Real Exam Questions Professional-Machine-Learning-Engineer Dumps Exam Questions in here [Aug-2021]
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NEW QUESTION 24
You are developing ML models with Al Platform for image segmentation on CT scans. You frequently update your model architectures based on the newest available research papers, and have to rerun training on the same dataset to benchmark their performance. You want to minimize computation costs and manual intervention while having version control for your code. What should you do?
- A. Use the gcloud command-line tool to submit training jobs on Al Platform when you update your code
- B. Use Cloud Build linked with Cloud Source Repositories to trigger retraining when new code is pushed to the repository
- C. Use Cloud Functions to identify changes to your code in Cloud Storage and trigger a retraining job
- D. Create an automated workflow in Cloud Composer that runs daily and looks for changes in code in Cloud Storage using a sensor.
Answer: A
NEW QUESTION 25
You are developing models to classify customer support emails. You created models with TensorFlow Estimators using small datasets on your on-premises system, but you now need to train the models using large datasets to ensure high performance. You will port your models to Google Cloud and want to minimize code refactoring and infrastructure overhead for easier migration from on-prem to cloud. What should you do?
- A. Create a Managed Instance Group with autoscaling
- B. Create a cluster on Dataproc for training
- C. Use Al Platform for distributed training
- D. Use Kubeflow Pipelines to train on a Google Kubernetes Engine cluster.
Answer: A
NEW QUESTION 26
Your team has been tasked with creating an ML solution in Google Cloud to classify support requests for one of your platforms. You analyzed the requirements and decided to use TensorFlow to build the classifier so that you have full control of the model's code, serving, and deployment. You will use Kubeflow pipelines for the ML platform. To save time, you want to build on existing resources and use managed services instead of building a completely new model. How should you build the classifier?
- A. Use AutoML Natural Language to build the support requests classifier
- B. Use the Natural Language API to classify support requests
- C. Use an established text classification model on Al Platform as-is to classify support requests
- D. Use an established text classification model on Al Platform to perform transfer learning
Answer: C
NEW QUESTION 27
You are building an ML model to detect anomalies in real-time sensor dat a. You will use Pub/Sub to handle incoming requests. You want to store the results for analytics and visualization. How should you configure the pipeline?
- A. 1 = BigQuery, 2 = Al Platform, 3 = Cloud Storage
- B. 1 = DataProc, 2 = AutoML, 3 = Cloud Bigtable
- C. 1 = Dataflow, 2 - Al Platform, 3 = BigQuery
- D. 1 = BigQuery, 2 = AutoML, 3 = Cloud Functions
Answer: D
NEW QUESTION 28
You are an ML engineer in the contact center of a large enterprise. You need to build a sentiment analysis tool that predicts customer sentiment from recorded phone conversations. You need to identify the best approach to building a model while ensuring that the gender, age, and cultural differences of the customers who called the contact center do not impact any stage of the model development pipeline and results. What should you do?
- A. Convert the speech to text and build a model based on the words
- B. Extract sentiment directly from the voice recordings
- C. Convert the speech to text and extract sentiments based on the sentences
- D. Convert the speech to text and extract sentiment using syntactical analysis
Answer: C
NEW QUESTION 29
You work with a data engineering team that has developed a pipeline to clean your dataset and save it in a Cloud Storage bucket. You have created an ML model and want to use the data to refresh your model as soon as new data is available. As part of your CI/CD workflow, you want to automatically run a Kubeflow Pipelines training job on Google Kubernetes Engine (GKE). How should you architect this workflow?
- A. Use Cloud Scheduler to schedule jobs at a regular interval. For the first step of the job. check the timestamp of objects in your Cloud Storage bucket If there are no new files since the last run, abort the job.
- B. Configure a Cloud Storage trigger to send a message to a Pub/Sub topic when a new file is available in a storage bucket. Use a Pub/Sub-triggered Cloud Function to start the training job on a GKE cluster
- C. Configure your pipeline with Dataflow, which saves the files in Cloud Storage After the file is saved, start the training job on a GKE cluster
- D. Use App Engine to create a lightweight python client that continuously polls Cloud Storage for new files As soon as a file arrives, initiate the training job
Answer: B
NEW QUESTION 30
A retail company intends to use machine learning to categorize new products. A labeled dataset of current products was provided to the Data Science team. The dataset includes 1,200 products. The labeled dataset has 15 features for each product such as title dimensions, weight, and price. Each product is labeled as belonging to one of six categories such as books, games, electronics, and movies.
