/using-parquet-on-athena-to-save-money-on-aws/

NEW QUESTION # 118
A data scientist wants to use Amazon Forecast to build a forecasting model for inventory demand for a retail company. The company has provided a dataset of historic inventory demand for its products as a .csv file stored in an Amazon S3 bucket. The table below shows a sample of the dataset.
How should the data scientist transform the data?

A. Use AWS Batch jobs to separate the dataset into a target time series dataset, a related time series dataset, and an item metadata dataset. Upload them directly to Forecast from a local machine.
B. Use a Jupyter notebook in Amazon SageMaker to transform the data into the optimized protobuf recordIO format. Upload the dataset in this format to Amazon S3.
C. Use a Jupyter notebook in Amazon SageMaker to separate the dataset into a related time series dataset and an item metadata dataset. Upload both datasets as tables in Amazon Aurora.
D. Use ETL jobs in AWS Glue to separate the dataset into a target time series dataset and an item metadata dataset. Upload both datasets as .csv files to Amazon S3.

Answer: D

Explanation:
Amazon Forecast requires the input data to be in a specific format. The data scientist should use ETL jobs in AWS Glue to separate the dataset into a target time series dataset and an item metadata dataset. The target time series dataset should contain the timestamp, item_id, and demand columns, while the item metadata dataset should contain the item_id, category, and lead_time columns. Both datasets should be uploaded as .csv files to Amazon S3 . References:
* How Amazon Forecast Works - Amazon Forecast
* Choosing Datasets - Amazon Forecast

NEW QUESTION # 119
A company wants to use machine learning (ML) to improve its customer churn prediction model. The company stores data in an Amazon Redshift data warehouse.
A data science team wants to use Amazon Redshift machine learning (Amazon Redshift ML) to build a model and run predictions for new data directly within the data warehouse.
Which combination of steps should the company take to use Amazon Redshift ML to meet these requirements? (Select THREE.)

A. Define the feature variables and target variable for the churn prediction model.
B. Manually export the training data to Amazon S3.
C. Use Amazon Redshift Spectrum to train the model.
D. Use the SQL EXPLAIN_MODEL function to run predictions.
E. Use the SQL prediction function to run predictions,
F. Write a CREATE MODEL SQL statement to create a model.

Answer: A,E,F

Explanation:
Amazon Redshift ML enables in-database machine learning model creation and predictions, allowing data scientists to leverage Redshift for model training without needing to export data.
To create and run a model for customer churn prediction in Amazon Redshift ML:
* Define the feature variables and target variable: Identify the columns to use as features (predictors) and the target variable (outcome) for the churn prediction model.
* Create the model: Write a CREATE MODEL SQL statement, which trains the model using Amazon Redshift's integration with Amazon SageMaker and stores the model directly in Redshift.
* Run predictions: Use the SQL PREDICT function to generate predictions on new data directly within Redshift.
Options B, D, and E are not required as Redshift ML handles model creation and prediction without manual data export to Amazon S3 or additional Spectrum integration.

NEW QUESTION # 120
......

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q115-Q120):

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