VALID BRAINDUMPS PROFESSIONAL-MACHINE-LEARNING-ENGINEER EBOOK PASS CERTIFY| RELIABLE PROFESSIONAL-MACHINE-LEARNING-ENGINEER LATEST VERSION: GOOGLE PROFESSIONAL MACHINE LEARNING ENGINEER

Valid Braindumps Professional-Machine-Learning-Engineer Ebook Pass Certify| Reliable Professional-Machine-Learning-Engineer Latest Version: Google Professional Machine Learning Engineer

Valid Braindumps Professional-Machine-Learning-Engineer Ebook Pass Certify| Reliable Professional-Machine-Learning-Engineer Latest Version: Google Professional Machine Learning Engineer

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Achieving the Google Professional Machine Learning Engineer Certification is a significant accomplishment for professionals in the field of machine learning. It demonstrates a high level of expertise in designing, implementing, and deploying machine learning models using Google Cloud Platform. Google Professional Machine Learning Engineer certification also provides opportunities for career advancement and recognition as a leader in the field of machine learning.

To be eligible for the exam, candidates should have experience in machine learning, including designing and implementing machine learning models, as well as experience with cloud-based machine learning services. Candidates should also have experience with data engineering, data analysis, and software engineering. Professional-Machine-Learning-Engineer Exam is intended for individuals who have at least three years of experience in the field, and who are able to demonstrate their knowledge through a combination of multiple choice and practical exam questions.

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Google Professional-Machine-Learning-Engineer Certification Exam is intended for machine learning engineers, data scientists, and software developers who want to demonstrate their expertise in building and deploying machine learning models on Google Cloud Platform. Professional-Machine-Learning-Engineer exam covers a wide range of topics, including data preparation and analysis, feature engineering, model selection and training, model evaluation and optimization, and deploying and managing machine learning models on Google Cloud Platform.

Google Professional Machine Learning Engineer Sample Questions (Q276-Q281):

NEW QUESTION # 276
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 Apache Parquet format.
  • C. Convert the records to JSON format.
  • D. Convert the records to GZIP CSV format.

Answer: B

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 # 277
You work for a delivery company. You need to design a system that stores and manages features such as parcels delivered and truck locations over time. The system must retrieve the features with low latency and feed those features into a model for online prediction. The data science team will retrieve historical data at a specific point in time for model training. You want to store the features with minimal effort. What should you do?

  • A. Store features in BigQuery timestamp partitioned tables, and use the BigQuery Storage Read API to serve the features.
  • B. Store features in Vertex Al Feature Store.
  • C. Store features as a Vertex Al dataset and use those features to tram the models hosted in Vertex Al endpoints.
  • D. Store features in Bigtable as key/value data.

Answer: B

Explanation:
Vertex AI Feature Store is a service that allows you to store and manage your ML features on Google Cloud.
You can use Vertex AI Feature Store to store features such as parcels delivered and truck locations over time, and retrieve them with low latency for online prediction. Online prediction is a type of prediction that provides low-latency responses to individual or small batches of input data. You can also use Vertex AI Feature Store to retrieve historical data at a specific point in time for model training. Model training is a process of learning the parameters of a ML model from data. By using Vertex AI Feature Store, you can store the features with minimal effort, and avoid the complexity of managing your own data storage and serving system. References:
* Vertex AI Feature Store documentation
* Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate


NEW QUESTION # 278
Your company manages an application that aggregates news articles from many different online sources and sends them to users. You need to build a recommendationmodel that will suggest articles to readers that are similar to the articles they are currently reading. Which approach should you use?

  • A. Create a collaborative filtering system that recommends articles to a user based on the user's past behavior.
  • B. Encode all articles into vectors using word2vec, and build a model that returns articles based on vector similarity.
  • C. Manually label a few hundred articles, and then train an SVM classifier based on the manually classified articles that categorizes additional articles into their respective categories.
  • D. Build a logistic regression model for each user that predicts whether an article should be recommended to a user.

Answer: B

Explanation:
* Option A is incorrect because creating a collaborative filtering system that recommends articles to a user based on the user's past behavior is not the best approach to suggest articles that are similar to the articles they are currently reading. Collaborative filtering is a method of recommendation that uses the ratings or preferences of other users to predict the preferences of a target user1. However, this method does not consider the content or features of the articles, and may not be able to find articles that are similar in terms of topic, style, or sentiment.
* Option B is correct because encoding all articles into vectors using word2vec, and building a model that returns articles based on vector similarity is a suitable approach to suggest articles that are similar to the articles they are currently reading. Word2vec is a technique that learns low-dimensional and dense representations of words from a large corpus of text, such that words that are semantically similar have similar vectors2. By applying word2vec to the articles, we can obtain vector representations of the articles that capture their meaning and usage. Then, we can use a similarity measure, such as cosine similarity, to find articles that have similar vectors to the current article3.
* Option C is incorrect because building a logistic regression model for each user that predicts whether an article should be recommended to a user is not a feasible approach to suggest articles that are similar to the articles they are currently reading. Logistic regression is a supervised learning method that models the probability of a binary outcome (such as recommend or not) based on some input features (such as user profile or article content)4. However, this method requires a large amount of labeled data for each user, which may not be available or scalable. Moreover, this method does not directly measure the similarity between articles, but rather the likelihood of a user's preference.
* Option D is incorrect because manually labeling a few hundred articles, and then training an SVM classifier based on the manually classified articles that categorizes additional articles into their respective categories is not an effective approach to suggest articles that are similar to the articles they are currently reading. SVM (support vector machine) is a supervised learning method that finds a hyperplane that separates the data into different classes (suchas news categories) with the maximum margin5. However, this method also requires a large amount of labeled data, which may be costly and time-consuming to obtain. Moreover, this method does not account for the fine-grained similarity between articles within the same category, or the cross-category similarity between articles from different categories.
References:
* Collaborative filtering
* Word2vec
* Cosine similarity
* Logistic regression
* SVM


