Professional-Data-Engineer試験復習、Professional-Data-Engineer復習対策Professional-Data-Engineer学習教材自体については、学習者が学習教材をさまざまな角度から効率的に学習できるように複数の機能を強化します。たとえば、試験を刺激する機能は、受験者が実際のProfessional-Data-Engineer試験の雰囲気とペースに精通し、予期しない問題の発生を回避するのに役立ちます。簡単に言えば、当社のProfessional-Data-Engineerトレーニングガイドは品質とサービスを優先し、Googleお客様にProfessional-Data-Engineer試験に合格するための新しい体験と快適な気持ちをお届けします。 Google Certified Professional Data Engineer Exam 認定 Professional-Data-Engineer 試験問題 (Q110-Q115):質問 # 110
You are choosing a NoSQL database to handle telemetry data submitted from millions of Internet-of-Things (IoT) devices. The volume of data is growing at 100 TB per year, and each data entry has about 100 attributes. The data processing pipeline does not require atomicity, consistency, isolation, and durability (ACID). However, high availability and low latency are required.
You need to analyze the data by querying against individual fields. Which three databases meet your requirements? (Choose three.)
A. HBase
B. MySQL
C. Redis
D. Cassandra
E. HDFS with Hive
F. MongoDB
正解：A、E、F
解説：
Explanation:
質問 # 111
Which of the following is NOT one of the three main types of triggers that Dataflow supports?
A. Trigger based on element size in bytes
B. Trigger that is a combination of other triggers
C. Trigger based on time
D. Trigger based on element count
正解：A
解説：
Explanation
There are three major kinds of triggers that Dataflow supports: 1. Time-based triggers 2. Data-driven triggers.
You can set a trigger to emit results from a window when that window has received a certain number of data elements. 3. Composite triggers. These triggers combine multiple time-based or data-driven triggers in some logical way Reference: https://cloud.google.com/dataflow/model/triggers
質問 # 112
Government regulations in your industry mandate that you have to maintain an auditable record of access to certain types of dat A.
Assuming that all expiring logs will be archived correctly, where should you store data that is subject to that mandate?
A. In a BigQuery dataset that is viewable only by authorized personnel, with the Data Access log used to provide the auditability.
B. Encrypted on Cloud Storage with user-supplied encryption keys. A separate decryption key will be given to each authorized user.
C. In a bucket on Cloud Storage that is accessible only by an AppEngine service that collects user information and logs the access before providing a link to the bucket.
D. In Cloud SQL, with separate database user names to each user. The Cloud SQL Admin activity logs will be used to provide the auditability.
正解：A
質問 # 113
Which TensorFlow function can you use to configure a categorical column if you don't know all of the possible values for that column?
A. categorical_column_with_hash_bucket
B. categorical_column_with_vocabulary_list
C. categorical_column_with_unknown_values
D. sparse_column_with_keys
正解：A
解説：
If you know the set of all possible feature values of a column and there are only a few of them, you can use categorical_column_with_vocabulary_list. Each key in the list will get assigned an auto-incremental ID starting from 0.
What if we don't know the set of possible values in advance? Not a problem. We can use categorical_column_with_hash_bucket instead. What will happen is that each possible value in the feature column occupation will be hashed to an integer ID as we encounter them in training.
Reference: https://www.tensorflow.org/tutorials/wide
質問 # 114
What is the recommended action to do in order to switch between SSD and HDD storage for your Google Cloud Bigtable instance?
A. the selection is final and you must resume using the same storage type
B. create a third instance and sync the data from the two storage types via batch jobs
C. run parallel instances where one is HDD and the other is SDD
D. export the data from the existing instance and import the data into a new instance
正解：D
解説：
When you create a Cloud Bigtable instance and cluster, your choice of SSD or HDD storage for the cluster is permanent. You cannot use the Google Cloud Platform Console to change the type of storage that is used for the cluster.
If you need to convert an existing HDD cluster to SSD, or vice-versa, you can export the data from the existing instance and import the data into a new instance. Alternatively, you can write
a Cloud Dataflow or Hadoop MapReduce job that copies the data from one instance to another.