[Full-Version] 2021 New DumpsTorrent Professional-Machine-Learning-Engineer PDF Recently Updated Questions
Professional-Machine-Learning-Engineer Exam with Guarantee Updated 72 Questions
Understanding functional and technical aspects of Professional Machine Learning Engineer - Google Data Preparation and Processing
The following will be discussed in Google Professional-Machine-Learning-Engineer dumps:
- Handling outliers
- Data validation
- Evaluation of data quality and feasibility
- Feature crosses
- Statistical fundamentals at scale
- Database migration
- Managing large samples (TFRecords)
- Design data pipelines
- Data exploration (EDA)
- Data privacy and compliance
- Handling missing data
- Visualization
- Build data pipelines
- Feature engineering
- Monitoring/changing deployed pipelines
- Batching and streaming data pipelines at scale
- Data leakage and augmentation
- Transformations (TensorFlow Transform)
- Data ingestion
- Streaming data (e.g. from IoT devices)
- Ingestion of various file types (e.g. Csv, json, img, parquet or databases, Hadoop/Spark)
- Class imbalance
- Feature selection
- Encoding structured data types
Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Problem Framing
The following will be discussed in Google Professional-Machine-Learning-Engineer dumps:
- Success metrics
- Define ML problem
- Assessing data readiness
- Identifying data sources
- Define business success criteria
- Defining business problems
- Aligning with Google AI principles and practices (e.g. different biases)
- Defining the input (features) and predicted output format
- Identify risks to feasibility and implementation of ML solution. Considerations include:
- Assessing and communicating business impact
- Managing incorrect results
- Assessing ML solution readiness
- Determination of when a model is deemed unsuccessful
- Defining problem type (classification, regression, clustering, etc.)
- Defining output use
- Identifying nonML solutions
- Defining outcome of model predictions
- Key results
NEW QUESTION 18
A company's Machine Learning Specialist needs to improve the training speed of a time-series forecasting model using TensorFlow. The training is currently implemented on a single-GPU machine and takes approximately 23 hours to complete. The training needs to be run daily.
The model accuracy is acceptable, but the company anticipates a continuous increase in the size of the training data and a need to update the model on an hourly, rather than a daily, basis. The company also wants to minimize coding effort and infrastructure changes.
What should the Machine Learning Specialist do to the training solution to allow it to scale for future demand?
- A. Change the TensorFlow code to implement a Horovod distributed framework supported by Amazon SageMaker. Parallelize the training to as many machines as needed to achieve the business goals.
- B. Do not change the TensorFlow code. Change the machine to one with a more powerful GPU to speed up the training.
- C. Move the training to Amazon EMR and distribute the workload to as many machines as needed to achieve the business goals.
- D. Switch to using a built-in AWS SageMaker DeepAR model. Parallelize the training to as many machines as needed to achieve the business goals.
Answer: A
NEW QUESTION 19
You work for an advertising company and want to understand the effectiveness of your company's latest advertising campaign. You have streamed 500 MB of campaign data into BigQuery. You want to query the table, and then manipulate the results of that query with a pandas dataframe in an Al Platform notebook. What should you do?
- A. Export your table as a CSV file from BigQuery to Google Drive, and use the Google Drive API to ingest the file into your notebook instance
- B. Use Al Platform Notebooks' BigQuery cell magic to query the data, and ingest the results as a pandas dataframe
- C. From a bash cell in your Al Platform notebook, use the bq extract command to export the table as a CSV file to Cloud Storage, and then use gsutii cp to copy the data into the notebook Use pandas. read_csv to ingest the file as a pandas dataframe
- D. Download your table from BigQuery as a local CSV file, and upload it to your Al Platform notebook instance Use pandas. read_csv to ingest the file as a pandas dataframe
Answer: D
NEW QUESTION 20
You have trained a text classification model in TensorFlow using Al Platform. You want to use the trained model for batch predictions on text data stored in BigQuery while minimizing computational overhead. What should you do?