NEW QUESTION # 279
You are training a TensorFlow model on a structured data set with 100 billion records stored in several CSV files. You need to improve the input/output execution performance. What should you do?

  • A. Convert the CSV files into shards of TFRecords, and store the data in the Hadoop Distributed File System (HDFS)
  • B. Convert the CSV files into shards of TFRecords, and store the data in Cloud Storage
  • C. Load the data into Cloud Bigtable, and read the data from Bigtable
  • D. Load the data into BigQuery and read the data from BigQuery.

Answer: B

Explanation:
The input/output execution performance of a TensorFlow model depends on how efficiently the model can read and process the data from the data source. Reading and processing data from CSV files can be slow and inefficient, especially if the data is large and distributed. Therefore, to improve the input/output execution performance, one should use a more suitable data format and storage system.
One of the best options for improving the input/output execution performance is to convert the CSV files into shards of TFRecords, and store the data in Cloud Storage. TFRecord is a binary data format that can store a sequence of serialized TensorFlow examples. TFRecord has several advantages over CSV, such as:
* Faster data loading: TFRecord can be read and processed faster than CSV, as it avoids the overhead of parsing and decoding the text data. TFRecord also supports compression and checksums, which can reduce the data size and ensure data integrity1
* Better performance: TFRecord can improve the performance of the model, as it allows the model to access the data in a sequential and streaming manner, and leverage the tf.data API to build efficient data pipelines. TFRecord also supports sharding and interleaving, which can increase the parallelism and throughput of the data processing2
* Easier integration: TFRecord can integrate seamlessly with TensorFlow, as it is the native data format for TensorFlow. TFRecord also supports various types of data, such as images, text, audio, and video, and can store the data schema and metadata along with the data3 Cloud Storage is a scalable and reliable object storage service that can store any amount of data. Cloud Storage has several advantages over other storage systems, such as:
* High availability: Cloud Storage can provide high availability and durability for the data, as it replicates the data across multiple regions and zones, and supports versioning and lifecycle management. Cloud Storage also offers various storage classes, such as Standard, Nearline, Coldline, and Archive, to meet different performance and cost requirements4
* Low latency: Cloud Storage can provide low latency and high bandwidth for the data, as it supports HTTP and HTTPS protocols, and integrates with other Google Cloud services, such as AI Platform, Dataflow, and BigQuery. Cloud Storage also supports resumable uploads and downloads, and parallel composite uploads, which can improve the data transfer speed and reliability5
* Easy access: Cloud Storage can provide easy access and management for the data, as it supports various tools and libraries, such as gsutil, Cloud Console, and Cloud Storage Client Libraries. Cloud Storage
* also supports fine-grained access control and encryption, which can ensure the data security and privacy.
The other options are not as effective or feasible. Loading the data into BigQuery and reading the data from BigQuery is not recommended, as BigQuery is mainly designed for analytical queries on large-scale data, and does not support streaming or real-time data processing. Loading the data into Cloud Bigtable and reading the data from Bigtable is not ideal, as Cloud Bigtable is mainly designed for low-latency and high-throughput key-value operations on sparse and wide tables, and does not support complex data types or schemas.
Converting the CSV files into shards of TFRecords and storing the data in the Hadoop Distributed File System (HDFS) is not optimal, as HDFS is not natively supported by TensorFlow, and requires additional configuration and dependencies, such as Hadoop, Spark, or Beam.
References: 1: TFRecord and tf.Example 2: Better performance with the tf.data API 3: TensorFlow Data Validation 4: Cloud Storage overview 5: Performance : [How-to guides]


NEW QUESTION # 280
You are training and deploying updated versions of a regression model with tabular data by using Vertex Al Pipelines. Vertex Al Training Vertex Al Experiments and Vertex Al Endpoints. The model is deployed in a Vertex Al endpoint and your users call the model by using the Vertex Al endpoint. You want to receive an email when the feature data distribution changes significantly, so you can retrigger the training pipeline and deploy an updated version of your model What should you do?

  • A. In Cloud Monitoring create a logs-based metric and a threshold alert for the metric. Configure Cloud Monitoring to send an email when the alert is triggered.
  • B. In Cloud Logging, create a logs-based alert using the logs in the Vertex Al endpoint. Configure Cloud Logging to send an email when the alert is triggered.
  • C. Export the container logs of the endpoint to BigQuery Create a Cloud Function to run a SQL query over the exported logs and send an email. Use Cloud Scheduler to trigger the Cloud Function.
  • D. Use Vertex Al Model Monitoring Enable prediction drift monitoring on the endpoint. and specify a notification email.

Answer: D


NEW QUESTION # 281
......

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