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Here the candidates need to demonstrate their expertise in using pre-built Machine Learning models as a service, including Machine Learning APIs (for instance, Speech API, Vision API, etc.), customizing Machine Learning APIs (for instance, Auto ML text, AutoML Vision, etc.), conversational experiences (for instance, Dialogflow). The applicants should also have the skills in deploying the Machine Learning pipeline. This involves the ability to ingest relevant data, perform retraining of machine learning models (BigQuery ML, Cloud Machine Learning Engine, Spark ML, Kubeflow), as well as execute continuous evaluation. Additionally, the students should be able to choose the relevant training & serving infrastructure as well as know how to fulfill measuring, monitoring, and troubleshooting of Machine Learning models.
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Google Professional-Data-Engineer Certification Exam is intended for data engineers, data scientists, and other IT professionals who want to demonstrate their proficiency in Google Cloud Platform data engineering technologies. Professional-Data-Engineer Exam covers topics such as design and implementation of data processing systems, building and maintaining databases, data analysis and visualization, and machine learning implementation.
NEW QUESTION # 70
You migrated your on-premises Apache Hadoop Distributed File System (HDFS) data lake to Cloud Storage.
The data scientist team needs to process the data by using Apache Spark and SQL. Security policies need to be enforced at the column level. You need a cost-effective solution that can scale into a data mesh. What should you do?
Answer: B
Explanation:
The key requirements are:
Data on Cloud Storage (migrated from HDFS).
Processing with Spark and SQL.
Column-level security.
Cost-effective and scalable for a data mesh.
Let's analyze the options:
Option A (Load to BigQuery tables, policy tags, Spark-BQ connector/BQ SQL):
Pros: BigQuery native tables offer excellent performance. Policy tags provide robust column-level security managed centrally in Data Catalog. The Spark-BigQuery connector allows Spark to read from/write to BigQuery. BigQuery SQL is powerful. Scales well.
Cons: "Loading" the data into BigQuery means moving it from Cloud Storage into BigQuery's managed storage. This incurs storage costs in BigQuery and an ETL step. While effective, it might not be the most
"cost-effective" if the goal is to query data in place on Cloud Storage, especially for very large datasets.
Option B (Long-living Dataproc, Hive, Ranger):
Pros: Provides a Hadoop-like environment with Spark, Hive, and Ranger for column-level security.
Cons: "Long-living Dataproc cluster" is generally not the most cost-effective, as you pay for the cluster even when idle. Managing Hive and Ranger adds operational overhead. While scalable, it requires more infrastructure management than serverless options.
Option C (IAM at file level, BQ external table, Dataproc Spark):
Pros: Using Cloud Storage is cost-effective for storage. BigQuery external tables allow SQL access.
Cons: IAM at the file level in Cloud Storage does not provide column-level security. This option fails to meet a critical requirement.
Option D (Define a BigLake table, policy tags, Spark-BQ connector/BQ SQL):
Pros:BigLake Tables: These tables allow you to query data in open formats (like Parquet, ORC) on Cloud Storage as if it were a native BigQuery table, but without ingesting the data into BigQuery's managed storage.
This is highly cost-effective for storage.
Column-Level Security with Policy Tags: BigLake tables integrate with Data Catalog policy tags to enforce fine-grained column-level security on the data residing in Cloud Storage. This is a centralized and robust security model.
Spark and SQL Access: Data scientists can use BigQuery SQL directly on BigLake tables. The Spark- BigQuery connector can also be used to access BigLake tables, enabling Spark processing.
Cost-Effective & Scalable Data Mesh: This approach leverages the cost-effectiveness of Cloud Storage, the serverless querying power and security features of BigQuery/Data Catalog, and provides a clear path to building a data mesh by allowing different domains to manage their data in Cloud Storage while exposing it securely through BigLake.
Cons: Performance for BigLake tables might be slightly different than BigQuery native storage for some workloads, but it's designed for high performance on open formats.
Why D is superior for this scenario:
BigLake tables (Option D) directly address the need to keep data in Cloud Storage (cost-effective for a data lake) while providing strong, centrally managed column-level security via policy tags and enabling both SQL (BigQuery) and Spark (via Spark-BigQuery connector) access. This is more aligned with modern data lakehouse and data mesh architectures than loading everything into native BigQuery storage (Option A) if the data is already in open formats on Cloud Storage, or managing a full Hadoop stack on Dataproc (Option B).