- A. Deploy and version the model on Al Platform.
- B. Use Dataflow with the SavedModel to read the data from BigQuery
- C. Submit a batch prediction job on Al Platform that points to the model location in Cloud Storage.
- D. Export the model to BigQuery ML.
Answer: D
NEW QUESTION 21
Your organization's call center has asked you to develop a model that analyzes customer sentiments in each call. The call center receives over one million calls daily, and data is stored in Cloud Storage. The data collected must not leave the region in which the call originated, and no Personally Identifiable Information (Pll) can be stored or analyzed. The data science team has a third-party tool for visualization and access which requires a SQL ANSI-2011 compliant interface. You need to select components for data processing and for analytics. How should the data pipeline be designed?
- A. 1 = Dataflow, 2 = BigQuery
- B. 1 = Dataflow, 2 = Cloud SQL
- C. 1 = Cloud Function, 2 = Cloud SQL
- D. 1 = Pub/Sub, 2 = Datastore
Answer: C
NEW QUESTION 22
A Machine Learning Specialist uploads a dataset to an Amazon S3 bucket protected with server-side encryption using AWS KMS.
How should the ML Specialist define the Amazon SageMaker notebook instance so it can read the same dataset from Amazon S3?
- A. Сonfigure the Amazon SageMaker notebook instance to have access to the VPC. Grant permission in the KMS key policy to the notebook's KMS role.
- B. Assign an IAM role to the Amazon SageMaker notebook with S3 read access to the dataset. Grant permission in the KMS key policy to that role.
- C. Define security group(s) to allow all HTTP inbound/outbound traffic and assign those security group(s) to the Amazon SageMaker notebook instance.
- D. Assign the same KMS key used to encrypt data in Amazon S3 to the Amazon SageMaker notebook instance.
Answer: D
Explanation:
Explanation/Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/encryption-at-rest.html
NEW QUESTION 23
A Data Scientist needs to analyze employment data. The dataset contains approximately 10 million observations on people across 10 different features. During the preliminary analysis, the Data Scientist notices that income and age distributions are not normal. While income levels shows a right skew as expected, with fewer individuals having a higher income, the age distribution also show a right skew, with fewer older individuals participating in the workforce.
Which feature transformations can the Data Scientist apply to fix the incorrectly skewed data? (Choose two.)
- A. High-degree polynomial transformation
- B. Numerical value binning
- C. One hot encoding
- D. Logarithmic transformation
- E. Cross-validation
Answer: B,E
NEW QUESTION 24
You want to rebuild your ML pipeline for structured data on Google Cloud. You are using PySpark to conduct data transformations at scale, but your pipelines are taking over 12 hours to run. To speed up development and pipeline run time, you want to use a serverless tool and SQL syntax. You have already moved your raw data into Cloud Storage. How should you build the pipeline on Google Cloud while meeting the speed and processing requirements?
- A. Convert your PySpark into SparkSQL queries to transform the data and then run your pipeline on Dataproc to write the data into BigQuery.
- B. Ingest your data into BigQuery using BigQuery Load, convert your PySpark commands into BigQuery SQL queries to transform the data, and then write the transformations to a new table
- C. Use Data Fusion's GUI to build the transformation pipelines, and then write the data into BigQuery
- D. Ingest your data into Cloud SQL convert your PySpark commands into SQL queries to transform the data, and then use federated queries from BigQuery for machine learning
Answer: A
NEW QUESTION 25
This graph shows the training and validation loss against the epochs for a neural network.
The network being trained is as follows:
* Two dense layers, one output neuron
* 100 neurons in each layer
* 100 epochs
* Random initialization of weights
Which technique can be used to improve model performance in terms of accuracy in the validation set?
- A. Random initialization of weights with appropriate seed
- B. Increasing the number of epochs
- C. Early stopping
- D. Adding another layer with the 100 neurons
Answer: B
NEW QUESTION 26
You are building a linear model with over 100 input features, all with values between -1 and 1. You suspect that many features are non-informative. You want to remove the non-informative features from your model while keeping the informative ones in their original form. Which technique should you use?
