[Jun-2023] Latest Google Professional-Machine-Learning-Engineer Certification Practice Test Questions
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The certification exam covers a wide range of topics related to machine learning engineering, including data preparation and analysis, feature engineering, model selection and training, hyperparameter tuning, deployment, and monitoring. Candidates will be required to demonstrate their ability to develop and manage machine learning models using Google Cloud Platform tools and services. Successful candidates will be able to design, implement, and optimize machine learning models to solve complex business problems and improve operational efficiency. The Google Professional Machine Learning Engineer Certification Exam is an excellent way for individuals to demonstrate their expertise in the field of machine learning engineering and to advance their careers in this rapidly growing field.
The certification exam is divided into several sections, each of which focuses on a specific aspect of machine learning. The sections include data preparation, model building, model deployment, and monitoring. Each section is designed to test the individual's ability to apply machine learning concepts in a practical setting. The exam format includes multiple-choice questions, case studies, and hands-on exercises, which measure the individual's ability to apply machine learning concepts to real-world scenarios.
NEW QUESTION # 34
You recently joined a machine learning team that will soon release a new project. As a lead on the project, you are asked to determine the production readiness of the ML components. The team has already tested features and data, model development, and infrastructure. Which additional readiness check should you recommend to the team?
- A. Ensure that model performance is monitored
- B. Ensure that feature expectations are captured in the schema
- C. Ensure that all hyperparameters are tuned
- D. Ensure that training is reproducible
Answer: C
NEW QUESTION # 35
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. Export the model to BigQuery ML.
- B. Deploy and version the model on Al Platform.
- C. Submit a batch prediction job on Al Platform that points to the model location in Cloud Storage.
- D. Use Dataflow with the SavedModel to read the data from BigQuery
Answer: A
NEW QUESTION # 36
Machine Learning Specialist is training a model to identify the make and model of vehicles in images. The Specialist wants to use transfer learning and an existing model trained on images of general objects. The Specialist collated a large custom dataset of pictures containing different vehicle makes and models.
What should the Specialist do to initialize the model to re-train it with the custom data?
- A. Initialize the model with pre-trained weights in all layers including the last fully connected layer.
- B. Initialize the model with random weights in all layers and replace the last fully connected layer.
- C. Initialize the model with random weights in all layers including the last fully connected layer.
- D. Initialize the model with pre-trained weights in all layers and replace the last fully connected layer.
Answer: D
Explanation:
Explanation/Reference:
NEW QUESTION # 37
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 = AutoML, 3 = Cloud Functions
- B. 1 = Dataflow, 2 - Al Platform, 3 = BigQuery
- C. 1 = BigQuery, 2 = Al Platform, 3 = Cloud Storage
- D. 1 = DataProc, 2 = AutoML, 3 = Cloud Bigtable
Answer: B
NEW QUESTION # 38
You are an ML engineer at a regulated insurance company. You are asked to develop an insurance approval model that accepts or rejects insurance applications from potential customers. What factors should you consider before building the model?
- A. Traceability, reproducibility, and explainability
- B. Federated learning, reproducibility, and explainability
- C. Redaction, reproducibility, and explainability
- D. Differential privacy federated learning, and explainability
Answer: A
NEW QUESTION # 39
A Machine Learning Specialist is developing a daily ETL workflow containing multiple ETL jobs. The workflow consists of the following processes:
* Start the workflow as soon as data is uploaded to Amazon S3.
* When all the datasets are available in Amazon S3, start an ETL job to join the uploaded datasets with multiple terabyte-sized datasets already stored in Amazon S3.
* Store the results of joining datasets in Amazon S3.
* If one of the jobs fails, send a notification to the Administrator.
Which configuration will meet these requirements?
- A. Develop the ETL workflow using AWS Batch to trigger the start of ETL jobs when data is uploaded to Amazon S3. Use AWS Glue to join the datasets in Amazon S3. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.
- B. Use AWS Lambda to chain other Lambda functions to read and join the datasets in Amazon S3 as soon as the data is uploaded to Amazon S3. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.
- C. Develop the ETL workflow using AWS Lambda to start an Amazon SageMaker notebook instance. Use a lifecycle configuration script to join the datasets and persist the results in Amazon S3. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.
- D. Use AWS Lambda to trigger an AWS Step Functions workflow to wait for dataset uploads to complete in Amazon S3. Use AWS Glue to join the datasets. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.
Answer: D
Explanation:
Explanation/Reference: https://aws.amazon.com/step-functions/use-cases/
NEW QUESTION # 40
A company is using Amazon Polly to translate plaintext documents to speech for automated company announcements. However, company acronyms are being mispronounced in the current documents.
How should a Machine Learning Specialist address this issue for future documents?
- A. Convert current documents to SSML with pronunciation tags.
- B. Use Amazon Lex to preprocess the text files for pronunciation
- C. Create an appropriate pronunciation lexicon.
- D. Output speech marks to guide in pronunciation.