Reference:
Google Cloud Documentation: BigLake > Overview. "BigLake lets you unify your data warehouses and data lakes. BigLake tables provide fine-grained access control for tables based on data in Cloud Storage, while preserving access through other Google Cloud services like BigQuery, GoogleSQL, Spark, Trino, and TensorFlow." Google Cloud Documentation: BigLake > Introduction to BigLake tables. "BigLake tables bring BigQuery features to your data in Cloud Storage. You can query external data with fine-grained security (including row- level and column-level security) without needing to move or duplicate data." Google Cloud Documentation: Data Catalog > Overview of policy tags. "You can use policy tags to enforce column-level access control for BigQuery tables, including BigLake tables." Google Cloud Blog: "Announcing BigLake - Unifying data lakes and warehouses" (and similar articles) highlight how BigLake enables querying data in place on Cloud Storage with BigQuery's governance features.
NEW QUESTION # 71
After migrating ETL jobs to run on BigQuery, you need to verify that the output of the migrated jobs is the same as the output of the original. You've loaded a table containing the output of the original job and want to compare the contents with output from the migrated job to show that they are identical. The tables do not contain a primary key column that would enable you to join them together for comparison. What should you do?
Answer: D
NEW QUESTION # 72
You are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?
Answer: B
Explanation:
As time is less, use cloud NLP and entity is used to label general subjects, sentiment label for sentiment analysis.
NEW QUESTION # 73
You orchestrate ETL pipelines by using Cloud Composer One of the tasks in the Apache Airflow directed acyclic graph (DAG) relies on a third-party service. You want to be notified when the task does not succeed. What should you do?
Answer: B
Explanation:
By assigning a function with notification logic to the on_failure_callback parameter, you can customize the action that is taken when a task fails in your DAG1. For example, you can send an email, a Slack message, or a PagerDuty alert to notify yourself or your team about the task failure2. This option is more flexible and reliable than configuring a Cloud Monitoring alert on the sla_missed metric, which only triggers when a task misses its scheduled deadline3. The sla_miss_callback parameter is also related to the sla_missed metric, and it is executed when the task instance has not succeeded and the time is past the task's scheduled execution date plus its sla4. The on_retry_callback parameter is executed before a task is retried4. These options are not suitable for notifying when a task does not succeed, as they depend on the task's schedule and retry settings, which may not reflect the actual task completion status. Reference:
1: Callbacks | Cloud Composer | Google Cloud
2: How to Send an Email on Task Failure in Airflow - Astronomer
3: Monitoring SLA misses | Cloud Composer | Google Cloud
4: BaseOperator | Apache Airflow Documentation
NEW QUESTION # 74
You are troubleshooting your Dataflow pipeline that processes data from Cloud Storage to BigQuery. You have discovered that the Dataflow worker nodes cannot communicate with one another Your networking team relies on Google Cloud network tags to define firewall rules You need to identify the issue while following Google-recommended networking security practices. What should you do?
Answer: B
Explanation:
Dataflow worker nodes need to communicate with each other and with the Dataflow service on TCP ports 12345 and 12346. These ports are used for data shuffling and streaming engine communication. By default, Dataflow assigns a network tag called dataflow to the worker nodes, and creates a firewall rule that allows traffic on these ports for the dataflow network tag. However, if you use a custom network tag for your Dataflow pipeline, you need to create a firewall rule that allows traffic on these ports for your custom network tag. Otherwise, the worker nodes will not be able to communicate with each other and the Dataflow service, and the pipeline will fail.
Therefore, the best way to identify the issue is to determine whether there is a firewall rule set to allow traffic on TCP ports 12345 and 12346 for the Dataflow network tag. If there is no such firewall rule, or if the firewall rule does not match the network tag used by your Dataflow pipeline, you need to create or update the firewall rule accordingly.
Option A is not a good solution, as determining whether your Dataflow pipeline has a custom network tag set does not tell you whether there is a firewall rule that allows traffic on the required ports for that network tag. You need to check the firewall rule as well.
Option C is not a good solution, as determining whether your Dataflow pipeline is deployed with the external IP address option enabled does not tell you whether there is a firewall rule that allows traffic on the required ports for the Dataflow network tag. The external IP address option determines whether the worker nodes can access resources on the public internet, but it does not affect the internal communication between the worker nodes and the Dataflow service.
Option D is not a good solution, as determining whether there is a firewall rule set to allow traffic on TCP ports 12345 and 12346 on the subnet used by Dataflow workers does not tell you whether the firewall rule applies to the Dataflow network tag. The firewall rule should be based on the network tag, not the subnet, as the network tag is more specific and secure. Reference: Dataflow network tags | Cloud Dataflow | Google Cloud, Dataflow firewall rules | Cloud Dataflow | Google Cloud, Dataflow network configuration | Cloud Dataflow | Google Cloud, Dataflow Streaming Engine | Cloud Dataflow | Google Cloud.
NEW QUESTION # 75
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