- A. Use Principal Component Analysis to eliminate the least informative features.
- B. Use L1 regularization to reduce the coefficients of uninformative features to 0.
- C. Use an iterative dropout technique to identify which features do not degrade the model when removed.
- D. After building your model, use Shapley values to determine which features are the most informative.
Answer: D
NEW QUESTION 27
A Data Engineer needs to build a model using a dataset containing customer credit card information How can the Data Engineer ensure the data remains encrypted and the credit card information is secure?
- A. Use a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMaker instance in a VPC. Use the SageMaker DeepAR algorithm to randomize the credit card numbers.
- B. Use an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically discard credit card numbers and insert fake credit card numbers.
- C. Use AWS KMS to encrypt the data on Amazon S3 and Amazon SageMaker, and redact the credit card numbers from the customer data with AWS Glue.
- D. Use an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker instance in a VPC. Use the SageMaker principal component analysis (PCA) algorithm to reduce the length of the credit card numbers.
Answer: D
Explanation:
Explanation/Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/pca.html
NEW QUESTION 28
You work for a large hotel chain and have been asked to assist the marketing team in gathering predictions for a targeted marketing strategy. You need to make predictions about user lifetime value (LTV) over the next 30 days so that marketing can be adjusted accordingly. The customer dataset is in BigQuery, and you are preparing the tabular data for training with AutoML Tables. This data has a time signal that is spread across multiple columns. How should you ensure that AutoML fits the best model to your data?
- A. Manually combine all columns that contain a time signal into an array Allow AutoML to interpret this array appropriately Choose an automatic data split across the training, validation, and testing sets
- B. Submit the data for training without performing any manual transformations, and indicate an appropriate column as the Time column Allow AutoML to split your data based on the time signal provided, and reserve the more recent data for the validation and testing sets
- C. Submit the data for training without performing any manual transformations Allow AutoML to handle the appropriate transformations Choose an automatic data split across the training, validation, and testing sets
- D. Submit the data for training without performing any manual transformations Use the columns that have a time signal to manually split your data Ensure that the data in your validation set is from 30 days after the data in your training set and that the data in your testing set is from 30 days after your validation set
Answer: D
NEW QUESTION 29
A large company has developed a BI application that generates reports and dashboards using data collected from various operational metrics. The company wants to provide executives with an enhanced experience so they can use natural language to get data from the reports. The company wants the executives to be able ask questions using written and spoken interfaces.
Which combination of services can be used to build this conversational interface? (Choose three.)
- A. Amazon Transcribe
- B. Amazon Polly
- C. Alexa for Business
- D. Amazon Lex
- E. Amazon Connect
- F. Amazon Comprehend
Answer: A,E,F
NEW QUESTION 30
A Machine Learning Specialist is using an Amazon SageMaker notebook instance in a private subnet of a corporate VPC. The ML Specialist has important data stored on the Amazon SageMaker notebook instance's Amazon EBS volume, and needs to take a snapshot of that EBS volume. However, the ML Specialist cannot find the Amazon SageMaker notebook instance's EBS volume or Amazon EC2 instance within the VPC.
Why is the ML Specialist not seeing the instance visible in the VPC?
- A. Amazon SageMaker notebook instances are based on the EC2 instances within the customer account, but they run outside of VPCs.
- B. Amazon SageMaker notebook instances are based on the Amazon ECS service within customer accounts.
- C. Amazon SageMaker notebook instances are based on AWS ECS instances running within AWS service accounts.
- D. Amazon SageMaker notebook instances are based on EC2 instances running within AWS service accounts.