Which model should be used for categorizing new products using the provided dataset for training?
- A. A deep convolutional neural network (CNN) with a softmax activation function for the last layer
- B. A DeepAR forecasting model based on a recurrent neural network (RNN)
- C. AnXGBoost model where the objective parameter is set to multi:softmax
- D. A regression forest where the number of trees is set equal to the number of product categories
Answer: A
NEW QUESTION 31
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. Logarithmic transformation
- B. Cross-validation
- C. One hot encoding
- D. High-degree polynomial transformation
- E. Numerical value binning
Answer: B,E
NEW QUESTION 32
You trained a text classification model. You have the following SignatureDefs:
What is the correct way to write the predict request?
- A. data = json dumps({"signature_name": f,serving_default", "instances": [['a', 'b'], [c\ 'd'], ['e\ T]]})
- B. data = json dumps({"signature_name": "serving_default"! "instances": [['a', 'b', "c", 'd', 'e', 'f']]})
- C. data = json.dumps({"signature_name": "serving_default'\ "instances": [fab', 'be1, 'cd']]})
- D. data = json.dumps({"signature_name": "serving_default, "instances": [['a', 'b\ 'c'1, [d\ 'e\ T]]})
Answer: D
NEW QUESTION 33
You have deployed multiple versions of an image classification model on Al Platform. You want to monitor the performance of the model versions overtime. How should you perform this comparison?
- A. Compare the mean average precision across the models using the Continuous Evaluation feature
- B. Compare the receiver operating characteristic (ROC) curve for each model using the What-lf Tool
- C. Compare the loss performance for each model on the validation data
- D. Compare the loss performance for each model on a held-out dataset.
Answer: C
NEW QUESTION 34
A city wants to monitor its air quality to address the consequences of air pollution. A Machine Learning Specialist needs to forecast the air quality in parts per million of contaminates for the next 2 days in the city. As this is a prototype, only daily data from the last year is available.
Which model is MOST likely to provide the best results in Amazon SageMaker?
- A. Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_typeof regressor.
- B. Use Amazon SageMaker Random Cut Forest (RCF) on the single time series consisting of the full year of data.
- C. Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the single time series consisting of the full year of data with a predictor_typeof regressor.
- D. Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_typeof classifier.
Answer: A
Explanation:
Explanation/Reference: https://aws.amazon.com/blogs/machine-learning/build-a-model-to-predict-the-impact-of-weather- on-urban-air-quality-using-amazon-sagemaker/?ref=Welcome.AI
NEW QUESTION 35
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. Decrease the batch size argument in your transformation
- B. Increase the buffer size for the shuffle option.
- C. Use the interleave option for reading data
- D. Set the prefetch option equal to the training batch size
- E. Reduce the value of the repeat parameter
Answer: A,C
NEW QUESTION 36
You have a demand forecasting pipeline in production that uses Dataflow to preprocess raw data prior to model training and prediction. During preprocessing, you employ Z-score normalization on data stored in BigQuery and write it back to BigQuery. New training data is added every week. You want to make the process more efficient by minimizing computation time and manual intervention. What should you do?
- A. Use the normalizer_fn argument in TensorFlow's Feature Column API
- B. Normalize the data with Apache Spark using the Dataproc connector for BigQuery
- C. Translate the normalization algorithm into SQL for use with BigQuery
- D. Normalize the data using Google Kubernetes Engine
Answer: C
NEW QUESTION 37
Your team is building an application for a global bank that will be used by millions of customers. You built a forecasting model that predicts customers1 account balances 3 days in the future. Your team will use the results in a new feature that will notify users when their account balance is likely to drop below $25. How should you serve your predictions?