Answer: A
Explanation:
Explanation/Reference: https://docs.aws.amazon.com/polly/latest/dg/ssml.html
NEW QUESTION # 41
You are an ML engineer at a global shoe store. You manage the ML models for the company's website. You are asked to build a model that will recommend new products to the user based on their purchase behavior and similarity with other users. What should you do?
- A. Build a regression model using the features as predictors
- B. Build a classification model
- C. Build a knowledge-based filtering model
- D. Build a collaborative-based filtering model
Answer: D
NEW QUESTION # 42
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 GZIP CSV format.
- B. Convert the records to XML format.
- C. Convert the records to Apache Parquet format.
- D. Convert the records to JSON format.
Answer: C
Explanation:
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 # 43
You are designing an architecture with a serveress ML system to enrich customer support tickets with informative metadata before they are routed to a support agent. You need a set of models to predict ticket priority, predict ticket resolution time, and perform sentiment analysis to help agents make strategic decisions when they process support requests. Tickets are not expected to have any domain-specific terms or jargon.
The proposed architecture has the following flow:
Which endpoints should the Enrichment Cloud Functions call?
- A. 1 = Al Platform, 2 = Al Platform, 3 = Cloud Natural Language API
- B. 1 = Al Platform, 2 = Al Platform, 3 = AutoML Vision
- C. 1 = Al Platform, 2 = Al Platform, 3 = AutoML Natural Language
- D. 1 = cloud Natural Language API, 2 = Al Platform, 3 = Cloud Vision API
Answer: C
NEW QUESTION # 44
You recently joined a machine learning team that will soon release a new project. As a lead on the project, you are asked to determine the production readiness of the ML components. The team has already tested features and data, model development, and infrastructure. Which additional readiness check should you recommend to the team?
- A. Ensure that model performance is monitored
- B. Ensure that feature expectations are captured in the schema
- C. Ensure that training is reproducible
- D. Ensure that all hyperparameters are tuned
Answer: C
Explanation:
https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/aad9f93b86b7addfea4c419b9100c6cdd26cacea.pdf
NEW QUESTION # 45
You need to analyze user activity data from your company's mobile applications. Your team will use BigQuery for data analysis, transformation, and experimentation with ML algorithms. You need to ensure real-time ingestion of the user activity data into BigQuery. What should you do?
- A. Run a Dataflow streaming job to ingest the data into BigQuery.
- B. Run an Apache Spark streaming job on Dataproc to ingest the data into BigQuery.
- C. Configure Pub/Sub and a Dataflow streaming job to ingest the data into BigQuery,
- D. Configure Pub/Sub to stream the data into BigQuery.
Answer: D
Explanation:
Pub/Sub is a messaging service that can be used to stream data into BigQuery in real-time. Configuring Pub/Sub to stream the user activity data into BigQuery would ensure real-time ingestion of the data. Source: Google Cloud
NEW QUESTION # 46
Your task is classify if a company logo is present on an image. You found out that 96% of a data does not include a logo. You are dealing with data imbalance problem. Which metric do you use to evaluate to model?
- A. F1 Score
- B. RMSE
- C. F Score with higher recall weighted than precision
- D. F Score with higher precision weighting than recall
Answer: C
NEW QUESTION # 47
You lead a data science team at a large international corporation. Most of the models your team trains are large-scale models using high-level TensorFlow APIs on AI Platform with GPUs. Your team usually takes a few weeks or months to iterate on a new version of a model. You were recently asked to review your team's spending. How should you reduce your Google Cloud compute costs without impacting the model's performance?
- A. Use AI Platform to run distributed training jobs with checkpoints.
- B. Migrate to training with Kuberflow on Google Kubernetes Engine, and use preemptible VMs with checkpoints.
- C. Migrate to training with Kuberflow on Google Kubernetes Engine, and use preemptible VMs without checkpoints.
- D. Use AI Platform to run distributed training jobs without checkpoints.
Answer: C
NEW QUESTION # 48
A Machine Learning Specialist receives customer data for an online shopping website. The data includes demographics, past visits, and locality information. The Specialist must develop a machine learning approach to identify the customer shopping patterns, preferences, and trends to enhance the website for better service and smart recommendations.
Which solution should the Specialist recommend?
- A. Random Cut Forest (RCF) over random subsamples to identify patterns in the customer database.
- B. Collaborative filtering based on user interactions and correlations to identify patterns in the customer database.
- C. Latent Dirichlet Allocation (LDA) for the given collection of discrete data to identify patterns in the customer database.
- D. A neural network with a minimum of three layers and random initial weights to identify patterns in the customer database.
Answer: B
Explanation:
Explanation
NEW QUESTION # 49
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. Reduce the value of the repeat parameter
- B. Use the interleave option for reading data
- C. Set the prefetch option equal to the training batch size
- D. Decrease the batch size argument in your transformation
- E. Increase the buffer size for the shuffle option.
Answer: C,D
Explanation:
https://towardsdatascience.com/overcoming-data-preprocessing-bottlenecks-with-tensorflow-data-service-nvidia-dali-and-other-d6321917f851
NEW QUESTION # 50
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