Answer: D
Explanation:
Explanation/Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/gs-setup-working-env.html
NEW QUESTION 31
A credit card company wants to build a credit scoring model to help predict whether a new credit card applicant will default on a credit card payment. The company has collected data from a large number of sources with thousands of raw attributes. Early experiments to train a classification model revealed that many attributes are highly correlated, the large number of features slows down the training speed significantly, and that there are some overfitting issues.
The Data Scientist on this project would like to speed up the model training time without losing a lot of information from the original dataset.
Which feature engineering technique should the Data Scientist use to meet the objectives?
- A. Use an autoencoder or principal component analysis (PCA) to replace original features with new features
- B. Run self-correlation on all features and remove highly correlated features
- C. Normalize all numerical values to be between 0 and 1
- D. Cluster raw data using k-means and use sample data from each cluster to build a new dataset
Answer: C
NEW QUESTION 32
You are training a Resnet model on Al Platform using TPUs to visually categorize types of defects in automobile engines. You capture the training profile using the Cloud TPU profiler plugin and observe that it is highly input-bound. You want to reduce the bottleneck and speed up your model training process. Which modifications should you make to the tf .data dataset?
Choose 2 answers
- A. Use the interleave option for reading data
- B. Decrease the batch size argument in your transformation
- C. Increase the buffer size for the shuffle option.
- D. Reduce the value of the repeat parameter
- E. Set the prefetch option equal to the training batch size
Answer: A,E
NEW QUESTION 33
A Machine Learning Specialist is assigned a TensorFlow project using Amazon SageMaker for training, and needs to continue working for an extended period with no Wi-Fi access.
Which approach should the Specialist use to continue working?
- A. Install Python 3 and boto3 on their laptop and continue the code development using that environment.
- B. Download TensorFlow from tensorflow.org to emulate the TensorFlow kernel in the SageMaker environment.
- C. Download the SageMaker notebook to their local environment, then install Jupyter Notebooks on their laptop and continue the development in a local notebook.
- D. Download the TensorFlow Docker container used in Amazon SageMaker from GitHub to their local environment, and use the Amazon SageMaker Python SDK to test the code.
Answer: A
Explanation:
Explanation
NEW QUESTION 34
You work for a toy manufacturer that has been experiencing a large increase in demand. You need to build an ML model to reduce the amount of time spent by quality control inspectors checking for product defects. Faster defect detection is a priority. The factory does not have reliable Wi-Fi. Your company wants to implement the new ML model as soon as possible. Which model should you use?
- A. AutoML Vision Edge mobile-versatile-1 model
- B. AutoML Vision model
- C. AutoML Vision Edge mobile-high-accuracy-1 model
- D. AutoML Vision Edge mobile-low-latency-1 model
Answer: B
NEW QUESTION 35
An interactive online dictionary wants to add a widget that displays words used in similar contexts. A Machine Learning Specialist is asked to provide word features for the downstream nearest neighbor model powering the widget.
What should the Specialist do to meet these requirements?
- A. Produce a set of synonyms for every word using Amazon Mechanical Turk.
- B. Create one-hot word encoding vectors.
- C. Download word embeddings pre-trained on a large corpus.
- D. Create word embedding vectors that store edit distance with every other word.
Answer: B
Explanation:
Explanation/Reference: https://aws.amazon.com/blogs/machine-learning/amazon-sagemaker-object2vec-adds-new- features-that-support-automatic-negative-sampling-and-speed-up-training/
NEW QUESTION 36
You have written unit tests for a Kubeflow Pipeline that require custom libraries. You want to automate the execution of unit tests with each new push to your development branch in Cloud Source Repositories. What should you do?
- A. Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories Configure a Pub/Sub trigger for Cloud Run, and execute the unit tests on Cloud Run.
- B. Using Cloud Build, set an automated trigger to execute the unit tests when changes are pushed to your development branch.
- C. Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories. Execute the unit tests using a Cloud Function that is triggered when messages are sent to the Pub/Sub topic
- D. Write a script that sequentially performs the push to your development branch and executes the unit tests on Cloud Run
Answer: B
NEW QUESTION 37
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