- A. 1. Build a notification system on Firebase
2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when the average of all account balance predictions drops below the $25 threshold - B. 1. Create a Pub/Sub topic for each user
2. Deploy an application on the App Engine standard environment that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold - C. 1 Build a notification system on Firebase
2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when your model predicts that a user's account balance will drop below the $25 threshold - D. 1. Create a Pub/Sub topic for each user
2 Deploy a Cloud Function that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold.
Answer: B
NEW QUESTION 38
Your team needs to build a model that predicts whether images contain a driver's license, passport, or credit card. The data engineering team already built the pipeline and generated a dataset composed of 10,000 images with driver's licenses, 1,000 images with passports, and 1,000 images with credit cards. You now have to train a model with the following label map: ['driversjicense', 'passport', 'credit_card']. Which loss function should you use?
- A. Categorical cross-entropy
- B. Categorical hinge
- C. Binary cross-entropy
- D. Sparse categorical cross-entropy
Answer: C
NEW QUESTION 39
Your team is working on an NLP research project to predict political affiliation of authors based on articles they have written. You have a large training dataset that is structured like this:
A)
B)
C)
D)
- A. Option A
- B. Option D
- C. Option B
- D. Option C
Answer: B
NEW QUESTION 40
A Machine Learning Specialist is working with a large company to leverage machine learning within its products. The company wants to group its customers into categories based on which customers will and will not churn within the next 6 months. The company has labeled the data available to the Specialist.
Which machine learning model type should the Specialist use to accomplish this task?
- A. Clustering
- B. Linear regression
- C. Classification
- D. Reinforcement learning
Answer: C
Explanation:
The goal of classification is to determine to which class or category a data point (customer in our case) belongs to. For classification problems, data scientists would use historical data with predefined target variables AKA labels (churner/non-churner) - answers that need to be predicted - to train an algorithm. With classification, businesses can answer the following questions:
* Will this customer churn or not?
* Will a customer renew their subscription?
* Will a user downgrade a pricing plan?
* Are there any signs of unusual customer behavior?
Reference: https://www.kdnuggets.com/2019/05/churn-prediction-machine-learning.html
NEW QUESTION 41
Your team needs to build a model that predicts whether images contain a driver's license, passport, or credit card. The data engineering team already built the pipeline and generated a dataset composed of 10,000 images with driver's licenses, 1,000 images with passports, and 1,000 images with credit cards. You now have to train a model with the following label map: ['driversjicense', 'passport', 'credit_card']. Which loss function should you use?
- A. Categorical cross-entropy
- B. Categorical hinge
- C. Sparse categorical cross-entropy
- D. Binary cross-entropy
Answer: C
Explanation:
se sparse_categorical_crossentropy. Examples for above 3-class classification problem: [1] , [2], [3]
NEW QUESTION 42
You have been asked to develop an input pipeline for an ML training model that processes images from disparate sources at a low latency. You discover that your input data does not fit in memory. How should you create a dataset following Google-recommended best practices?
- A. Convert the images Into TFRecords, store the images in Cloud Storage, and then use the tf. data API to read the images for training
- B. Convert the images to tf .Tensor Objects, and then run tf. data. Dataset. from_tensors ().
- C. Create a tf.data.Dataset.prefetch transformation
- D. Convert the images to tf .Tensor Objects, and then run Dataset. from_tensor_slices{).
Answer: A
NEW QUESTION 43
A company ingests machine learning (ML) data from web advertising clicks into an Amazon S3 data lake. Click data is added to an Amazon Kinesis data stream by using the Kinesis Producer Library (KPL). The data is loaded into the S3 data lake from the data stream by using an Amazon Kinesis Data Firehose delivery stream.
As the data volume increases, an ML specialist notices that the rate of data ingested into Amazon S3 is relatively constant. There also is an increasing backlog of data for Kinesis Data Streams and Kinesis Data Firehose to ingest.
Which next step is MOST likely to improve the data ingestion rate into Amazon S3?
- A. Decrease the retention period for the data stream.
- B. Increase the number of shards for the data stream.
- C. Increase the number of S3 prefixes for the delivery stream to write to.
- D. Add more consumers using the Kinesis Client Library (KCL).
Answer: B
Explanation:
Explanation/Reference:
NEW QUESTION 44
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